Robotic systems and imagers and table coordinate system alignment

Description

ROBOTIC SYSTEMS AND IMAGERS AND TABLE COORDINATE SYSTEMALIGNMENTFIELD

[0001] The present technology generally relates to surgical navigation, and relates more particularly to surgical navigation using shared data between surgical systems.BACKGROUND

[0002] Surgical robots may assist a surgeon or other medical provider in carrying out a surgical procedure, or may complete one or more surgical procedures autonomously. Additionally, one or more navigation systems may be used to track the location of various reference markers attached to surgical tools, anatomical elements, and / or other objects during a surgery. Imaging may be used by a medical provider for diagnostic and / or therapeutic purposes or to detect components in a surgical setting.

[0003] Prior to beginning a given surgical procedure, a registration process may be completed to correlate two or more coordinate spaces with each other, including, for example, coordinate spaces of one or more robots, navigation systems, and / or imaging devices. A robotic arm may move within the confines of a surgical setting. As surgical procedures incorporate more robotic devices in the surgical setting, spatial location as well as path calculations for the movement of the robotic devices become a major concern due to potential collisions occurring between the various robotic devices.SUMMARY

[0004] Example aspects of the present disclosure include:

[0005] A system according to at least one embodiment of the present disclosure includes a robotic arm mounted to a patient bed at a known location in a surgical environment, an imaging device disposed proximate to the robotic arm and the patient bed in the surgical environment, one or more processors and a memory storing data thereon that, when processed by the one or more processors, causes the one or more processors to receive first information about a pose of the robotic arm, receive second information about the patient bed relative to the robotic arm, determine, based on the first information and the second information, a pose of the imaging device relative to the robotic arm or relative to the patient bed and generate, based on thedetermined pose of the imaging device relative to the robotic arm or relative to the patient bed, a navigation path for the imaging device.

[0006] Any of the aspects herein, wherein the robotic arm includes one or more sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

[0007] Any of the aspects herein, wherein the data further causes the one or more processors to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

[0008] Any of the aspects herein, wherein the three-dimensional volume collision avoidance mapping includes at least one sub-volume to be avoided by the robotic arm.

[0009] Any of the aspects herein, wherein the at least one sub-volume to be avoided by the robotic arm is determined based on a predicted motion of a patient or a surgeon.

[0010] Any of the aspects herein, wherein the at least one sub-volume to be avoided by the robotic arm includes a high probability zone that is defined based on a predicted motion of a patient or a surgeon.

[0011] Any of the aspects herein, wherein the data further causes the one or more processors are caused to receive third information about a pose of the imaging device, receive fourth information about the imaging device relative to the patient bed, determine, based on the third information and the fourth information, a pose of the robotic arm relative to the imaging device or relative to the patient bed and generate, based on the determined pose of the robotic arm relative to the imaging device or relative to the patient bed, a three-dimensional navigation path for the robotic arm.

[0012] A system according to at least one embodiment of the present disclosure includes one or more processors and a memory storing data thereon that, when processed by the one or more processors, causes the one or more processors to receive first information about a patient bed, receive second information about a pose of a robotic arm mounted to the patient bed at an unknown location, receive third information about the patient bed relative to the robotic arm, determine, based on the first information, the second information and the third information, a pose of an imaging device relative to the robotic arm or relative to the patient bed and generate, based on the determined pose of the imaging device relative to the robotic arm or relative to the patient bed, a navigation path for the imaging device.

[0013] Any of the aspects herein, wherein the first information is registered information about the patient bed.

[0014] Any of the aspects herein, wherein the first information is received from sensor information about the patient bed.

[0015] Any of the aspects herein, wherein the robotic arm includes sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

[0016] Any of the aspects herein, wherein the one or more processors are caused to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

[0017] Any of the aspects herein, wherein the three-dimensional volume collision avoidance mapping includes at least one sub-volume to be avoided by the robotic arm.

[0018] Any of the aspects herein, wherein the at least one sub-volume to be avoided by the robotic arm is determined based on a predicted motion of a patient or a surgeon.

[0019] Any of the aspects herein, wherein the at least one sub-volume to be avoided by the robotic arm includes a high probability zone that is defined based on a predicted motion of a patient or a surgeon.

[0020] Any of the aspects herein, wherein the data further causes the one or more processors to receive fourth information about a pose of the imaging device, receive fifth information about a pose of the imaging device relative to the patient bed, determine, based on the fourth information and the fifth information, a pose of the robotic arm relative to the imaging device or relative to the patient bed and generate, based on the determined pose of the robotic arm relative to the imaging device or relative to the patient bed, a three-dimensional navigation path for the robotic arm.

[0021] A system according to at least one embodiment of the present disclosure includes a robotic arm in proximity to a patient bed in a surgical environment, an imaging device disposed proximate to the robotic arm and the patient bed in the surgical environment, one or more processors and a memory storing data thereon that, when processed by the one or more processors, causes the one or more processors to generate a mapping of a working volume of the surgical environment, align coordinates determined for the imaging device from the three- dimensional mapping of the working volume of the surgical environment with coordinates for the robotic arm or coordinates for the patient bed and generate, based on the aligned coordinates forthe imaging device with the coordinates for the robotic arm or the patient bed, a three- dimensional navigation path for the imaging device.

[0022] Any of the aspects herein, wherein the data further enables the one or more processors to register one or more portions of the object to the first robotic arm

[0023] Any of the aspects herein, wherein the three-dimensional mapping is generated based on sensor input received from at least one of an imaging sensor and a depth sensor.

[0024] Any of the aspects herein, wherein the robotic arm includes sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

[0025] Any of the aspects herein, wherein the one or more processors are caused to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

[0026] Any aspect in combination with any one or more other aspects.

[0027] Any one or more of the features disclosed herein.

[0028] Any one or more of the features as substantially disclosed herein.

[0029] Any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.

[0030] Any one of the aspects / features / embodiments in combination with any one or more other aspects / features / embodiments .

[0031] Use of any one or more of the aspects or features as disclosed herein.

[0032] It is to be appreciated that any feature described herein can be claimed in combination with any other feature(s) as described herein, regardless of whether the features come from the same described embodiment.

[0033] The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims.

[0034] The phrases “at least one”, “one or more”, and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. When each one of A, B, and C in the aboveexpressions refers to an element, such as X, Y, and Z, or class of elements, such as Xi-Xn, Yi-Ym, and Zi-Zo, the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., Xi and X2) as well as a combination of elements selected from two or more classes (e.g., Yi and Zo).

[0035] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably.

[0036] The preceding is a simplified summary of the disclosure to provide an understanding of some aspects of the disclosure. This summary is neither an extensive nor exhaustive overview of the disclosure and its various aspects, embodiments, and configurations. It is intended neither to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure but to present selected concepts of the disclosure in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other aspects, embodiments, and configurations of the disclosure are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.

[0037] Numerous additional features and advantages of the present invention will become apparent to those skilled in the art upon consideration of the embodiment descriptions provided hereinbelow.BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are incorporated into and form a part of the specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the disclosure. The drawings simply illustrate preferred and alternative examples of how the disclosure can be made and used and are not to be construed as limiting the disclosure to only the illustrated and described examples. Further features and advantages will become apparent from the following, more detailed, description of the various aspects, embodiments, and configurations of the disclosure, as illustrated by the drawings referenced below.

[0039] Fig. 1 is a block diagram of a system in accordance with at least one embodiment of the present disclosure;

[0040] Fig. 2A is a diagram illustrating movement of a surgical system through a navigation volume in accordance with an example embodiment of the present disclosure;

[0041] Fig. 2B is a diagram illustrating movement of a surgical system through a working volume in accordance with another example embodiment of the present disclosure;

[0042] Fig. 2C is a diagram illustrating movement of a surgical system through a navigation volume in accordance with yet another example embodiment of the present disclosure;

[0043] Fig. 3A is a diagram illustrating movement of a surgical system through a navigation volume in accordance with the example embodiment of the present disclosure;

[0044] Fig. 3B is a diagram illustrating movement of a surgical system through a working volume in accordance with the another example embodiment of the present disclosure;

[0045] Fig. 3C is a diagram illustrating movement of a surgical system through a navigation volume in accordance with the yet another example embodiment of the present disclosure;

[0046] Fig. 4 is a flowchart of a method according to the example embodiment of the present disclosure;

[0047] Fig. 5 is a flowchart of a method according to the another example embodiment of the present disclosure; and

[0048] Fig. 6 is a flowchart of a method according to the yet another example embodiment of the present disclosure.DETAILED DESCRIPTION

[0049] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example or embodiment, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, and / or may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the disclosed techniques according to different embodiments of the present disclosure). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a computing device and / or a medical device.

[0050] In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Alternatively or additionally,functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions). Computer- readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0051] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple Al l, A12, A12X, A12Z, or A13 Bionic processors; or any other general purpose microprocessors), graphics processing units (e.g., Nvidia GeForce RTX 2000- series processors, Nvidia GeForce RTX 3000-series processors, AMD Radeon RX 5000-series processors, AMD Radeon RX 6000-series processors, or any other graphics processing units), application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0052] Before any embodiments of the disclosure are explained in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Further, the present disclosure may use examples to illustrate one or more aspects thereof. Unless explicitly stated otherwise, the use or listing of one or more examples (which may be denoted by “for example,” “by way of example,” “e.g.,” “such as,” or similar language) is not intended to and does not limit the scope of the present disclosure.

[0053] The terms proximal and distal are used in this disclosure with their conventional medical meanings, proximal being closer to the operator or user of the system, and further from the region of surgical interest in or on the patient, and distal being closer to the region of surgical interest in or on the patient, and further from the operator or user of the system.

[0054] During robotic surgery or surgical procedures, a robotic system that includes one or more robotic arms, a robotic imaging device, a surgeon, etc., may share a working environment around a patient. The one or more robotic arms may have a proximity sensor or a force gauge that may stop or move the one or more robotic arms (1) when the one or more robotic arms is about to collide with an object, or (2) when the one or more robotic arms is close to an object. The object may include, for example, the robotic imaging device or the surgeon mentioned above.Similarly, a depth camera, which may be part of a navigation system, may determine that the one or more robotic arms is contacting or close to another object and may cause the one or more robotic arms to stop to prevent collision.

[0055] According to at least one embodiment of the present disclosure, a system builds navigation paths for surgical systems to avoid collisions between the surgical systems and other objects within a working environment based on shared data between the surgical systems. The surgical systems may at least include a robotic system and / or a robotic imaging device. The navigation paths may be dynamically altered and react to surgical system or user movements to avoid collision. In some embodiments of the present disclosure, the system may include a depth camera or other imaging device (e.g., part of the navigation system) capable of tracking the position of the surgical systems and other objects such as the user in real-time or near real-time. The system may be configured to create three dimensional (3D) maps of the working environment, and may further divide the working environment into sections that are determined based on the likeliness of a surgical system or user movement relative to the section (e.g., the likeliness the surgical system or user enters the section, the likeliness the surgical system or user leaves the section, the likeliness the surgical system or user moves within the section, etc.).

[0056] In some embodiments of the present disclosure, the 3D maps may take into account biomechanical considerations, such as defining a distance from a non-moving surgical system, object or user as being safe (e.g., a distance outside of an arm’s length away from the nonmoving surgical system components, object or user in any direction may be defined as safe for the surgical system to move through). In some embodiments of the present disclosure, the systemmay construct or build kinetic models of the surgical system or user or multiple surgical systems or users (e.g., the camera may track nodes associated with joints of the surgical system or user or multiple surgical systems or users to determine general movements of the surgical system or user or multiple surgical systems or users) to predict the movement (e.g., a user arm movement, a robotic arm movement, a robotic imaging device movement, a movement from a first section of the working environment to a second different section of the working environment, etc.) of the surgical system or user or multiple surgical system or users. In at least one embodiment of the present disclosure, the system may use shared data between the surgical systems and / or the predictions of surgical system or user movements to build the navigation paths for the surgical system (or, more generally, other moving objects within the working environment) that avoids high traffic areas (e.g., areas where multiple surgical systems, objects or users are present, areas in which there is a strong likelihood that the user’s body part(s) or the surgical system’s mechanical part(s) will be positioned, etc.) and / or that changes the movement speed of the surgical system. Additionally or alternatively, the navigation paths may be adjusted based on surgical system or user movement and / or predicted surgical system or user movement in real time to avoid collisions.

[0057] Embodiments of the present disclosure provide technical solutions to one or more of the problems of (1) underutilizing data from the various surgical systems, and (2) collisions during surgery or surgical procedures.

[0058] Turning first to Fig. 1, a block diagram of aspects of a system 100 according to at least one embodiment of the present disclosure is shown. The system 100 may be used to control and navigate at least one surgical system based on shared data among various surgical systems; map areas of a navigation volume and control the at least one surgical system based on the mapped areas; and / or carry out one or more other aspects of one or more of the methods disclosed herein. The system 100 comprises a computing device 102, one or more imaging devices 112, a robotic system 114, a navigation system 118, a database 130, and / or a cloud or other network 134. Systems according to other embodiments of the present disclosure may comprise more or fewer components than the system 100. For example, the system 100 may not include the imaging device 112, the robotic system 114, one or more components of the computing device 102, the database 130, and / or the cloud 134.

[0059] The computing device 102 comprises a processor 104, a memory 106, a communication interface 108, and a user interface 110. Computing devices according to other embodiments of the present disclosure may comprise more or fewer components than the computing device 102.

[0060] The processor 104 of the computing device 102 may be any processor described herein or any similar processor. The processor 104 may be configured to execute instructions stored in the memory 106, which instructions may cause the processor 104 to carry out one or more computing steps utilizing or based on data received from the imaging device 112, the robotic system 114, the navigation system 118, the database 130, and / or the cloud 134.

[0061] The memory 106 may be or comprise RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible, non-transitory memory for storing computer-readable data and / or instructions. The memory 106 may store information or data useful for completing, for example, any step of the method 400 described herein, or of any other method. The memory 106 may store, for example, one or more image processing algorithms 120, one or more prediction algorithms 122, one or more navigation algorithms 124, and / or one or more registration algorithms 128. Additionally or alternatively, one or more of the algorithms discussed herein (e.g., prediction algorithms 122) may be provided as a prediction model (i.e., a model trained on surgical data that receives real-time or near real-time inputs and outputs movement predictions based thereon). The prediction model may be, for example, artificial intelligence models, Machine Learning models, Convolutional Neural Network (CNN) models, combinations thereof, and / or the like. The prediction model may be trained on surgical data related to one or more parameters (e.g., type of surgery, handedness of the surgeon, type of surgeon, type of surgical procedure, time of day, patient position, information associated with the patient, surgical room, surgical team, duration of surgery, etc.) that may be relevant to the surgery or surgical procedure in which the prediction model is used. For instance, the prediction model may be trained on data related to previous surgeries of a first surgeon, and the prediction model may be implemented during a surgery or surgical procedure conducted by the first surgeon.

[0062] In some embodiments, the prediction model may return a confidence score along with the prediction. The confidence score may be or comprise a quantitative indicator (e.g., a value, a percent, etc.) that reflects the overall level of certainty of the predicted movement. In some embodiments, the system 100 may implement one or more prediction models trained on differentdata sets and may use the prediction model yielding the highest confidence score in predicting the movement of the user.

[0063] Such instructions or algorithms may, in some embodiments, be organized into one or more applications, modules, packages, layers, or engines. Alternatively or additionally, the memory 106 may store other types of data (e.g., machine learning models, artificial neural networks, etc.) that can be processed by the processor 104 to carry out the various methods (e.g., a method 400) and features described herein. Thus, although various components of memory 106 are described as instructions, it should be appreciated that functionality described herein can be achieved through use of instructions, algorithms, and / or machine learning models. The data, algorithms, and / or instructions may cause the processor 104 to manipulate data stored in the memory 106 and / or received from or via the imaging device 112, the robotic system 114, the database 130, and / or the cloud 134.

[0064] The computing device 102 may also comprise a communication interface 108. The communication interface 108 may be used for receiving image data or other information from an external source (such as the imaging device 112, the robotic system 114, the navigation system 118, the database 130, the cloud 134, and / or any other system or component not part of the system 100), and / or for transmitting instructions, images, or other information to an external system or device (e.g., another computing device 102, the imaging device 112, the robotic system 114, the navigation system 118, the database 130, the cloud 134, and / or any other system or component not part of the system 100). The communication interface 108 may comprise one or more wired interfaces (e.g., a USB port, an Ethernet port, a Firewire port) and / or one or more wireless transceivers or interfaces (configured, for example, to transmit and / or receive information via one or more wireless communication protocols such as 802.11a / b / g / n, Bluetooth, NFC, ZigBee, and so forth). In some embodiments, the communication interface 108 may be useful for enabling the device 102 to communicate with one or more other processors 104 or computing devices 102, whether to reduce the time needed to accomplish a computing-intensive task or for any other reason.

[0065] The computing device 102 may also comprise one or more user interfaces 110. The user interface 110 may be or comprise a keyboard, mouse, trackball, monitor, television, screen, touchscreen, and / or any other device for receiving information from a user and / or for providing information to a user. The user interface 110 may be used, for example, to receive a userselection or other user input regarding any step of any method described herein. Notwithstanding the foregoing, any required input for any step of any method described herein may be generated automatically by the system 100 (e.g., by the processor 104 or another component of the system 100) or received by the system 100 from a source external to the system 100. In some embodiments, the user interface 110 may be useful to allow a surgeon or other user to modify instructions to be executed by the processor 104 according to one or more embodiments of the present disclosure, and / or to modify or adjust a setting of other information displayed on the user interface 110 or corresponding thereto.

[0066] Although the user interface 110 is shown as part of the computing device 102, in some embodiments, the computing device 102 may utilize a user interface 110 that is housed separately from one or more remaining components of the computing device 102. In some embodiments, the user interface 110 may be located proximate one or more other components of the computing device 102, while in other embodiments, the user interface 110 may be located remotely from one or more other components of the computer device 102.

[0067] The imaging device 112 may be operable to image anatomical feature(s) (e.g., a bone, veins, tissue, etc.) and / or other aspects of patient anatomy to yield image data (e.g., image data depicting or corresponding to a bone, veins, tissue, etc.). “Image data” as used herein refers to the data generated or captured by an imaging device 112, including in a machine-readable form, a graphical / visual form, and in any other form. In various examples, the image data may comprise data corresponding to an anatomical feature of a patient, or to a portion thereof. The image data may be or comprise a preoperative image, an intraoperative image, a postoperative image, or an image taken independently of any surgical procedure. In some embodiments, a first imaging device 112 may be used to obtain first image data (e.g., a first image) at a first time, and a second imaging device 112 may be used to obtain second image data (e.g., a second image) at a second time after the first time. The imaging device 112 may be capable of taking a two dimensional (2D) image or a 3D image to yield the image data. The imaging device 112 may be or comprise, for example, an ultrasound scanner (which may comprise, for example, a physically separate transducer and receiver, or a single ultrasound transceiver), an 0-arm, a C-arm, a G-arm, or any other device utilizing X-ray-based imaging (e.g., a fluoroscope, a CT scanner, or other X-ray machine), a magnetic resonance imaging (MRI) scanner, an optical coherence tomography (OCT) scanner, an endoscope, a microscope, an optical camera, a thermographic camera (e.g., aninfrared camera), a radar system (which may comprise, for example, a transmitter, a receiver, a processor, and one or more antennae), or any other imaging device 112 suitable for obtaining images of an anatomical feature of a patient. The imaging device 112 may be contained entirely within a single housing, or may comprise a transmitter / emitter and a receiver / detector that are in separate housings or are otherwise physically separated.

[0068] In some embodiments, the imaging device 112 may be or comprise one or more depth sensors, cameras, or tracking devices. The depth sensors may track the position of one or more users (e.g., one or more surgeons, one or more members of a surgical staff, a patient, etc.) within the context of a surgical environment (e.g., an operating room, an exam room during a surgical procedure, etc.), and may relay the information related to the position of the one or more users to one or more components of the system 100 (e.g., the computing device 102, the navigation system 118, etc.). In one embodiment, the depth sensors may track one or more portions of the user (e.g., the arms of the user, the hands of the user, etc.) using, for example, a kinetic skeleton model to track the joints of the user and the movements thereof.

[0069] In some embodiments, the imaging device 112 may comprise more than one imaging device 112. For example, a first imaging device may provide first image data and / or a first image, and a second imaging device may provide second image data and / or a second image. In still other embodiments, the same imaging device may be used to provide both the first image data and the second image data, and / or any other image data described herein. The imaging device 112 may be operable to generate a stream of image data. For example, the imaging device 112 may be configured to operate with an open shutter, or with a shutter that continuously alternates between open and shut so as to capture successive images. For purposes of the present disclosure, unless specified otherwise, image data may be considered to be continuous and / or provided as an image data stream if the image data represents two or more frames per second.

[0070] The robotic system 114 may be any surgical robot or surgical robotic system. The robot or robotic system 114 may be or comprise, for example, the Mazor X™ Stealth Edition robotic guidance system. The robotic system 114 may be configured to position the imaging device 112 at one or more precise pose(s) (i.e., position(s) and orientation(s)), and / or to return the imaging device 112 to the same pose(s) at a later point in time. The robotic systeml 14 may additionally or alternatively be configured to manipulate a surgical tool (whether based on guidance from the navigation system 118 or not) to accomplish or to assist with a surgical task. In someembodiments, the robotic system 114 may be configured to hold and / or manipulate an anatomical element during or in connection with a surgical procedure. The robotic system 114 may comprise one or more robotic arms 116. In some embodiments, the robotic arm 116 may comprise a first robotic arm and a second robotic arm, though the robotic system 114 may comprise more than two robotic arms. In some embodiments, one or more of the robotic arms 116 may be used to hold and / or maneuver the imaging device 112. In embodiments where the imaging device 112 comprises two or more physically separate components (e.g., a transmitter and receiver), one robotic arm 116 may hold one such component, and another robotic arm 116 may hold another such component. Each robotic arm 116 may be positionable independently of the other robotic arm. The robotic arms may be controlled in a single, shared coordinate space, or in separate coordinate spaces.

[0071] The robotic system 114, together with the robotic arm 116, may have, for example, one, two, three, four, five, six, seven, or more degrees of freedom. Further, the robotic arm 116 may be positioned or positionable in any pose, plane, and / or focal point. The pose includes a position and an orientation. As a result, an imaging device 112, surgical tool, or other object held by the robotic system 114 (or, more specifically, by the robotic arm 116) may be precisely positionable in one or more needed and specific positions and orientations.

[0072] The robotic arm(s) 116 may comprise one or more sensors that enable the processor 104 (or a processor of the robotic system 114) to determine a precise pose in space of the robotic arm (as well as any object or element held by or secured to the robotic arm).

[0073] In some embodiments, reference markers (i.e., navigation markers) may be placed on the robotic system 114 (including, e.g., on the robotic arm 116), the imaging device 112, or any other object in the surgical space. The reference markers may be tracked by the navigation system 118, and the results of the tracking may be used by the robotic system 114 and / or by an operator of the system 100 or any component thereof. In some embodiments, the navigation system 118 can be used to track other components of the system (e.g., imaging device 112) and the system can operate without the use of the robotic system 114 (e.g., with the surgeon manually manipulating the imaging device 112 and / or one or more surgical tools, based on information and / or instructions generated by the navigation system 118, for example).

[0074] The navigation system 118 may provide navigation for a surgeon and / or a surgical robot during an operation. The navigation system 118 may be any now-known or future-developednavigation system, including, for example, the Medtronic StealthStation™ S8 surgical navigation system or any successor thereof. The navigation system 118 may include one or more cameras or other sensor(s) for tracking one or more reference markers, navigated trackers, or other objects within the operating room or other room in which some or all of the system 100 is located. The one or more cameras may be optical cameras, infrared cameras, or other cameras. In some embodiments, the navigation system may comprise one or more electromagnetic sensors. In various embodiments, the navigation system 118 may be used to track a position and orientation (i.e., pose) of the imaging device 112, the robotic system 114 and / or robotic arm 116, and / or one or more surgical tools (or, more particularly, to track a pose of a navigated tracker attached, directly or indirectly, in fixed relation to the one or more of the foregoing). The navigation system 118 may include a display for displaying one or more images from an external source (e.g., the computing device 102, imaging device 112, or other source) or for displaying an image and / or video stream from the one or more cameras or other sensors of the navigation system 118. In some embodiments, the system 100 can operate without the use of the navigation system 118. The navigation system 118 may be configured to provide guidance to a surgeon or other user of the system 100 or a component thereof, to the robotic system 114, or to any other element of the system 100 regarding, for example, a pose of one or more anatomical elements, whether or not a tool is in the proper trajectory, and / or how to move a tool into the proper trajectory to carry out a surgical task according to a preoperative or other surgical plan.

[0075] The database 130 may store information that correlates one coordinate system to another (e.g., one or more robotic coordinate systems to a patient coordinate system and / or to a navigation coordinate system). Additionally or alternatively, the information directed to coordinate system correlation may be stored in or accessed by one or more other components of the system 100 (e.g., the computing device 102, the processor 104, the memory 106, etc.). The database 130 may additionally or alternatively store, for example, one or more surgical plans (including, for example, pose information about a target and / or image information about a patient’s anatomy at and / or proximate the surgical site, for use by the robotic system 114, the navigation system 118, and / or a user of the computing device 102 or of the system 100); one or more images useful in connection with a surgery to be completed by or with the assistance of one or more other components of the system 100; and / or any other useful information. The database 130 may be configured to provide any such information to the computing device 102 or to anyother device of the system 100 or external to the system 100, whether directly or via the cloud 134. In some embodiments, the database 130 may be or comprise part of a hospital image storage system, such as a picture archiving and communication system (PACS), a health information system (HIS), and / or another system for collecting, storing, managing, and / or transmitting electronic medical records including image data.

[0076] The cloud 134 may be or represent the Internet or any other wide area network. The computing device 102 may be connected to the cloud 134 via the communication interface 108, using a wired connection, a wireless connection, or both. In some embodiments, the computing device 102 may communicate with the database 130 and / or an external device (e.g., a computing device) via the cloud 134.

[0077] The system 100 or similar systems (lOOa-lOOc) may be used, for example, to carry out one or more aspects of the methods 400-600 described herein, or any other method. The system 100 or similar systems (100a- 100c) may also be used for other purposes.

[0078] Figs. 2A and 3A depict aspects of a system 100a in accordance with an example embodiment of the present disclosure. In some embodiments of the present disclosure, system 100a may determine a working environment or a navigation volume 232a associated with a planned surgery or surgical procedure and performs dynamic collision avoidance with respect to surgical systems. Fig. 2A illustrates movement of a surgical system (e.g., the robotic arm 116 of the robotic system 114) through the navigation volume 232a in accordance with the example embodiment of the present disclosure.

[0079] System 100a may include a depth sensor 204. The depth sensor 204 may be or include components similar to imaging device(s) 112 and, in some embodiments of the present disclosure, may be used to capture one or more images of and / or track movements associated the robotic arm 116 or other objects within the navigation volume 232a. For instance, as depicted in Figs. 2A and 3A, the depth sensor 204 views and captures one or more images of a patient bed 208, the robotic arm 116 and a robotic imaging device 112. As previously noted, the depth sensor 204 may additionally or alternatively track the movement of the robotic arm 116 (e.g., using kinetic models of the joints of the robotic arm 116), and generate information associated therewith. The tracked movements and other image information related to the robotic arm 116 and other objects within the navigation volume 232a such as the patient table 208, the robotic imaging device 112, a patient 202 or other user such as a surgeon or other medical personnel,may also be sent to one or more other components of the system 100a (e.g., the computing device 102, the navigation system 118, the database 130, etc.).

[0080] In some embodiments of the present disclosure, the depth sensor 204 may include one or more components of the computing device 102. In some embodiments of the present disclosure, the depth sensor 204 may be coupled with the navigation system 118, such that image information flowing from the depth sensor 204 related to the position and / or movement of the robotic arm 116 and other objects within the navigation volume 232a such as the patient bed 208, the robotic imaging device 112, the patient 202 or other user such as a surgeon or other medical personnel, may be fed into the navigation system 118. The navigation system 118 may process the information generated by the depth sensor 204 (and any other imaging device in the system 100a) and use the processed information to cause the robotic arm 116 to move in such a way as to avoid collisions with the other objects within the navigation volume 232a such as the patient bed 208, the robotic imaging device 112, the patient 202 or other user such as a surgeon or other medical personnel in proximity of the robotic arm 116.

[0081] The system 100a is configured to define the navigation volume 232a. In some embodiments of the present disclosure, the navigation volume 232a may be predefined (e.g., by a surgical plan). In other embodiments of the present disclosure, the navigation volume 232a may be defined based on information generated by the depth sensor 204. For instance, the system 100a may define the navigation volume 232a based on the relative positions of the robotic arm 116, the patient bed 208, the robotic imaging device 112 and / or one or more other objects proximate to the robotic arm 116 and / or the patient bed 208 (e.g., other surgical components or tools, stands, tool trays, walls of an operating room, patient anatomy, etc.). As illustrated in Figs. 2A and 3 A, the robotic arm 116 is mounted to the patient bed 208 at a known location. As shown in Figs. 2A and 3A, the navigation volume 232a may be defined as a volume encompassing the robotic arm 116, the robotic imaging device 112 and the patient bed 208 including a user 202. Additionally, or alternatively, the navigation volume 232a may include only one of either the robotic arm 116 or the robotic imaging device 112.

[0082] The robotic arm 116 may be configured to traverse one or more paths including a first navigation path 216a from a first position to an end position 220a. The robotic arm 116 may be caused (e.g., by a computing device 102, by a processor 104, by a navigation system 118, etc.) to traverse from the start position to the end position 220a for a variety of reasons. For instance, auser may determine that the robotic arm 116 may be better used at the end position 220a, and may use the user interface 110 to cause the robotic arm 116 to move; the robotic arm 116 may be programmed to move from the start position to the end position 220a after a predetermined amount of time or based on a current step in a surgery or surgical procedure (e.g., the robotic arm 116 is needed to move to the end position 220a to begin operating a surgical tool during a drilling step in the surgery); the robotic arm 116 may move along the first navigation path 216a to make room for additional components at the first position (e.g., a surgeon moves to place a surgical tool at the first position occupied by the robotic arm 116); and / or for any other reason. It is to be understood that while the terms “start” and “end” are used here to describe the movement of the robotic arm 116 along the first navigation path 216a, the robotic arm 116 is not restricted to a singular movement along a single navigation path in this context. Indeed, the robotic arm 116 may move multiple times in the duration of the surgery or surgical procedure along multiple different navigation paths from multiple different start points to multiple different end points for various reasons. Furthermore, while examples are provided in relation to a single robotic arm 116, it is to be understood from the embodiments of the present disclosure that multiple robotic arms may be moved, and subsequent navigation paths adjusted.

[0083] Additionally, while discussions herein are directed to the navigation system 118 dynamically updating the navigation paths for the robotic arm 116, the navigation paths associated with additional or alternative components (e.g., different robotic arms, the robotic imaging device 112, surgical tools connected to navigated components, etc.) may also be calculated. The navigation paths for these alternative components are also dynamically updated by the navigation system 118 using methods and techniques discussed herein.

[0084] Before or during the movement of the robotic arm 116 along the first navigation path 216a, the depth sensor 204 (and / or one or more components thereof) may capture one or more images of the surgical environment (such as images of the navigation volume 232a, the robotic arm 116, the robotic imaging device 112, and / or the patient bed 208), and / or other information associated with the relative positioning of the robotic arm 116, the robotic imaging device 112, and / or the patient bed 208. The system 100a may process the information (e.g., via the processor 104) received from the depth sensor 204 to determine the poses of the robotic arm 116, the robotic imaging device 112, and / or the patient bed 208.

[0085] Based on the pose of the robotic arm 116, the pose of the robotic imaging device 112 and / or the position of the patient bed 208, the system 100a may construct a 3D volume collision avoidance mapping 212a. The 3D map 212a may provide one or more sub-volumes that define areas that are more or less likely to be occupied by the other surgical systems from the time the pose of the robotic arm 116 was determined. The 3D volume collision avoidance mapping 212a may include one or more high probability zones 224a, one or more low probability zones 228a, and / or one or more no-fly zones 230a. The one or more high probability zones 224a may correspond to a volume that the system 100a has determined has a high chance of being occupied by a user or other surgical system (i.e., robotic imaging device 112) in the future or near future, while the one or more low probability zones 228a may be a volume that the system 100a has determined is unlikely to be occupied by a user or other surgical system in the future or near future.

[0086] In some embodiments of the present disclosure, the one or more high probability zones 224a and / or the one or more low probability zones 228a may be based on the one or more images and / or the other information collected by the depth sensor 204. The one or more high probability zones 224a and / or the one or more low probability zones 228a may be defined in the 3D volume collision avoidance mapping 212a based on not only the information captured by the depth sensor 204 (or other imaging components of the system 100a), but additionally or alternatively based on a variety of parameters. For instance, the system 100a may take into account the type of surgery (e.g., a spinal surgery may define high probability zones around a working volume proximate to or encompassing the spine of a patient); a handedness of the surgeon (e.g., whether the surgeon is right handed or left handed); the type of surgeon (e.g., an orthopedic surgeon may move differently and / or may move more frequently than an anesthesiologist); the type of surgical procedure (e.g., a surgical procedure on a single vertebra may involve less movement of the surgeon than a surgical procedure involving multiple vertebrae); a time of day (e.g., during morning surgeries, a surgeon may move more often and more quickly than surgeries later in the day); a patient position (e.g., a patient in the prone position may move less than a patient in a standing position); information associated with the patient (e.g., gender, height, weight, etc.); a surgical room (e.g., larger surgical rooms may permit for greater range of movement of the surgeon and / or the robotic arm); a surgical team (e.g., the number and type of surgeons or other surgical staff may result in increased user movement and additional high traffic areas); a durationof a surgery (e.g., a longer surgery may require movement of the surgeon more often than a shorter surgery); combinations thereof; and / or the like. In some embodiments of the present disclosure, the system 100a may use one or more prediction algorithms (e.g., prediction algorithms 122) that may be, include, or implement machine learning algorithms (e.g., a classifier, a Support Vector Machine (SVM), etc.) trained on historical data related to the above- mentioned parameters (e.g., data from previous surgeries, data tied to the specific surgeon or surgical team, etc.) to predict the movement of the surgical system or the user and generate the 3D volume collision avoidance mapping 212a based thereon.

[0087] Based on the definition of the 3D volume collision avoidance mapping 212a, the navigation system 118 (or components thereof such as the computing device 102) may cause the robotic arm 116 to move along a different navigation path in order to, for example, avoid the high probability zone 224a. The navigation system 118 may determine that a collision is likely to occur if the robotic arm 116 were to continue navigation along the first navigation path 216a (due to first navigation path 216a crossing through the high probability zone 224a of the 3D volume collision avoidance mapping 212a) and may calculate a different navigation route for the robotic arm 116 to traverse, such as a second navigation path 236a or a third navigation path 240a. The determination of the navigation path for the robotic arm 116 may be determined along any part of the first navigation path 216a and may be calculated in real time or near real time based on changes in movement associated with a user. Alternatively, the determination of the navigation path may be preprogrammed based on the surgical plan or predicted.

[0088] Additionally, or alternatively, the navigation system 118 may cause the speed of the robotic arm 116 to be adjusted based on the 3D volume collision avoidance mapping 212a. In some embodiments of the present disclosure, the navigation system 118 may determine that the movement of the robotic arm 116 through along the first navigation path 216a should be slowed down or sped up, based on the movement of a user, the movement of the robotic imaging device 112, and / or safety rules defined by the system 100a. For instance, the navigation system 118 may cause the movement of the robotic arm 116 to speed up (such that the robotic arm 116 passes through the high probability zone 224a in which collision is likely before a user or other surgical system enters the high probability zone 224a) or slow down (such that the robotic arm 116 continues on the first navigation path 216a at a slower speed so as to permit a user or other surgical systems to pass through the high probability zone 224a before the robotic arm 116 entersthe high probability zone 224a) while navigating along the first navigation path 216a to reduce the chance of collision between a user or other surgical systems and the robotic arm 116. In some embodiments of the present disclosure, the navigation system 118 may adjust the movement speed of the robotic arm 116 only when the robotic arm 116 is inside of or near the high probability zone 224a, such as when the robotic arm 116 is within a threshold distance of the high probability zone 224a (e.g., within 5 meters (m), within 2m, within Im , within 0.5m, within 0.2m, within 0.1m, etc. of the high probability zone 224a).

[0089] In some embodiments of the present disclosure, the navigation system 118 may detect when a user or other surgical system moves into or out of one or more of the high probability zones 224a and the one or more low probability zones 228a. Moreover, the system 100a may redefine or recreate the 3D volume collision avoidance mapping 212a based on the movement of a user or other surgical system component. For example, a user may enter the one or more high probability zones 224a, and the depth sensor 204 may capture information related to the movement of the user or other surgical system component with the information being relayed to the system 100a. The system 100a may then update the 3D volume collision avoidance mapping 212a (e.g., the high probability zone 224a may be redefined as a no-fly zone 230a, etc.).

[0090] In some embodiments of the present disclosure, the system 100a may consider the above-mentioned parameters (e.g., type of surgeon, patient positioning, the time of day, the type of surgery, duration of surgery, etc.) in defining the probability ranges and / or the rules governing the movement of the robotic arm 116 along the first navigation path 216a, or any other navigation path. For example, the navigation system 118 permits the robotic arm 116 to navigate through the low probability zone 228a for a first procedure type but does not allow the robotic arm 116 to navigate through the low probability zone 228a for a second procedure type.

[0091] In some embodiments of the present disclosure, the end point 220a may be the same for the second navigation path 236a and / or third navigation path 240a. In other embodiments of the present disclosure, the end position 220a may be different for one or both of the second navigation path 236a and third navigation path 240a than the end position 220a of the first navigation path 216a. In some embodiments of the present disclosure, the end position 220a may be dynamically updated based on the planned movement or a predicted movement of a user, the robotic imaging device 112 or other object within the navigation volume 232a. For instance, at a first time the user or the robotic imaging device 112 may move such that the navigation system118 causes the robotic arm 116 to change from moving along the first navigation path 216a to the second navigation path 236a or to the third navigation path 240a, with the end position 220a remaining the same. However, at a second time, a change in the movement and / or position of a user or the robotic imaging device 112 (e.g., the user changes the direction in which he is walking, the user reaches for a tool proximate the end position 220a, etc.) may be captured and processed by the depth sensor 204 (and / or one or more components therein such as the computing device 102), and the robotic arm 116 may be caused to move to a different end point than the end point 220a such that the robotic arm 116 avoids collision with the user or robotic imaging device 112 at the end position 220a.

[0092] In some embodiments of the present disclosure, the 3D volume collision avoidance mapping 212a may define the no-fly zone 230a that may be or include an immediate area around the robotic imaging device 112, the patient bed 208 or a user (e.g., a volume defined by a Im distance, 0.5m distance, a 0.2m distance, a 0.1m distance, etc. from the user in all directions). The navigation system 118 may cause the robotic arm 116 to move differently near the no-fly zone 230a than other sub-volumes in the 3D volume collision avoidance mapping 212a. For instance, the navigation system 118 may define the no-fly zone 230a as a volume through which the robotic arm 116 may not traverse. In some embodiments of the present disclosure, the no-fly zone 230a may be a volume through which the robotic arm 116 may navigate subject to restrictions (e.g., the robotic arm 116 moves at the slowest speed setting). In some embodiments of the present disclosure, the navigation system 118 may identify the no-fly zone 230a, and automatically re-route the robotic arm 116 if the navigation path traversed by the robotic arm 116 passes through the no-fly zone 230a. In some embodiments of the present disclosure, the navigation system 118 may determine the no-fly zone 230a and may cause the robotic arm 116 to slow down or stop navigating the navigation path if the navigation path passes through the no-fly zone 230a and / or comes within a threshold distance of the no-fly zone 230a (e.g., within 5m, within 2m, within Im, etc. of the no-fly zone 230a).

[0093] In some embodiments of the present disclosure, the no-fly zone 230a may be modified by the navigation system 118 within the 3D volume collision avoidance mapping 212a based on, for example, a variety of parameters (e.g., the surgeon, the type of surgery or surgical procedure, the duration of the surgery or surgical procedure, etc.), safety regulation, and / or how hazardous the robotic arm 116 may be to user. For example, the depth sensor 204 may capture images thatshow that an end effector of the robotic arm 116 is gripping a surgical tool (e.g., a drill or other object hazardous to the user), and the updated 3D volume collision avoidance mapping 212a may include an expanded volume of the no-fly zone 230a, such that the possible navigation paths the robotic arm 116 cause the robotic arm 116 to move at a further relative distance from a user than with the original no-fly zone 230a. In another example, the depth sensor 204 may capture information indicating that the robotic arm 116 is carrying a drape (e.g., an object not hazardous to a user), and the resulting 3D volume collision avoidance mapping 212a as constructed by the prediction algorithms 122 may maintain the volume of the no-fly zone 230a or reduce the volume of the no-fly zone 230a, such that the robotic arm 116 may move along navigation paths that bring the robotic arm 116 closer to a user while traversing the navigation path than previously permitted.

[0094] As illustrated in Fig. 3 A, the robotic arm 116 has one or more encoders 308 on some or all of its joints. Data from the encoder(s) 308 may enable the robotic system 114 or the navigation system 118 to determine a structural spatial location of the robotic arm(s) 116 with respect to the patient bed 208. In some embodiments of the present disclosure, robotic arm 116 is mounted to the patient bed 208 at a known location 315. Alternatively, or additionally the robotic arm 116 may be mounted to the floor, to a movable cart or the patient bed 208. As discussed and illustrated above with respect to Fig. 2A, the robotic arm 116 performs the 3D volume collision avoidance mapping 112a taking into consideration the patient bed 208 and other surgical systems such as the robotic imaging device 112.

[0095] The position of an end effector (e.g., terminal end) of the robotic arm 116 may be known or determined relative to a base 360 of the robotic arm 116. Given a known position 315 of the base 360 of the robotic arm 116 to the patient bed 208 and the immovable relative position of the base 360 of the robotic arm 116 and the patient bed 208, the position of the end effector relative to the patient bed 208 may be known during movement of the robotic arm 116 and / or during a stationary period of the end effector using the one or more encoders 308 at the joints of the robotic arm 116. For example, the various encoders 308 may be positioned at the movable joints along the robotic arm 116 of the robotic system 114. The encoders 308 may include electrical, optical, physical and other appropriate encoders. The encoders 308 may be used to measure and determine a relative and / or absolute movement or position of the end effector relative to the base 360 of the robotic arm 116. The robotic arm 116 of the roboticsystem 114 may include or be in communication with one or more processors 104 that receive signals from the encoders 308 to determine the absolute or relative movement of portions of the robotic system 114. The processor 104 may execute one or more instructions to determine the position of the robotic arm 116 of the robotic system 114, such as the end effector. Accordingly, the location of the end effector may be determined relative to the robotic system 114 in a robotic coordinate system 330 that may be determined relative to the based 360 of the robotic arm 116.

[0096] The registration of the robotic coordinate system 330 to a navigation coordinate system 320 (a coordinate system used for the depth sensor 204) also includes a determination of the location of the base 360 or the end effector of the robotic arm 116 as within or defining the robotic coordinate system 330 as discussed above. A determination is made whether a robotic reference device 335 is connected at a known or predetermined position relative to a selected portion of the robotic arm 116, such as an immovable or removably fixed portion, such as the base 360 of the robotic arm 116. Since the robotic reference 335 is fixed at a known or predetermined portion relative to the immovable portion, such as the base 360, then tracking the reference 335 with the depth sensor 204 would allow for an automatic determination of the robotic coordinate system 330 relative to the navigation coordinate system 320.

[0097] For example, when the robotic reference device 335 is tracked (e.g., sensed in the navigation coordinate system 320) the processor 104 may access the memory 116 to recall the position of the robotic reference device 335 in the robotic coordinate system 330. The determination of whether the robotic reference device 335 is at a known or predetermined position may include a manual input to depth sensor 204, such as with the user interface 110, or with other appropriate determinations. Thus, a determination of the registration of the robotic coordinate system 330 to the navigation coordinate system 320 based on the tracked position of the robotic reference device 335. As the robotic reference device 335 has a tracked position, the position is known in the navigation coordinate system 320. The position of the robotic reference device 335 is also known in the robotic coordinate system 330. Because the position of the robotic reference device 225 is known within robotic coordinate system 330 and the robotic coordinates are known to the fixed or removable fixed portion (e.g., the base 360), the position of the robotic reference device 335 in the robotic coordinate system 330 and the navigation coordinate system 320 are known once the robotic reference device 335 is tracked in the navigation coordinate system 320. The position of the robotic reference device 335, therefore, isused as a correlation between the robotic coordinate system 330 and the navigation coordinate system 320 to determine a translation map and registration.

[0098] The robotic imaging device 112 captures images at a location in the robotic imaging device coordinate system 340 and provides the captured images to the navigation system 118. The navigation system 118 represents the intelligence of the system 100 and reconciles the image data received from the robotic imaging device 112 with data received from the robotic system 114 of the robotic arm 116 (e.g., the encoder data, the coordinate data, etc.). The navigation system 118 correlates the location of either the patient bed 208 or the robotic arm 116 with the robotic imaging device 112. The location and model of the patient bed 208 is known. The navigation system 118 helps control the robotic arm 116 based on the information it gathers and processes. According to an alternative embodiment of the present disclosure, although the navigation system 118 reconciles the positions of the other systems, the actual computations could be performed on any of the other systems (e.g., the robotic system 114 and / or the robotic imaging device 112) without departing from the spirit and scope of the present disclosure. Registration information may be or include information related to the registration of one or more of the navigation system 118, the robotic arm 116, the robotic imaging device 112 and / or the patient bed 208 to another one or more of navigation system 118, the robotic arm 116, the robotic imaging device 112 and / or the patient bed 208. For example, registration data may contain coordinates of the navigation system 118, the robotic arm 116, the robotic imaging device 112 and / or the patient bed 208 in one or more coordinate systems (e.g., the navigation coordinate system 320, the robotic coordinate system 330, the robotic imaging device coordinate system 340 and the patient bed coordinate system 350).

[0099] Data is used to create an assumed ballpark navigation planning path for the robotic arm 116 to avoid colliding with the robotic imaging device 112 (e.g., if the robotic imaging device 112 is in an imaging operation state or in a non-imaging operation state) or an assumed ballpark navigation planning path for the robotic imaging device 112 to avoid colliding with the robotic arm 116 and / or the patient bed 208 to which the robotic arm 116 is attached. As discussed above with respect to Fig. 2A, additional calculated safety margins are taken into consideration when creating the assumed ballpark navigation planning path for the robotic arm 116 and the assumed ballpark navigation planning path for the robotic imaging device 112.

[0100] Figs. 2B and 3B depict aspects of a system 100b in accordance with another example embodiment of the present disclosure. In some embodiments of the present disclosure, system 100b may determine a working environment or a navigation volume 232b associated with a planned surgery or surgical procedure and performs dynamic collision avoidance with respect to surgical systems. Fig. 2B illustrates movement of a surgical system (e.g., the robotic arm 116 of the robotic system 114) through the navigation volume 232b in accordance with the another example embodiment of the present disclosure.

[0101] The system 100b is configured to define the navigation volume 232b. In some embodiments of the present disclosure, the navigation volume 232b may be predefined (e.g., by a surgical plan). In other embodiments of the present disclosure, the navigation volume 232b may be defined based on information generated by the depth sensor 204. For instance, the system 100a may define the navigation volume 232b based on the relative positions of the robotic arm 116, the patient bed 208, the robotic imaging device 112 and / or one or more other objects proximate to the robotic arm 116 and / or the patient bed 208 (e.g., other surgical components or tools, stands, tool trays, walls of an operating room, patient anatomy, etc.). As illustrated in Figs. 2B and 3B, the robotic arm 116 is mounted to the patient bed 208 at an unknown location. As shown in Figs. 2B and 3B, the navigation volume 232b may be defined as a volume encompassing the robotic arm 116, the robotic imaging device 112 and the patient bed 208 including a user 202. Additionally, or alternatively, the navigation volume 232b may include only one of either the robotic arm 116 or the robotic imaging device 112.

[0102] The robotic arm 116 may be configured to traverse one or more paths including a first navigation path 216b from a first position to an end position 220b. The robotic arm 116 may be caused (e.g., by a computing device 102, by a processor 104, by a navigation system 118, etc.) to traverse from the start position to the end position 220b for a variety of reasons. For instance, a user may determine that the robotic arm 116 may be better used at the end position 220b, and may use the user interface 110 to cause the robotic arm 116 to move; the robotic arm 116 may be programmed to move from the start position to the end position 220b after a predetermined amount of time or based on a current step in a surgery or surgical procedure (e.g., the robotic arm 116 is needed to move to the end position 220b to begin operating a surgical tool during a drilling step in the surgery); the robotic arm 116 may move along the first navigation path 216b to make room for additional components at the first position (e.g., a surgeon moves to place a surgicaltool at the first position occupied by the robotic arm 116); and / or for any other reason. It is to be understood that while the terms “start” and “end” are used here to describe the movement of the robotic arm 116 along the first navigation path 216b, the robotic arm 116 is not restricted to a singular movement along a single navigation path in this context. Indeed, the robotic arm 116 may move multiple times in the duration of the surgery or surgical procedure along multiple different navigation paths from multiple different start points to multiple different end points for various reasons. Furthermore, while examples are provided in relation to a single robotic arm 116, it is to be understood from the embodiments of the present disclosure that multiple robotic arms may be moved, and subsequent navigation paths adjusted.

[0103] Additionally, while discussions herein are directed to the navigation system 118 dynamically updating the navigation paths for the robotic arm 116, the navigation paths associated with additional or alternative components (e.g., different robotic arms, the robotic imaging device 112, surgical tools connected to navigated components, etc.) may also be calculated. The navigation paths for these alternative components are also dynamically updated by the navigation system 118 using methods and techniques discussed herein.

[0104] Before or during the movement of the robotic arm 116 along the first navigation path 216b, the depth sensor 204 (and / or one or more components thereof) may capture one or more images of the surgical environment (such as images of the navigation volume 232b, the robotic arm 116, the robotic imaging device 112, and / or the patient bed 208), and / or other information associated with the relative positioning of the robotic arm 116, the robotic imaging device 112, and / or the patient bed 208. The system 100a may process the information (e.g., via the processor 104) received from the depth sensor 204 to determine the poses of the robotic arm 116, the robotic imaging device 112, and / or the patient bed 208.

[0105] Based on the pose of the robotic arm 116, the pose of the robotic imaging device 112 and / or the position of the patient bed 208, the system 100b may construct a 3D volume collision avoidance mapping 212b. The 3D map 212b may provide one or more sub-volumes that define areas that are more or less likely to be occupied by the other surgical systems from the time the pose of the robotic arm 116 was determined. The 3D volume collision avoidance mapping 212b may include one or more high probability zones 224b, one or more low probability zones 228b, and / or one or more no-fly zones 230b. The one or more high probability zones 224b may correspond to a volume that the system 100b has determined has a high chance of being occupiedby a user or other surgical system (i.e., robotic imaging device 112) in the future or near future, while the one or more low probability zones 228b may be a volume that the system 100a has determined is unlikely to be occupied by a user or other surgical system in the future or near future.

[0106] In some embodiments of the present disclosure, the one or more high probability zones 224b and / or the one or more low probability zones 228b may be based on the one or more images and / or the other information collected by the depth sensor 204. The one or more high probability zones 224b and / or the one or more low probability zones 228b may be defined in the 3D volume collision avoidance mapping 212b based on not only the information captured by the depth sensor 204 (or other imaging components of the system 100b), but additionally or alternatively based on a variety of parameters. For instance, the system 100b may take into account the type of surgery (e.g., a spinal surgery may define high probability zones around a working volume proximate to or encompassing the spine of a patient); a handedness of the surgeon (e.g., whether the surgeon is right handed or left handed); the type of surgeon (e.g., an orthopedic surgeon may move differently and / or may move more frequently than an anesthesiologist); the type of surgical procedure (e.g., a surgical procedure on a single vertebra may involve less movement of the surgeon than a surgical procedure involving multiple vertebrae); a time of day (e.g., during morning surgeries, a surgeon may move more often and more quickly than surgeries later in the day); a patient position (e.g., a patient in the prone position may move less than a patient in a standing position); information associated with the patient (e.g., gender, height, weight, etc.); a surgical room (e.g., larger surgical rooms may permit for greater range of movement of the surgeon and / or the robotic arm); a surgical team (e.g., the number and type of surgeons or other surgical staff may result in increased user movement and additional high traffic areas); a duration of a surgery (e.g., a longer surgery may require movement of the surgeon more often than a shorter surgery); combinations thereof; and / or the like. In some embodiments of the present disclosure, the system 100a may use one or more prediction algorithms (e.g., prediction algorithms 122) that may be, include, or implement machine learning algorithms (e.g., a classifier, a Support Vector Machine (SVM), etc.) trained on historical data related to the above- mentioned parameters (e.g., data from previous surgeries, data tied to the specific surgeon or surgical team, etc.) to predict the movement of the surgical system or the user and generate the 3D volume collision avoidance mapping 212b based thereon.

[0107] Based on the definition of the 3D volume collision avoidance mapping 212b, the navigation system 118 (or components thereof such as the computing device 102) may cause the robotic arm 116 to move along a different navigation path in order to, for example, avoid the high probability zone 224b. The navigation system 118 may determine that a collision is likely to occur if the robotic arm 116 were to continue navigation along the first navigation path 216b (due to first navigation path 216b crossing through the high probability zone 224b of the 3D volume collision avoidance mapping 212b) and may calculate a different navigation route for the robotic arm 116 to traverse, such as a second navigation path 236b or a third navigation path 240b. The determination of the navigation path for the robotic arm 116 may be determined along any part of the first navigation path 216b and may be calculated in real time or near real time based on changes in movement associated with a user. Alternatively, the determination of the navigation path may be preprogrammed based on the surgical plan or predicted.

[0108] Additionally, or alternatively, the navigation system 118 may cause the speed of the robotic arm 116 to be adjusted based on the 3D volume collision avoidance mapping 212b. In some embodiments of the present disclosure, the navigation system 118 may determine that the movement of the robotic arm 116 through along the first navigation path 216b should be slowed down or sped up, based on the movement of a user, the movement of the robotic imaging device 112, and / or safety rules defined by the system 100b. For instance, the navigation system 118 may cause the movement of the robotic arm 116 to speed up (such that the robotic arm 116 passes through the high probability zone 224b in which collision is likely before a user or other surgical system enters the high probability zone 224b) or slow down (such that the robotic arm 116 continues on the first navigation path 216b at a slower speed so as to permit a user or other surgical systems to pass through the high probability zone 224b before the robotic arm 116 enters the high probability zone 224b) while navigating along the first navigation path 216b to reduce the chance of collision between a user or other surgical systems and the robotic arm 116. In some embodiments of the present disclosure, the navigation system 118 may adjust the movement speed of the robotic arm 116 only when the robotic arm 116 is inside of or near the high probability zone 224b, such as when the robotic arm 116 is within a threshold distance of the high probability zone 224b (e.g., within 5 meters (m), within 2m, within Im , within 0.5m, within 0.2m, within 0.1m, etc. of the high probability zone 224b).

[0109] In some embodiments of the present disclosure, the navigation system 118 may detect when a user or other surgical system moves into or out of one or more of the high probability zones 224b and the one or more low probability zones 228b, and the system 100b may redefine or recreate the 3D volume collision avoidance mapping 212b based on the movement of a user or other surgical systems. For example, a user may enter the one or more high probability zones 224b, and the depth sensor 204 may capture information related to the movement of the user or the other surgical systems, with the information being relayed to the system 100b (and / or components thereof). The system 100b may then update the 3D volume collision avoidance mapping 212b (e.g., the high probability zone 224b may be redefined as a no-fly zone 230b, etc.).

[0110] In some embodiments of the present disclosure, the system 100b may consider the above-mentioned parameters (e.g., type of surgeon, patient positioning, the time of day, the type of surgery, duration of surgery, etc.) in defining the probability ranges and / or the rules governing the movement of the robotic arm 116 along the first navigation path 216b, or any other navigation path. For example, the navigation system 118 permits the robotic arm 116 to navigate through the low probability zone 228b for a first procedure type but does not allow the robotic arm 116 to navigate through the low probability zone 228b for a second procedure type.

[0111] In some embodiments of the present disclosure, the end point 220b may be the same for the second navigation path 236b and / or third navigation path 240b. In other embodiments of the present disclosure, the end position 220b may be different for one or both of the second navigation path 236b and third navigation path 240b than the end position 220b of the first navigation path 216b. In some embodiments of the present disclosure, the end position 220b may be dynamically updated based on the planned movement or a predicted movement of a user, the robotic imaging device 112 or other object within the navigation volume 232b. For instance, at a first time the user or the robotic imaging device 112 may move such that the navigation system 118 causes the robotic arm 116 to change from moving along the first navigation path 216b to the second navigation path 236b or to the third navigation path 240b, with the end position 220b remaining the same. However, at a second time, a change in the movement and / or position of a user or the robotic imaging device 112 (e.g., the user changes the direction in which he is walking, the user reaches for a tool proximate the end position 220b, etc.) may be captured and processed by the depth sensor 204 (and / or one or more components therein such as the computing device 102), and the robotic arm 116 may be caused to move to a different end pointthan the end point 220b such that the robotic arm 116 avoids collision with the user or robotic imaging device 112 at the end position 220b.

[0112] In some embodiments of the present disclosure, the 3D volume collision avoidance mapping 212b may define the no-fly zone 230b that may be or include an immediate area around the robotic imaging device 112, the patient bed 208 or a user (e.g., a volume defined by a Im distance, 0.5m distance, a 0.2m distance, a 0.1m distance, etc. from the user in all directions). The navigation system 118 may cause the robotic arm 116 to move differently near the no-fly zone 230b than other sub-volumes in the 3D volume collision avoidance mapping 212b. For instance, the navigation system 118 may define the no-fly zone 230b as a volume through which the robotic arm 116 may not traverse. In some embodiments of the present disclosure, the no-fly zone 230b may be a volume through which the robotic arm 116 may navigate subject to restrictions (e.g., the robotic arm 116 moves at the slowest speed setting). In some embodiments of the present disclosure, the navigation system 118 may identify the no-fly zone 230b, and automatically re-route the robotic arm 116 if the navigation path traversed by the robotic arm 116 passes through the no-fly zone 230b. In some embodiments of the present disclosure, the navigation system 118 may determine the no-fly zone 230b and may cause the robotic arm 116 to slow down or stop navigating the navigation path if the navigation path passes through the no-fly zone 230a and / or comes within a threshold distance of the no-fly zone 230b (e.g., within 5m, within 2m, within Im, etc. of the no-fly zone 230b).

[0113] In some embodiments of the present disclosure, the no-fly zone 230b may be modified by the navigation system 118 within the 3D volume collision avoidance mapping 212b based on, for example, a variety of parameters (e.g., the surgeon, the type of surgery or surgical procedure, the duration of the surgery or surgical procedure, etc.), safety regulation, and / or how hazardous the robotic arm 116 may be to user. For example, the depth sensor 204 may capture images that show that an end effector of the robotic arm 116 is gripping a surgical tool (e.g., a drill or other object hazardous to the user), and the updated 3D volume collision avoidance mapping 212b may include an expanded volume of the no-fly zone 230b, such that the possible navigation paths the robotic arm 116 cause the robotic arm 116 to move at a further relative distance from a user than with the original no-fly zone 230b. In another example, the depth sensor 204 may capture information indicating that the robotic arm 116 is carrying a drape (e.g., an object not hazardous to a user), and the resulting 3D volume collision avoidance mapping 212b as constructed by theprediction algorithms 122 may maintain the volume of the no-fly zone 230b or reduce the volume of the no-fly zone 230b, such that the robotic arm 116 may move along navigation paths that bring the robotic arm 116 closer to a user while traversing the navigation path than previously permitted.

[0114] As illustrated in Fig. 3B, the robotic arm 116 has one or more encoders 308 on some or all of its joints. Data from the encoder(s) 308 may enable the robotic system 114 to determine a structural spatial location of the robotic arm(s) 116 with respect to the patient bed 208. In some embodiments of the present disclosure, robotic arm 116 is mounted to the patient bed 208 at an unknown location 335. Alternatively, or additionally the robotic arm 116 may be mounted to the floor, to a movable cart or the patient bed 208. As discussed and illustrated above with respect to Fig. 2B, the robotic arm 116 performs the 3D volume collision avoidance mapping 112b taking into consideration the patient bed 208 and other surgical systems such as the robotic imaging device 112.

[0115] As stated above, the location of where the robotic arm 116 is connected to the patient bed 208 is at an unknown location 325. The patient bed 208, however, includes a patient bed tracker 345. According to an embodiment of the present disclosure, the patient bed tracker 345 may be spatial unique identification device or method that is detectable by the robotic system 114, the depth sensor 204 and / or the robotic imaging device 112. Therefore, according to embodiments of the present disclosure, coordination between a patient bed coordinate system 350 and one or more of the navigation coordinate system 320, the robotic coordinate system 330 and a robotic imaging device coordinate system can be generated. For example, if the robotic system 114 is used, a coordination between the patient bed coordinate system 350 and the robotic coordinate system 330 can be created. For example, a coordination between the patient bed tracker 345 and the robotic reference device 335 can be established using data or information stored in memory 106 or information and data from the depth sensor 204 and the navigation system 118. Once established, the location of the where the robotic arm 116 is mounted to the patient bed 208 would be known.

[0116] Afterward, the position of the end effector of the robotic arm 116 may be known or determined relative to the base 360 of the robotic arm 116. Given the determined location of the base 360 of the robotic arm 116 to the patient bed 208 and the immovable relative position of the base 360 of the robotic arm 116 and the patient bed 208, the position of the end effector relativeto the patient bed 208 may be known during movement of the robotic arm 116 and / or during a stationary period of the end effector using the one or more encoders 308 at the joints of the robotic arm 116. For example, the various encoders 308 may be positioned at the movable joints along the robotic arm 116 of the robotic system 114. The encoders 308 may include electrical, optical, physical and other appropriate encoders. The encoders 308 may be used to measure and determine a relative and / or absolute movement or position of the end effector relative to the base 360 of the robotic arm 116. The robotic arm 116 of the robotic system 114 may include or be in communication with one or more processors 104 that receive signals from the encoders 308 to determine the absolute or relative movement of portions of the robotic system 114. The processor 104 may execute one or more instructions to determine the position of the robotic arm 116 of the robotic system 114, such as the end effector. Accordingly, the location of the end effector may be determined relative to the robotic system 114 in a robotic coordinate system 330 that may be determined relative to the based 360 of the robotic arm 116.

[0117] The registration of the robotic coordinate system 330 to the navigation coordinate system 320 also includes the determination of the location of the base 360 or the end effector of the robotic arm 116 as within or defining the robotic coordinate system 330 as discussed above. The determination is made whether a robotic reference device 335 is connected at a known or predetermined position relative to a selected portion of the robotic arm 116, such as an immovable or removably fixed portion, such as the base 360 of the robotic arm 116. Since the robotic reference 335 is fixed at a known or predetermined portion relative to the immovable portion, such as the base 360, then the tracking the robotic reference device 335 with the depth sensor 204 would allow for an automatic determination of the robotic coordinate system 330 relative to the navigation coordinate system 320.

[0118] For example, when the robotic reference device 335 is tracked (e.g., sensed in the navigation coordinate system 320) the processor 104 may access the memory 116 to recall the position of the robotic reference device 335 in the robotic coordinate system 330. The determination of whether the robotic reference device 335 is at a known or predetermined position may include a manual input to depth sensor 204, such as with the user interface 110, or with other appropriate determinations. Thus, a determination of the registration of the robotic coordinate system 330 to the navigation coordinate system 320 based on the tracked position of the robotic reference device 335. As the robotic reference device 335 has a tracked position, theposition is known in the navigation coordinate system 320. The position of the robotic reference device 335 is also known in the robotic coordinate system 330. Because the position of the robotic reference device 225 is known within robotic coordinate system 330 and the robotic coordinates are known to the fixed or removable fixed portion (e.g., the base 360), the position of the robotic reference device 335 in the robotic coordinate system 330 and the navigation coordinate system 320 are known once the robotic reference device 335 is tracked in the navigation coordinate system 320. The position of the robotic reference device 335, therefore, is used as a correlation between the robotic coordinate system 330 and the navigation coordinate system 320 to determine a translation map and registration.

[0119] The robotic imaging device 112 captures images at a location in the robotic imaging device coordinate system 340 and provides the captured images to the navigation system 118. The navigation system 118 represents the intelligence of the system 100 and reconciles the image data received from the robotic imaging device 112 with data received from the robotic system 114 of the robotic arm 116 (e.g., the encoder data, the coordinate data, etc.). The navigation system 118 also reconciles image data received from the patient bed tracker 345 in the patient bed coordinate system 350 and reconciles the tracking image data of the bed tracker 345 with data received from the robotic system 114 of the robotic arm 116 (e.g., the encoder data, the coordinate data, etc.). The navigation system 118 correlates the location of either the patient bed 208 or the robotic arm 116 with the robotic imaging device 112. The model of the patient bed 208 is known. The navigation system 118 helps control the robotic arm 116 based on the information it gathers and processes. According to an alternative embodiment of the present disclosure, although the navigation system 118 reconciles the positions of the other systems, the actual computations could be performed on any of the other systems (e.g., the robotic system 114 and / or the robotic imaging device 112) without departing from the spirit and scope of the present disclosure. Registration information may be or include information related to the registration of one or more of the navigation system 118, the robotic arm 116, the robotic imaging device 112 and / or the patient bed 208 to another one or more of navigation system 118, the robotic arm 116, the robotic imaging device 112 and / or the patient bed 208. For example, registration data may contain coordinates of the navigation system 118, the robotic arm 116, the robotic imaging device 112 and / or the patient bed 208 in one or more coordinate systems (e.g., the navigation coordinatesystem 320, the robotic coordinate system 330, the robotic imaging device coordinate system 340 and the patient bed coordinate system 350).

[0120] Data is used to create an assumed ballpark navigation planning path for the robotic arm 116 to avoid colliding with the robotic imaging device 112 (e.g., if the robotic imaging device 112 is in an imaging operation state or in a non-imaging operation state) or an assumed ballpark navigation planning path for the robotic imaging device 112 to avoid colliding with the robotic arm 116 and / or the patient bed 208 to which the robotic arm 116 is attached. As discussed above with respect to Fig. 2A, additional calculated safety margins are taken into consideration when creating the assumed ballpark navigation planning path for the robotic arm 116 and the assumed ballpark navigation planning path for the robotic imaging device 112.

[0121] Figs. 2C and 3C depict aspects of a system 100c in accordance with yet another example embodiment of the present disclosure. In some embodiments of the present disclosure, system 100c may determine a working environment or a navigation volume 232c associated with a planned surgery or surgical procedure and performs dynamic collision avoidance with respect to surgical systems. Fig. 2C illustrates movement of a surgical system (e.g., the robotic imaging device 112) through the navigation volume 232c in accordance with the yet another example embodiment of the present disclosure.

[0122] The system 100c is configured to define the navigation volume 232c. In some embodiments of the present disclosure, the navigation volume 232c may be predefined (e.g., by a surgical plan). In other embodiments of the present disclosure, the navigation volume 232c may be defined based on information generated by the depth sensor 204. For instance, the system 100c may define the navigation volume 232c based on the relative positions of the robotic arm 116, the patient bed 208, the robotic imaging device 112 and / or one or more other objects proximate to the robotic arm 116 and / or the patient bed 208 (e.g., other surgical components or tools, stands, tool trays, walls of an operating room, patient anatomy, etc.). As illustrated in Figs. 2C and 3C, the robotic arm 116 is not mounted to the patient bed 208. As shown in Figs. 2C and 3C, the navigation volume 232c may be defined as a volume encompassing the robotic arm 116, the robotic imaging device 112 and the patient bed 208 including a user 202. Additionally, or alternatively, the navigation volume 232c may include only one of either the robotic arm 116 or the robotic imaging device 112.

[0123] As illustrated in Fig. 2C, the robotic imaging device 112 may be configured to traverse one or more paths including a first navigation path 216c from a first position to an end position 220c. The robotic imaging device 112 may be caused (e.g., by a computing device 102, by a processor 104, by a navigation system 118, etc.) to traverse from the start position to the end position 220c for a variety of reasons. For instance, a user may determine that the robotic imaging device 112 may be better used at the end position 220c, and may use the user interface 110 to cause the robotic imaging device 112 to move; the robotic imaging device 112 may be programmed to move from the start position to the end position 220c after a predetermined amount of time or based on a current step in a surgery or surgical procedure. It is to be understood that while the terms “start” and “end” are used here to describe the movement of the robotic imaging device 112 along the first navigation path 216c, the robotic imaging device 112 is not restricted to a singular movement along a single navigation path in this context and may move in more alternative paths.

[0124] Based on the pose of the robotic arm 116, the pose of the robotic imaging device 112 and / or the position of the patient bed 208, the system 100c may construct a 3D volume collision avoidance mapping 212c. The 3D map 212c may provide one or more sub-volumes that define areas that are more or less likely to be occupied by the other surgical systems from the time the pose of the robotic imaging device 112 was determined. The 3D volume collision avoidance mapping 212c may include one or more high probability zones 224c, one or more low probability zones 228b, and / or one or more no-fly zones 230c. The one or more high probability zones 224c may correspond to a volume that the system 100c has determined has a high chance of being occupied by a user or other surgical system (i.e., robotic arm 116) in the future or near future, while the one or more low probability zones 228c may be a volume that the system 100c has determined is unlikely to be occupied by a user or other surgical system in the future or near future.

[0125] In some embodiments of the present disclosure, the one or more high probability zones 224c and / or the one or more low probability zones 228c may be based on the one or more images and / or the other information collected by the depth sensor 204. The one or more high probability zones 224c and / or the one or more low probability zones 228c may be defined in the 3D volume collision avoidance mapping 212c based on not only the information captured by the depth sensor 204 (or other imaging components of the system 100c), but additionally or alternatively based ona variety of parameters. For instance, the system 100c may take into account the type of surgery (e.g., a spinal surgery may define high probability zones around a working volume proximate to or encompassing the spine of a patient); a handedness of the surgeon (e.g., whether the surgeon is right handed or left handed); the type of surgeon (e.g., an orthopedic surgeon may move differently and / or may move more frequently than an anesthesiologist); the type of surgical procedure (e.g., a surgical procedure on a single vertebra may involve less movement of the surgeon than a surgical procedure involving multiple vertebrae); a time of day (e.g., during morning surgeries, a surgeon may move more often and more quickly than surgeries later in the day); a patient position (e.g., a patient in the prone position may move less than a patient in a standing position); information associated with the patient (e.g., gender, height, weight, etc.); a surgical room (e.g., larger surgical rooms may permit for greater range of movement of the surgeon and / or the robotic arm); a surgical team (e.g., the number and type of surgeons or other surgical staff may result in increased user movement and additional high traffic areas); a duration of a surgery (e.g., a longer surgery may require movement of the surgeon more often than a shorter surgery); combinations thereof; and / or the like. In some embodiments of the present disclosure, the system 100a may use one or more prediction algorithms (e.g., prediction algorithms 122) that may be, include, or implement machine learning algorithms (e.g., a classifier, a Support Vector Machine (SVM), etc.) trained on historical data related to the above- mentioned parameters (e.g., data from previous surgeries, data tied to the specific surgeon or surgical team, etc.) to predict the movement of the surgical system or the user and generate the 3D volume collision avoidance mapping 212c based thereon.

[0126] Based on the definition of the 3D volume collision avoidance mapping 212c, the navigation system 118 (or components thereof such as the computing device 102) may cause the robotic imaging device 112 to move along a different navigation path in order to, for example, avoid the high probability zone 224c. The navigation system 118 may determine that a collision is likely to occur if the robotic imaging device 112 were to continue navigation along the first navigation path 216c (due to first navigation path 216c crossing through the high probability zone 224c of the 3D volume collision avoidance mapping 212c) and may calculate a different navigation route for the robotic imaging device 112 to traverse.

[0127] In some embodiments of the present disclosure, the navigation system 118 may detect when a user or other surgical system moves into or out of one or more of the high probabilityzones 224c and the one or more low probability zones 228c, and the system 100c may redefine or recreate the 3D volume collision avoidance mapping 212c based on the movement of a user or other surgical systems. For example, a user may enter the one or more high probability zones 224c, and the depth sensor 204 may capture information related to the movement of the user or the other surgical systems, with the information being relayed to the system 100c (and / or components thereof). The system 100c may then update the 3D volume collision avoidance mapping 212c (e.g., the high probability zone 224c may be redefined as a no-fly zone 230c, etc.).

[0128] In some embodiments of the present disclosure, the system 100c may consider the above-mentioned parameters (e.g., type of surgeon, patient positioning, the time of day, the type of surgery, duration of surgery, etc.) in defining the probability ranges and / or the rules governing the movement of the robotic arm 116 along the first navigation path 216c, or any other navigation path. For example, the navigation system 118 permits the robotic arm 116 to navigate through the low probability zone 228c for a first procedure type but does not allow the robotic arm 116 to navigate through the low probability zone 228c for a second procedure type.

[0129] In some embodiments of the present disclosure, the 3D volume collision avoidance mapping 212c may define the no-fly zone 230c that may be or include an immediate area around the robotic arm 116, the patient bed 208 or a user (e.g., a volume defined by a Im distance, 0.5m distance, a 0.2m distance, a 0.1m distance, etc. from the user in all directions). The navigation system 118 may cause the robotic imaging device 112 to move differently near the no-fly zone 230c than other sub-volumes in the 3D volume collision avoidance mapping 212c. For instance, the navigation system 118 may define the no-fly zone 230c as a volume through which the robotic imaging device 112 may not traverse. In some embodiments of the present disclosure, the no-fly zone 230c may be a volume through which the robotic imaging device 112 may navigate subject to restrictions In some embodiments of the present disclosure, the navigation system 118 may identify the no-fly zone 230c, and automatically re-route the robotic imaging device 112 if the navigation path traversed by the robotic imaging device 112 passes through the no-fly zone 230c. In some embodiments of the present disclosure, the navigation system 118 may determine the no-fly zone 230c and may cause the robotic imaging device 112 to slow down or stop navigating the navigation path if the navigation path passes through the no-fly zone 230c and / or comes within a threshold distance of the no-fly zone 230c (e.g., within 5m, within 2m, within Im, etc. of the no-fly zone 230c).

[0130] In some embodiments of the present disclosure, the no-fly zone 230c may be modified by the navigation system 118 within the 3D volume collision avoidance mapping 212c based on, for example, a variety of parameters (e.g., the surgeon, the type of surgery or surgical procedure, the duration of the surgery or surgical procedure, etc.), safety regulation, and / or how hazardous the robotic arm 116 may be to user.

[0131] As illustrated in Fig. 3C, the robotic arm 116 has one or more encoders 308 on some or all of its joints. Data from the encoder(s) 308 may enable the robotic system 114 to determine a structural spatial location of the robotic arm(s) 116 with respect to the patient bed 208 using the navigation system 118. In some embodiments of the present disclosure, robotic arm 116 is provided in proximity to the patient bed 208. Alternatively, or additionally the robotic arm 116 may be mounted to the floor, to a movable cart or the patient bed 208. As discussed and illustrated above with respect to Fig. 2C, the robotic imaging device 112 performs the 3D volume collision avoidance mapping 212c taking into consideration the patient bed 208 and other surgical systems such as the robotic imaging device 112. Therefore, according to embodiments of the present disclosure, coordination between the robotic coordinate system 350 and one or more of the navigation coordinate system 320, the robotic coordinate system 330 and the patient bed coordinate system 350 can be generated as discussed above.

[0132] Data is used to create an assumed ballpark navigation planning path for the robotic arm 116 to avoid colliding with the robotic imaging device 112 (e.g., if the robotic imaging device 112 is in an imaging operation state or in a non-imaging operation state) or an assumed ballpark navigation planning path for the robotic imaging device 112 to avoid colliding with the robotic arm 116 and / or the patient bed 208 to which the robotic arm 116 is attached. As discussed above with respect to Fig. 2C, additional calculated safety margins are taken into consideration when creating the assumed ballpark navigation planning path for the robotic arm 116 and the assumed ballpark navigation planning path for the robotic imaging device 112.

[0133] According to embodiments of the present disclosure, the no-fly zones 230a-230c discussed above may include a zone where a microscope is provided. According to embodiments of the present disclosure, a microscope may be included as one or more of the objects provided in the navigation volume 232a-232c illustrated in Figs. 2A-3C. The microscope may be tracked as discussed above. For example, the microscope may include a tracking device that is used by thenavigation system 118, the robotic system 114 and / or the robotic imaging device 112 used to determine the location and position of the microscope within the navigation volume 232a-232c.

[0134] According to further embodiments of the present disclosure, the microscope may be a motorized microscope and the motorized microscope may be tracked into a known location or reposition into a known location by one or more of the navigation system 118, the robotic system 114 and / or the robotic imaging device 112. The motorized microscope is tracked such that neither the robotic system 114 nor the robotic imaging device 112 collides with the motorized microscope.

[0135] Fig. 4 is a flowchart of a method 400 according to an example embodiment of the present disclosure. The method 400 (and / or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to the processor(s) 104 of the computing device 102 described above. The at least one processor may be part of a robot (such as the robotic system 114) or part of a navigation system (such as a navigation system 118). A processor other than any processor described herein may also be used to execute the method 400. The at least one processor may perform the method 400 by executing instructions stored in a memory such as the memory 106. The instructions may correspond to one or more steps of the method 400 described below. The instructions may cause the processor to execute one or more algorithms, such as an image processing algorithm 120, a prediction algorithm 122, a navigation algorithm 124, and / or a registration algorithm 128.

[0136] While a general order for the steps of the method 400 is shown in Fig. 4, the method 400 can include more or fewer steps or can arrange the order of the steps differently than those shown in Fig. 4. Generally, the method 400 starts with a START operation at step 404 and ends with an END operation at step 432. The method 400 can be executed as a set of computerexecutable instructions executed by an assembly machine (e.g., robotic assembly system, automation assembly system, computer aided drafting (CAD) machine, etc.) and encoded or stored on a computer readable medium. Hereinafter, the method 400 shall be explained with reference to the components, devices, assemblies, environments, etc. described in conjunction with Figs. 1, 2A and 3A.

[0137] The method 400 may begin with the START operation at step 404 and proceed to step 408 where the robotic system 114 is mounted to a patient bed 208 at a known location in asurgical environment. After the robotic system 114 is mounted to the patient bed 208 at the known location in the surgical environment at step 408, method 400 proceeds to step 412, where a robotic imaging device 112 is provided proximate to the mounted robotic system 114 on the patient bed 208. After the robotic imaging device 112 is provided proximate to the mounted robotic system 114 on the patient bed 208 at step 412, method 400 proceeds to step 416, where first information about a pose of the robotic system 112 is received.

[0138] According to embodiments of the present disclosure, the data related to the pose of the robotic system 114 (e.g., a robotic arm 116) may be captured by a depth sensor (e.g., a depth sensor 204), an imaging device (e.g., an imaging device 112), and / or one or more other sensors (e.g., a force gauge sensor contacting the robotic arm 116). In some embodiments of the present disclosure, the robotic arm 116 may comprise one or more tracking markers that facilitate definition of the pose of the robotic arm 116. For instance, the tracking markers may appear visible in captured images of the robotic arm 116 within the surgical environment, such that the system can identify the markers and, by extension, the pose of the robotic arm 116. The system may make use of one or more image processing algorithms (e.g., image processing algorithms 120) that receive the image information as an input and output the pose of the robotic arm 116 and / or one or more other components or elements within the surgical environment that also comprise tracking markers (e.g., the robotic imaging device 112, surgical tools and the patient bed 208, imaging equipment, etc.). The data related to the position of the robotic arm 116, a user, and / or other components or elements may be repeatedly captured by the sensors (e.g., the depth sensor) throughout the course of the surgery or surgical procedure.

[0139] After the first information about a pose of the robotic system 112 is received at step 416, method 400 proceeds to step 420, where second information about the patient bed 208 relative to the robotic system 114 is received. As noted above in step 416, the system may use one or more image processing algorithms that output information about the patient bed 208 relative to the robotic system 114, and additionally or alternatively the pose of one or more other components or elements within the surgical environment. The resulting pose information may be passed from the one or more image processing algorithms to one or more components of the system (e.g., a computing device such as a computing device 102, a navigation system such as a navigation system 118, etc.).

[0140] After the second information about the patient bed 208 relative to the robotic system 114 is received at step 420, method 400 proceeds to step 424 where a pose of the robotic imaging device 114 relative to the robotic system 114 or relative to the patient bed 208 is determined based on the first and second information. The system may use one or more registration algorithms (e.g., registration algorithms 128) to register the robotic imaging device 112 or the patient bed 208 to the robotic system 114. The registration of the robotic imaging device 112 or the patient bed 208 to the robotic system 114 may allow the system to determine relative movements of the robotic arm 116 and / or one or more objects within a common coordinate system, allowing the system to identify pose(s) of the robotic arm 116 and / or one or more objects to, for example, prevent collisions when the movement and / or location of the one or more objects (e.g., if the object moves from a first pose at a first time to a second pose at a second time, the system can identify coordinates associated with the object at the first time and / or coordinates associated with the object at the second time, and prevent the robotic arm 116 from occupying or passing through those coordinates to facilitate collision avoidance). The registration algorithm may take one or more coordinate points of the robotic arm 116 in the robotic system coordinate system 330 and one or more coordinate points of the robotic imaging device 112 in the robotic imaging device coordinate system 340 as input values, and may output the coordinate points of the robotic arm 116 in the robotic imaging device coordinate system 340. In some embodiments of the present disclosure, the registration algorithm may output many sets of coordinates of the robotic arm 116, the robotic imaging device 112, the patient bed 208 and the depth sensor 204 into a single common coordinate system.

[0141] After the pose of the robotic imaging device 114 relative to the robotic system 114 or relative to the patient bed 208 is determined based on the first and second information at step 424, method 400 proceeds to step 428 where a navigation path for the imaging device 112 is generated based on the determined pose of the robotic imaging device 112 relative to the robotic system 114 or the patient bed 208. The navigation system 118 may cause the robotic imaging device 112 or the robotic system 114 to move along the navigation path such that the robotic arm 116 or the robotic imaging device 112 avoids high traffic areas (e.g., a high traffic zone 224a) and / or no-fly zones 230a. After the navigation path for the imaging device 112 is generated based on the determined pose of the imaging device 112 relative to the robotic system 114 or the patient bed 208 at step 428, method 400 may end with the END operation at step 432.

[0142] Fig. 5 is a flowchart of a method 500 according to another example embodiment of the present disclosure. The method 500 (and / or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to the processor(s) 104 of the computing device 102 described above. The at least one processor may be part of a robot (such as the robotic system 114) or part of a navigation system (such as a navigation system 118). A processor other than any processor described herein may also be used to execute the method 500. The at least one processor may perform the method 500 by executing instructions stored in a memory such as the memory 106. The instructions may correspond to one or more steps of the method 500 described below. The instructions may cause the processor to execute one or more algorithms, such as an image processing algorithm 120, a prediction algorithm 122, a navigation algorithm 124, and / or a registration algorithm 128.

[0143] While a general order for the steps of the method 500 is shown in Fig. 5, the method 500 can include more or fewer steps or can arrange the order of the steps differently than those shown in Fig. 5. Generally, the method 500 starts with a START operation at step 504 and ends with an END operation at step 536. The method 500 can be executed as a set of computerexecutable instructions executed by an assembly machine (e.g., robotic assembly system, automation assembly system, computer aided drafting (CAD) machine, etc.) and encoded or stored on a computer readable medium. Hereinafter, the method 500 shall be explained with reference to the components, devices, assemblies, environments, etc. described in conjunction with Figs. 1, 2B and 3B.

[0144] The method 500 may begin with the START operation at step 504 and proceed to step 508 where the robotic system 114 is mounted to a patient bed 208 at an unknown location in a surgical environment. After the robotic system 114 is mounted to the patient bed 208 at the unknown location in the surgical environment at step 508, method 500 proceeds to step 512, where a robotic imaging device 112 is provided proximate to the mounted robotic system 114 on the patient bed 208. After the robotic imaging device 112 is provided proximate to the mounted robotic system 114 on the patient bed 208 at step 512, method 500 proceeds to step 516, where first information about the patient bed 208 is received.

[0145] According to embodiments of the present disclosure, the data related to the pose of the robotic system 114 (e.g., a robotic arm 116) may be captured by a depth sensor (e.g., a depthsensor 204), an imaging device (e.g., an imaging device 112), and / or one or more other sensors (e.g., a force gauge sensor contacting the robotic arm 116). In some embodiments of the present disclosure, the robotic arm 116 may comprise one or more tracking markers that facilitate definition of the pose of the robotic arm 116. For instance, the tracking markers may appear visible in captured images of the robotic arm 116 within the surgical environment, such that the system can identify the markers and, by extension, the pose of the robotic arm 116. The system may make use of one or more image processing algorithms (e.g., image processing algorithms 120) that receive the image information as an input and output the pose of the robotic arm 116 and / or one or more other components or elements within the surgical environment that also comprise tracking markers (e.g., the robotic imaging device 112, surgical tools and the patient bed 208, imaging equipment, etc.). The data related to the position of the robotic arm 116, a user, and / or other components or elements may be repeatedly captured by the sensors (e.g., the depth sensor) throughout the course of the surgery or surgical procedure.

[0146] After the first information about the patient bed 208 is received at step 516, method 500 proceeds to step 520, where second information about the pose of the robotic system 114 is received. After the second information about the robotic system 114 is received at step 520, method 500 proceeds to step 524 where third information about the patient bed 208 relative to the robotic arm 114 is received. As noted above in step 516, the system may use one or more image processing algorithms that output information about the patient bed 208 relative to the robotic system 114, and additionally or alternatively the pose of one or more other components or elements within the surgical environment. The resulting pose information may be passed from the one or more image processing algorithms to one or more components of the system (e.g., a computing device such as a computing device 102, a navigation system such as a navigation system 118, etc.).

[0147] After the third information about the patient bed 208 relative to the robotic arm 114 is received at step 524, method 500 proceeds to step 528 where a pose of the robotic imaging device 114 relative to the robotic system 114 or relative to the patient bed 208 is determined based on the first, second and third information.

[0148] The system may use one or more registration algorithms (e.g., registration algorithms 128) to register the robotic imaging device 112 or the patient bed 208 to the robotic system 114. The registration of the robotic imaging device 112 or the patient bed 208 to the robotic system114 may allow the system to determine relative movements of the robotic arm 116 and / or one or more objects within a common coordinate system, allowing the system to identify pose(s) of the robotic arm 116 and / or one or more objects to, for example, prevent collisions when the movement and / or location of the one or more objects (e.g., if the object moves from a first pose at a first time to a second pose at a second time, the system can identify coordinates associated with the object at the first time and / or coordinates associated with the object at the second time, and prevent the robotic arm 116 from occupying or passing through those coordinates to facilitate collision avoidance). The registration algorithm may take one or more coordinate points of the robotic arm 116 in the robotic system coordinate system 330 and one or more coordinate points of the robotic imaging device 112 in the robotic imaging device coordinate system 340 as input values, and may output the coordinate points of the robotic arm 116 in the robotic imaging device coordinate system 340. In some embodiments of the present disclosure, the registration algorithm may output many sets of coordinates of the robotic arm 116, the robotic imaging device 112, the patient bed 208 and the depth sensor 204 into a single common coordinate system.

[0149] After the pose of the robotic imaging device 114 relative to the robotic system 114 or relative to the patient bed 208 is determined based on the first, second and third information at step 528, method 500 proceeds to step 532 where a navigation path for the imaging device 112 is generated based on the determined pose of the imaging device 112 relative to the robotic system 114 or the patient bed 208. The navigation system 118 may cause the robotic imaging device 112 or the robotic system 114 to move along the navigation path such that the robotic arm 116 or the robotic imaging device 112 avoids high traffic areas (e.g., a high traffic zone 224b) and / or no-fly zones 230b. After the navigation path for the imaging device 112 is generated based on the determined pose of the imaging device 112 relative to the robotic system 114 or the patient bed 208 at step 532, method 500 may end with the END operation at step 536.

[0150] Fig. 6 is a flowchart of a method 600 according to yet another example embodiment of the present disclosure. The method 600 (and / or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to the processor(s) 104 of the computing device 102 described above. The at least one processor may be part of a robot (such as the robotic system 114) or part of a navigation system (such as a navigation system 118). A processor other than any processordescribed herein may also be used to execute the method 600. The at least one processor may perform the method 600 by executing instructions stored in a memory such as the memory 106. The instructions may correspond to one or more steps of the method 600 described below. The instructions may cause the processor to execute one or more algorithms, such as an image processing algorithm 120, a prediction algorithm 122, a navigation algorithm 124, and / or a registration algorithm 128.

[0151] While a general order for the steps of the method 600 is shown in Fig. 6, the method 600 can include more or fewer steps or can arrange the order of the steps differently than those shown in Fig. 6. Generally, the method 600 starts with a START operation at step 604 and ends with an END operation at step 628. The method 600 can be executed as a set of computerexecutable instructions executed by an assembly machine (e.g., robotic assembly system, automation assembly system, computer aided drafting (CAD) machine, etc.) and encoded or stored on a computer readable medium. Hereinafter, the method 600 shall be explained with reference to the components, devices, assemblies, environments, etc. described in conjunction with Figs. 1, 2C and 3C.

[0152] The method 600 may begin with the START operation at step 604 and proceed to step 608 where the robotic system 114 is placed in proximity to a patient bed 208 in a surgical environment. After the robotic system 114 is placed in proximity to the patient bed 208 in the surgical environment at step 608, method 600 proceeds to step 612, where a robotic imaging device 112 is provided proximate to the robotic system 114 and the patient bed 208. After the robotic imaging device 112 is provided proximate to the robotic system 114 and the patient bed 208 at step 612, method 600 proceeds to step 616, where a mapping of a working volume of the surgical environment by the robotic imaging device 112 is generated. After the mapping of a working volume of the surgical environment by the robotic imaging device 112 is generated at step 616, method 600 proceeds to step 620, where coordinates determined for the robotic imaging device 112 from the mapping of the working volume are aligned with coordinates for the robotic system 114 or the patient bed 208.

[0153] The system may use one or more registration algorithms (e.g., registration algorithms 128) to register the robotic imaging device 112 or the patient bed 208 to the robotic system 114. The registration of the robotic imaging device 112 or the patient bed 208 to the robotic system 114 may allow the system to determine relative movements of the robotic arm 116 and / or one ormore objects within a common coordinate system, allowing the system to identify pose(s) of the robotic arm 116 and / or one or more objects to, for example, prevent collisions when the movement and / or location of the one or more objects (e.g., if the object moves from a first pose at a first time to a second pose at a second time, the system can identify coordinates associated with the object at the first time and / or coordinates associated with the object at the second time, and prevent the robotic arm 116 from occupying or passing through those coordinates to facilitate collision avoidance). The registration algorithm may take one or more coordinate points of the robotic arm 116 in the robotic system coordinate system 330 and one or more coordinate points of the robotic imaging device 112 in the robotic imaging device coordinate system 340 as input values, and may output the coordinate points of the robotic arm 116 in the robotic imaging device coordinate system 340. In some embodiments of the present disclosure, the registration algorithm may output many sets of coordinates of the robotic arm 116, the robotic imaging device 112, the patient bed 208 and the depth sensor 204 into a single common coordinate system.

[0154] After the coordinates determined for the robotic imaging device 112 from the mapping of the working volume are aligned with the coordinates for the robotic system 114 or the patient bed 208 at step 620, method 600 proceeds to step 624 where a navigation path for the robotic imaging device 112 is generated based on the aligned coordinates for the robotic imaging device 112 with the coordinates for the robotic system 114 or the patient bed 208. The navigation system 118 may cause the robotic imaging device 112 or the robotic system 114 to move along the navigation path such that the robotic arm 116 or the robotic imaging device 112 avoids high traffic areas (e.g., a high traffic zone 224c) and / or no-fly zones 230c. After the navigation path for the robotic imaging device 112 is generated based on the aligned coordinates for the robotic imaging device 112 with the coordinates for the robotic system 114 or the patient bed 208 at step 624, method 600 may end with the END operation at step 628.

[0155] The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the disclosure are grouped together in one or more aspects, embodiments, and / or configurations for the purpose of streamlining the disclosure. The features of the aspects, embodiments, and / or configurations of the disclosure may be combined in alternate aspects, embodiments, and / or configurations other than those discussed above. This method of disclosure is not to be interpreted as reflecting anintention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, embodiment, and / or configuration. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

[0156] Moreover, though the foregoing has included description of one or more aspects, embodiments, and / or configurations and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, embodiments, and / or configurations to the extent permitted, including alternate, interchangeable and / or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and / or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

[0157] Example 1. A system, comprising: a robotic arm mounted to a patient bed at a known location in a surgical environment; an imaging device disposed proximate to the robotic arm and the patient bed in the surgical environment; one or more processors; and a memory storing data thereon that, when processed by the one or more processors, causes the one or more processors to: receive first information about a pose of the robotic arm; receive second information about the patient bed relative to the robotic arm; determine, based on the first information and the second information, a pose of the imaging device relative to the robotic arm or relative to the patient bed; and generate, based on the determined pose of the imaging device relative to the robotic arm or relative to the patient bed, a navigation path for the imaging device.

[0158] Example 2. The system of example 1, wherein the robotic arm includes one or more sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

[0159] Example 3. The system of example 1, wherein the data further causes the one or more processors to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

[0160] Example 4. The system of example 3, wherein the three-dimensional volume collision avoidance mapping includes at least one sub-volume to be avoided by the robotic arm.

[0161] Example 5. The system of example 4, wherein the at least one sub-volume to be avoided by the robotic arm is determined based on a predicted motion of a patient or a surgeon.

[0162] Example 6. The system of example 4, wherein the at least one sub-volume to be avoided by the robotic arm includes a high probability zone that is defined based on a predicted motion of a patient or a surgeon.

[0163] Example 7. The system of example 1, wherein the data further causes the one or more processors are caused to: receive third information about a pose of the imaging device; receive fourth information about the imaging device relative to the patient bed; determine, based on the third information and the fourth information, a pose of the robotic arm relative to the imaging device or relative to the patient bed; and generate, based on the determined pose of the robotic arm relative to the imaging device or relative to the patient bed, a three-dimensional navigation path for the robotic arm.

[0164] Example 8. A system, comprising: one or more processors; and a memory storing data thereon that, when processed by the one or more processors, causes the one or more processors to: receive first information about a patient bed; receive second information about a pose of a robotic arm mounted to the patient bed at an unknown location; receive third information about the patient bed relative to the robotic arm; determine, based on the first information, the second information and the third information, a pose of an imaging device relative to the robotic arm or relative to the patient bed; and generate, based on the determined pose of the imaging device relative to the robotic arm or relative to the patient bed, a navigation path for the imaging device.

[0165] Example 9. The system of example 8, wherein the first information is registered information about the patient bed.

[0166] Example 10. The system of example 8, wherein the first information is received from sensor information about the patient bed.

[0167] Example 11. The system of example 8, wherein the robotic arm includes sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

[0168] Example 12. The system of example 8, wherein the one or more processors are caused to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

[0169] Example 13. The system of example 12, wherein the three-dimensional volume collision avoidance mapping includes at least one sub-volume to be avoided by the robotic arm.

[0170] Example 14. The system of example 13, wherein the at least one sub-volume to be avoided by the robotic arm is determined based on a predicted motion of a patient or a surgeon.

[0171] Example 15. The system of example 13, wherein the at least one sub-volume to be avoided by the robotic arm includes a high probability zone that is defined based on a predicted motion of a patient or a surgeon.

[0172] Example 16. The system of example 8, wherein the data further causes the one or more processors to: receive fourth information about a pose of the imaging device; receive fifth information about a pose of the imaging device relative to the patient bed; determine, based on the fourth information and the fifth information, a pose of the robotic arm relative to the imaging device or relative to the patient bed; and generate, based on the determined pose of the robotic arm relative to the imaging device or relative to the patient bed, a three-dimensional navigation path for the robotic arm.

[0173] Example 17. A system, comprising: a robotic arm in proximity to a patient bed in a surgical environment; an imaging device disposed proximate to the robotic arm and the patient bed in the surgical environment; one or more processors; and a memory storing data thereon that, when processed by the one or more processors, causes the one or more processors to:generate a mapping of a working volume of the surgical environment; align coordinates determined for the imaging device from the three-dimensional mapping of the working volume of the surgical environment with coordinates for the robotic arm or coordinates for the patient bed; and generate, based on the aligned coordinates for the imaging device with the coordinates for the robotic arm or the patient bed, a three-dimensional navigation path for the imaging device.

[0174] Example 18. The system of example 17, wherein the three-dimensional mapping is generated based on sensor input received from at least one of an imaging sensor and a depth sensor.

[0175] Example 19. The system of example 17, wherein the robotic arm includes sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

[0176] Example 20. The system of example 17, wherein the one or more processors are caused to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

Claims

CLAIMSWhat is claimed is:

1. A system, comprising: a robotic arm (116) mounted to a patient bed (208) at a known location in a surgical environment; an imaging device (112) disposed proximate to the robotic arm and the patient bed in the surgical environment; one or more processors (104); and a memory (106) storing data thereon that, when processed by the one or more processors, causes the one or more processors to: receive first information about a pose of the robotic arm; receive second information about the patient bed relative to the robotic arm; determine, based on the first information and the second information, a pose of the imaging device relative to the robotic arm or relative to the patient bed; and generate, based on the determined pose of the imaging device relative to the robotic arm or relative to the patient bed, a navigation path for the imaging device.

2. The system of claim 1, wherein the robotic arm includes one or more sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

3. The system of claims 1 or 2, wherein the data further causes the one or more processors to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

4. The system of claim 3, wherein the three-dimensional volume collision avoidance mapping includes at least one sub-volume to be avoided by the robotic arm.

5. The system of claim 4, wherein the at least one sub- volume to be avoided by the robotic arm is determined based on a predicted motion of a patient or a surgeon.

6. The system of claim 4, wherein the at least one sub- volume to be avoided by the robotic arm includes a high probability zone that is defined based on a predicted motion of a patient or a surgeon.

7. The system of any of the preceding, wherein the data further causes the one or more processors are caused to: receive third information about a pose of the imaging device; receive fourth information about the imaging device relative to the patient bed; determine, based on the third information and the fourth information, a pose of the robotic arm relative to the imaging device or relative to the patient bed; and generate, based on the determined pose of the robotic arm relative to the imaging device or relative to the patient bed, a three-dimensional navigation path for the robotic arm.

8. A system, comprising: one or more processors (104); and a memory (106) storing data thereon that, when processed by the one or more processors, causes the one or more processors to: receive first information about a patient bed (208); receive second information about a pose of a robotic arm (116) mounted to the patient bed at an unknown location; receive third information about the patient bed relative to the robotic arm; determine, based on the first information, the second information and the third information, a pose of an imaging device (112) relative to the robotic arm or relative to the patient bed; and generate, based on the determined pose of the imaging device relative to the robotic arm or relative to the patient bed, a navigation path for the imaging device.

9. The system of claim 8, wherein the first information is registered information about the patient bed.

10. The system of claims 7 or 8, wherein the first information is received from sensor information about the patient bed.

11. The system of any of the preceding, wherein the robotic arm includes sensors at its joints to determine a spatial relationship of the robotic arm with respect to the patient bed.

12. The system of any of the preceding, wherein the one or more processors are caused to determine a three-dimensional volume collision avoidance mapping of the robotic arm in the surgical environment.

13. The system of claim 12, wherein the three-dimensional volume collision avoidance mapping includes at least one sub-volume to be avoided by the robotic arm.

14. The system of claim 13, wherein the at least one sub-volume to be avoided by the robotic arm is determined based on a predicted motion of a patient or a surgeon.

15. The system of any of the preceding, wherein the data further causes the one or more processors to: receive fourth information about a pose of the imaging device; receive fifth information about a pose of the imaging device relative to the patient bed; determine, based on the fourth information and the fifth information, a pose of the robotic arm relative to the imaging device or relative to the patient bed; and generate, based on the determined pose of the robotic arm relative to the imaging device or relative to the patient bed, a three-dimensional navigation path for the robotic arm.

Images