251 results about "Robot manipulator" patented technology

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

A method of programming an industrial robot (100), composed of a robot manipulator (110) and a robot controller (120), comprises: recording movements of the robot manipulator during a kinesthetic programming session, for thereby obtaining a robot trajectory; capturing speech data while recording the movements of the robot manipulator; decomposing the recorded speech data into a plurality of phases; for each phase of the speech data, parsing the speech data into robot parameter values using a natural-language model (170), identifying a contemporaneous phase of the robot trajectory and annotating it with the robot parameter values; and, on the basis of the annotated robot trajectory, generating a robot program executable by the robot controller.

A robotic system includes a robotic arm that interacts with an object during operation. It further includes a locking component that, during operation, locks a multi-position component in an operating position. Responsive to a triggering event, locking of the multi-position component in the operating position is released and the multi-position component moves away from its operating position, facilitating separation between the robotic arm and the object.

A method of programming an industrial robot (100), with a robot manipulator (110) and robot controller (120), comprises: recording movements of the robot manipulator during a demonstration-based robot programming session, for thereby obtaining a robot trajectory; acquiring a video of the robot programming session; and generating, on the basis of the robot trajectory and the video, a robot program (C) executable by the robot controller, wherein the robot program includes at least one motion command which is expressed in a first reference frame (0 1). The method further comprises capturing operator input data indicating a first object (151) in an image in the video. The generated robot program includes a command to identify a position of an object resembling the first object in a work area (150) of a robot which executes the robot program; and the first reference frame is defined with respect to the identified position of the object resembling the first object.

The invention relates to the technical field of man-machine interaction, and provides a robot mechanical arm collision avoidance planning method and system, a medium and equipment, and the method comprises the steps: obtaining posture features through the processing of a feature coding layer and an encoder for the observed posture features of a human body, and carrying out the inverse discrete cosine transform after the posture features are processed through a decoder and a feature decoding layer, obtaining human body posture features at future moments; for observed human body track features, track features are obtained through processing of a feature encoding layer and an encoder, feature fusion is carried out on the track features and posture features through a multi-head attention layer, after the fused track features are processed through a decoder and a feature decoding layer, inverse discrete cosine transform is carried out, and human body track features at the future moment are obtained; calculating the human body posture features at the future moment into a human body model, and performing point cloudization on the human body model to obtain predicted dynamic human body point cloud; and obtaining an object point cloud, taking the object point cloud and the dynamic human body point cloud as an obstacle, and performing collision avoidance trajectory planning.

Systems and methods are disclosed to automatically load packages into containers. In one embodiment, an example system may include a robotic platform configured to elevate and rotate a container to a predetermined orientation, and a robotic manipulator configured to retrieve a first package from a surface and to position the first package inside the container at a predetermined position.

Techniques for controlling an alignment of a non-destructive test (NDT) inspection probe are described. The techniques include receiving laser profile data from a laser profiling sensor corresponding to a weld location and surface properties of an object under non-destructive testing and receiving image data from an image sensor corresponding to the weld location. The laser profile data and the image sensor may be fused to generate fused data corresponding to the weld location. A control signal is generated to adjust a robotic manipulator of the NDT probe based on the fused data.

A computer-implemented method, apparatus and system is provided for robotic task completion from natural language requests. The method may include: receiving a natural language command, processing the natural language command with a generative large language model to extract an intent and associated context, creating a three-dimensional (3D) open-vocabulary semantic scene graph of the environment, associating the scene graph with the intent and associated context, creating, based at least in part on the scene graph, an execution plan comprising a sequence of actions to complete the natural language command, and generating executable code or tool calls corresponding to one or more actions in the sequence of actions, and controlling one or more robotic manipulators and / or actuators to perform one or more actions of the sequence of actions based on the execution plan.

A method of programming an industrial robot, comprising: recording movements of a robot manipulator during a demonstration-based programming session, to obtain a robot trajectory (301); using a natural-language model (171), parsing speech data describing the programming session into at least one robot parameter (305) value and annotating the robot trajectory with the robot parameter value; and, on the basis of the annotated robot trajectory (306), generating a robot program (307). The method further comprises: capturing a video (302) of the programming session; using a vision-enabled language model, VLM (172), generating descriptions (303) of movements of the robot manipulator which are visible in the video; and generating said speech data (304) on the basis of the descriptions of the robot manipulator movements.

A method for controlling a robotic manipulator according to a task comprises accepting a feedback signal including a sequence of multi-modal observations of a state of execution of the task. The multi-modal observations are processed with a neural network having a self-attention module with a hierarchically conditioned output to produce a skill of the robotic manipulator and an action conditioned on the skill. The neural network is trained in a supervised manner with demonstration data to produce a sequence of skills and a corresponding sequence of actions for the actuators of the robotic manipulator to perform the task. The method further comprises determining one or more control commands for the one or more actuators based on the produced action and submitting the one or more control commands to the one or more actuators causing a change of the state of execution of the task.

A container transport can move containers around a warehouse environment. The container transport can include a support that can connect the container transport with a robotic manipulator. The support can rotate the container transport between a storage position and a carrying position. In the carrying position the container can receive a container in a container receiving area. A manipulator can move the container into and / or out of container receiving area.

A robot manipulator has a base; an arm connected to the base via at least a shoulder joint; and a control system with a dynamic stability determination module. The dynamic stability determination module of the control system determines whether a dynamic stability criterion is met. If the dynamic stability criterion is met, the arm is responds compliantly to an applied force; and if the dynamic stability criterion is not met, the control system causes motion of the base in the direction of the applied force.

This invention provides a system and method for reliably interconnecting a native gladhand on a trailer with an AV truck, in which the gladhand can vary in placement and type on the trailer front. A visually guided robot manipulator arm accesses the trailer front. The arm includes an end effector assembly that is arranged to visually navigate to the gladhand and engage the gladhand by latching onto a gladhand using a connection tool that includes a hinged gladhand / wedge capture assembly and a pressurized clamping connection plate that selectively engages the trailer gladhand seal. The capture assembly is arranged to accommodate different gladhand geometries in latching thereonto. The connection tool includes a hinge that is spring-loaded in both a locked and unlocked bistable state, and sensors monitor the state. The system can include passive or active compliance elements that accommodate moderate misalignment between the engaged gladhand and the robotic arm / connection tool.

The present invention belongs to the field of robotics technology, specifically a constant force grinding method for complex surfaces of a robot based on force feedback. The method comprises the following steps: Step 1, collecting information of a six-dimensional force sensor through a robot controller; Step 2, introducing a fastest differential tracker in the self-disturbance rejection control; Step 3, forming an admittance control model by the robot and the environment to realize the outer loop control of the robot manipulator; Step 4, constructing a non-singular terminal sliding film control law to realize the inner loop control of the robot manipulator, thereby realizing the grinding of the curved workpiece surface. The present invention can improve the force tracking effect of the manipulator in a complex and unknown environment, thereby greatly improving the robot grinding accuracy.

The invention relates to a composite robot mechanical arm motion path planning method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining real-time chassis positioning data of a moving object, obtaining chassis positioning error data and initial moving path data of a mechanical arm of the moving object under the condition that it is detected that the real-time chassis positioning data has errors relative to preset target chassis positioning data, and determining the moving object according to the chassis positioning error data. And determining motion path compensation data, updating the initial motion path data according to the motion path compensation data to obtain target motion path data of the mechanical arm, and controlling the mechanical arm to move according to a path generated by the target motion path data. By adopting the method, the precision of path planning of the mechanical arm can be improved.

A teleoperation system, including a robotic controller configured to translate a plurality of user inputs to a plurality of robotic control signals. A robotic manipulator, configured to operate separately from the robotic controller and to receive robotic control signals from the robotic controller, the robotic manipulator comprising a plurality of first joints and an end effector, wherein the robotic manipulator is configured to assume a plurality of poses, and wherein each respective pose of the plurality of poses corresponds to a respective configuration of the plurality of first joints. A feedback system, operatively coupled to the robotic manipulator, the feedback system configured to generate feedback information and transmit the feedback information to the robotic controller, wherein the feedback information includes information that indicates a current pose of the robotic manipulator, including the respective configuration of the plurality of first joints corresponding to the current pose of the robotic manipulator.

Described herein are sample collectors configured for use with robotic medical systems. A robotic medical system can include a medical instrument that can be inserted into a patient to capture a sample. A robotic manipulator can be engaged with the medical instrument and configured to operate the medical instrument; a sterile barrier can be used to separate a sterile field containing the medical instrument from a non-sterile field containing the robotic manipulator; a sample collector can include a receiver portion positioned in the sterile field and configured to receive the sample when a distal end of the medical instrument is retracted from the patient; and a connector coupled to the receiver portion and configured to attach to the robotic medical system. The robotic system can place the sample in the receiver portion.

A method of controlling a robot manipulator for operating in a shared workspace with human(s) is provided. The method includes: determining a safety control set with respect to a safety condition between selected part(s) of the robot manipulator and selected part(s) of the human(s) using a safety control function: determining a hard constraint control set with respect to hard constraint(s) in trajectory tracking in DOF(s) of a component of the robot manipulator for a task using a hard constraint function: determining a soft constraint control set with respect to soft constraint(s) in trajectory tracking in DOF(s) of the component for the task using a soft constraint function: and performing control input optimization based on the safety control set. the hard constraint control set and the soft constraint control set to determine a control input for controlling the robot manipulator. In particular, the safety control function is configured to determine the safety control set: based on a control model for the robot manipulator that is configured in a control affine form based on a control mode of the robot manipulator, and for each part pair of part pair(s) of a selected part of the selected part(s) of the robot manipulator and a selected part of the selected part(s) of the human(s): based on a safety distance function associated with the part pair which corresponds to a control barrier function, and based on a sliding manifold associated with the part pair configured based on the safety distance function and a relative degree of the safety distance function with respect to the control input to the robot manipulator.

The invention discloses a track detection device for an industrial robot. Comprising a workbench, a fixing rod is movably installed at the top of the workbench, a fourth connecting rod and a first connecting rod are movably installed at the top of the fixing rod, and first moving rods are movably installed at the two ends of the fourth connecting rod. According to the device, the first auxiliary rods and the movable blocks are arranged, when the robot manipulator moves to the position area among the four first auxiliary rods, the robot moves according to the preset advancing track, and the first auxiliary rods and the movable blocks can keep synchronous movement with the manipulator; the background control system compares data detected by the laser distance measuring sensor in the operation process with a standard data value, if the data value measured by the laser distance measuring sensor exceeds a preset data value in the equipment operation process, it is indicated that the robot manipulator deviates during operation, and if the data value measured by the laser distance measuring sensor exceeds the preset data value, the robot manipulator does not deviate during operation. Through the operation mode, detection of the moving track of the robot can be completed.

The invention provides a multi-modal flow matching-based motion prediction method and device for a body-equipped robot, and relates to the technical field of intelligent robots, and the method comprises the steps: obtaining an instruction text, an image feature set collected by a robot, and a depth image set corresponding to the image features of all moments and positions; performing feature splicing processing and feature refining processing on the image feature set to obtain an image sequence feature set, and performing feature fusion processing on the depth image set based on the image sequence feature set to determine a target visual feature; text features in the instruction text are fused with target visual features, text visual modal feature information is determined, and based on the text visual modal feature information, motion of the robot mechanical arm is predicted, and motion pose prediction features are determined. According to the method, the accuracy of motion prediction of the body-equipped robot can be remarkably improved.

Provided is a robot control device capable of facilitating the work of setting a control center for controlling the operation of a robot. The robot control device controls a robot manipulator which is equipped with an end effector. The robot control device includes: an image processing unit that, by using a feature extraction model for detecting images of the robot manipulator, and position / posture information of the robot manipulator, detects, from images (M1, M2) in which at least part of the robot manipulator is captured, a position in a three-dimensional space which corresponds to designated positions (P1, P2) designated on the images, as a position relative to the robot manipulator; and a coordinate system determination unit that sets a control center, for controlling the operation of the robot manipulator, to the position detected in the three-dimensional space.

A feedback controller for controlling a robotic manipulator to perform a task is provided. The feedback controller comprises a memory configured to store a hierarchical reinforcement learning (HRL) neural network including (i) nominal control policy, and (ii) recovery control policy. The recovery control policy is trained based on a recovery policy reward to select a switch-to-nominal action to transfer control to the nominal control policy to perform the task. The recovery policy reward is dependent on a nominal policy reward for training the nominal control policy. The feedback controller further comprises a processor configured to iteratively execute the HRL neural network to select at least one of a nominal action based on the nominal control policy, and a recovery action based on the recovery control policy and to control the robotic manipulator to perform the task based on the selected action.

The invention provides a robot mechanical arm cooperative control method based on imitation learning. The method comprises the steps that a robot historical state data set is constructed, feature extraction is conducted on the robot historical state data set through an encoder structure, and potential feature representation is obtained; acquiring a color image and a depth image of a target operated by a to-be-predicted robot through a binocular camera, and performing feature extraction processing on the color image and the depth image through a mode classification module and a visual feature extraction network to obtain mode feature and visual feature vectors; and inputting the joint state information, the potential characterization, the operation mode features and the fused visual features into a decoder to obtain an action block prediction result, and performing weighted averaging on action blocks at a plurality of continuous moments based on a time sequence integrated weighting mechanism to obtain a continuous control track of the robot mechanical arm. According to the method, the problems of discontinuous multi-stage strategies and error accumulation in a traditional control algorithm are solved, and two-arm cooperative control with smooth action and stable task is realized.

Controlling a Robotic Manipulator for Packing an Object Provided is a computer-implemented method of controlling a robotic manipulator for packing an object, involving obtaining an image of an object grasped by an end effector of the robotic manipulator and determining a major axis of the object in the image. A first object pose is determined wherein the major axis of the object is aligned with an axis of a receiving space. The robotic manipulator is controlled to manipulate the object to the first object pose above the receiving space and move the object from the first object pose down into the receiving space. In response to detecting by a force sensor a contact force above a predetermined force threshold at the end effector, the robotic manipulator is controlled to manipulate the object to a second object pose, above the receiving space, for initiating a further attempt to place the object in the receiving space.

The innovation of the robotic compliant actuator derives from the utilization of the series elastic actuation principle, coupled with the fundamentals in compliant mechanics. This actuator constitutes a dynamic and adaptable actuation framework wherein an elastic component is sequentially integrated with a motor and gearbox, resulting in compelling force-regulating attributes. This actuation concept has been specifically customized for robotic systems, particularly robot manipulators and legged robots, fundamentally transforming the manner in which robots engage with their surroundings, executing tasks demanding precision, adaptability, and safety. This compliant actuator introduces an ingeniously designed mechanical spring that assimilates a compliant mechanism within its elastic component, thereby endowing itself with additional benefits inherent to compliant mechanics. Additionally, another layer of compliant mechanism serves as an amplification means to detect spring displacement. This augmentation aptly exemplifies human-like force-regulation behavior, particularly in the realm of robotic applications.

The invention discloses a parallel telescopic clamping jaw using a belt as an active surface, and belongs to the technical field of robot manipulators. The parallel telescopic clamping jaw with the belts as the driving surface comprises a base, the upper end of the base is provided with two installation frames capable of being horizontally adjusted in a sliding mode, the two installation frames are each provided with a finger capable of being adjusted in a lifting mode, the two fingers are each provided with a belt, and each belt is wound around a plurality of driven winding drums and a power winding drum; a rotary motion steering engine is arranged on the installation frame to drive the power winding drum to rotate so as to drive the belt to operate, and the object can be clamped and driven to move and rotate in the horizontal direction and the vertical direction at the same time.

The design relates to photographic robotic arm for capturing videos or images of products, people, or scenes from different angles, and may be configured as a robotic manipulator; the design is characterized by the ornamental shape and external configuration of the photographic robotic arm.

A controller for controlling robotic manipulator according to a task is provided. The controller is to collect data relating to a state and an object property of an object, and execute a state adapter model to produce a state correction to state of the object having the object property different from a unitary property of a unitary object. The controller is to execute a control policy using the state correction to produce an action for the unitary object, and execute an action adapter model to produce an action correction to the action produced by the control policy. The state correction and action correction are produced based on difference between object property and unitary property. The control policy is to map a state of the unitary object to the action of the robotic manipulator to manipulate the unitary object according to the task.

A robotic system includes an autonomous order picker lift truck configured to move within a commercial environment that includes commercial products supported by donor pallets; a robotic manipulator assembly positioned on a platform of the autonomous order picker lift truck; and a control system communicably coupled to the robotic manipulator assembly and the autonomous order picker lift truck. The control system is configured to perform operations including commanding the autonomous order picker lift truck to move to a particular donor pallet or shelf that is mounted on a rack assembly at or above a support surface of the commercial environment; commanding the autonomous order picker lift truck to move the platform to the particular donor pallet or shelf; and commanding the robotic manipulator assembly to pick a commercial product or a case from the particular donor pallet or shelf and place it on an order pallet or a receiving pallet.

A robotic system includes a robotic manipulator configured to manipulate an endoscope having a camera associated therewith and control circuitry configured communicatively coupled to the robotic manipulator. The control circuitry can be configured to receive an image depicting a field-of-view (FOV) of the camera associated with the instrument, detect an anatomical feature in the image, display a graphical interface that includes the image and a visual overlay indicating a location of the anatomical feature in the image, and track the anatomical feature based at least in part on determining that the anatomical feature is a target anatomical feature.