Systems and methods for grasping containers in diagnostic laboratory systems
Patent Information
- Authority / Receiving Office
- EP · EP
- Patent Type
- Applications
- Current Assignee / Owner
- SIEMENS HEALTHCARE DIAGNOSTICS INC
- Filing Date
- 2024-07-22
- Publication Date
- 2026-07-08
Smart Images
Figure US2024038940_06032025_PF_FP_ABST
Abstract
Description
SYSTEMS AND METHODS FOR GRASPING CONTAINERS IN DIAGNOSTIC LABORATORY SYSTEMSCROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims benefit under 35 USC § 119(e) of US Provisional Application No. 63 / 579,292, filed August 28, 2023. The entire contents of the abovereferenced patent application(s) are hereby expressly incorporated herein by reference.FIELD
[0002] This disclosure relates to systems and methods for grasping containers in diagnostic laboratory systems.BACKGROUND
[0003] Diagnostic laboratory systems conduct clinical chemistry tests that identify analytes or other constituents in biological samples or specimens such as blood serum, blood plasma, urine, interstitial liquid, cerebrospinal liquids, and the like. The biological samples are collected in sample containers, such as sample tubes, and transported to a laboratory system that may be located in a laboratory. After the sample containers are received at the laboratory system, the sample containers are loaded into one or more sample container carriers (e.g., tube carriers or tube racks). The sample container carriers are then loaded into a sample handler of the laboratory system. The sample handler is an input / output module of a laboratory system that enables the laboratory system to receive and discharge sample containers. Robots may transfer the sample containers between various locations and components within the laboratory system.
[0004] The sample containers may have barcode labels or other indicia that contain patient information and that should not be damaged during testing. The locations of the barcode labels differ from one sample container to another sample container depending on where the barcode label is placed. If the robots used to transfer the sample containers use the same grasping locations with reference to the bottoms of the sample containers, the grasping may contact and damage the barcode labels. The sample containers may also have anomalies that should not be contacted by the grippers orend effectors of the robots. Accordingly, laboratory systems having robots that are able to grasp sample containers while avoiding indicia and anomalies are sought.SUMMARY
[0005] According to a first aspect, a method of grasping a container in a diagnostic laboratory system using a robot is provided. The method includes capturing one or more images of a container in a diagnostic laboratory system, wherein each captured image includes image data; analyzing the image data to identify one or more surface properties of the container; determining a grasping location on the container for a gripper of a robot in the diagnostic laboratory system to grasp in response to the analyzing; and grasping the container at the grasping location using the gripper.
[0006] In another aspect, a diagnostic laboratory system is provided. The diagnostic laboratory system includes an imaging device configured to capture an image of a container; a robot comprising a gripper configured to grasp the container and move the container within the diagnostic laboratory system. In addition, the diagnostic laboratory system includes a computer configured to: receive image data generated by the imaging device; analyze the image data to identify one or more surface properties of the container; determine a grasping location on the container for the gripper in response to the analyzing; and generate instructions to direct the gripper to grasp the container at the grasping location.
[0007] In a further aspect, a method of grasping a sample container in a diagnostic laboratory system using a robot is provided. The method includes: capturing one or more images of a sample container in a diagnostic laboratory system, wherein each captured image includes image data, and wherein the sample container is configured to hold a biological sample; analyzing the image data to identify one or more surface properties of the sample container, the one or more surface properties including at least one of an indicia, a liquid, and an anomaly on the surface of the sample container; determining a grasping location on the sample container for a gripper of a robot in the diagnostic laboratory system to grasp that avoids the one or more surface properties; and grasping the sample container at the grasping location using the gripper.
[0008] Still other aspects, features, and advantages of this disclosure may be readily apparent from the following description and illustration of a number of example embodiments, including the best mode contemplated for carrying out the disclosure. This disclosure may also be capable of other and different embodiments, and its several details may be modified in various respects, all without departing from the scope of the disclosure.BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The drawings described below are provided for illustrative purposes and are not necessarily drawn to scale. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive. The drawings are not intended to limit the scope of the disclosure in any way.
[0010] FIG. 1 illustrates a perspective view of a diagnostic laboratory system located in a laboratory according to one or more embodiments.
[0011] FIG. 2 illustrates a detailed view of the computer of FIG. 1 in communication with a sample handler of the diagnostic laboratory system according to one or more embodiments.
[0012] FIG. 3 illustrates a robot and a sample container carrier located in a sample handler of a diagnostic laboratory system according to one or more embodiments.
[0013] FIG. 4A illustrates a top perspective view of nodes of a robot gripper undesirably grasping a sample container on a barcode label according to one or more embodiments.
[0014] FIG. 4B illustrates a top plan view of the sample container of FIG. 4A showing different locations where the nodes may contact the surface of the sample container according to one or more embodiments.
[0015] FIG. 40 illustrates the robot gripper of FIG. 4A grasping the sample container in a higher vertical position than shown in FIG. 4A according to one or more embodiments.
[0016] FIG. 4D illustrates the robot gripper of FIG. 4A grasping a sample container having a different pose that the sample container shown in FIG. 4A according to one or more embodiments.
[0017] FIG. 4E illustrates a plan view of a sample container being grasped by three nodes of a robot gripper according to one or more embodiments.
[0018] FIGS. 5A-5F illustrate elevation views of sample containers that have different surface properties according to one or more embodiments.
[0019] FIG. 6A-6F illustrate examples of different sample containers along with optimal grasping locations and conventional grasping locations of a robot gripper according to one or more embodiments.
[0020] FIG. 7 illustrates an elevation view of a first sample container and a second sample container located or being moved on a track of a diagnostic laboratory system according to one or more embodiments.
[0021] FIG. 8 illustrates a flowchart of a method of grasping sample containers according to one or more embodiments.
[0022] FIG. 9 illustrates a top plan view of a track of a diagnostic laboratory system with a sample container and a sample mover positioned at a transfer location according to one or more embodiments.
[0023] FIG. 10 illustrates a flowchart of a method of grasping a container in a diagnostic laboratory system using a robot according to one or more embodiments.
[0024] FIG. 11 illustrates a flowchart of a method of grasping a sample container in a diagnostic laboratory system using a robot according to one or more embodiments.DETAILED DESCRIPTION
[0025] Independent of the grammatical term usage, individuals with male, female or other gender identities are included within the term.
[0026] Automated diagnostic laboratory systems perform analyses (e.g., tests) on various biological samples, such as blood, blood serum, urine, and other bodily fluids.The samples are collected from patients and placed into sample containers, such as sample tubes. The sample containers along with testing instructions are then sent to a laboratory. The testing instructions may indicate which tests are to be performed on the samples by instruments located in the laboratory. A technician or software executing on a computer may determine which instruments in the laboratory are to perform each test on each of the samples per the instructions.
[0027] The components in automated diagnostic laboratory systems can be broadly characterized as a sample transport system, sample movers, and instruments. The sample transport system may include hardware, such as a track, that is configured to move the sample movers throughout the laboratory systems. The sample movers may house containers, such as sample containers, present on the sample transport system. The instruments may be modules and / or analyzers that the sample movers may be directed to, wherein processes and analyses may be performed by the instruments. Examples of the instruments include centrifuges, chemistry analyzers, decappers, storage modules, and refrigeration modules.
[0028] Typical workflow in a laboratory system may include loading a sample container onto a sample mover and then instructing the sample transport system to transport the sample mover to one or more of the instruments. Many laboratory systems use robot systems to move the sample containers to various locations, such as locations within the instruments and to and from the transport system. The robot systems may use robot grippers (e.g., end effectors) to grasp and move the sample containers. In addition to sample containers, the robot systems may move quality control packs, calibrator packs, and other items throughout the laboratory systems.
[0029] The robot systems may use grasping rules including a plurality of instructions to operate and move the grippers. Given all the different items that may be grasped by the robot systems, it may not be advantageous to use the same grasping rules for every item that is grasped by the robot systems. For example, different item geometries and surface properties of the items may make use of the same grasping rules inefficient. Furthermore, should the gripper grasp all containers using the same grasping rules,there is a risk that short containers may be grasped at unstable locations, such as to close to their tops, which may cause breaks or spillage of the container contents.
[0030] Some containers may have indicia, such as barcode labels attached to the exterior surfaces of the containers. The indicia may include or reference patient information or testing criteria, for example. These barcode labels may need to be kept intact as the containers are moved throughout the laboratory system. The locations of the indicia may vary between individual containers. Thus, if the same grasping rules and locations are used for all the containers, the grippers may damage the indicia. For example, the grippers may grasp the indicia, which may scratch off or mar some of the indicia.
[0031] In some situations, the exteriors of some containers may have exposed adhesives, such as from indicia labels, and / or liquids spilled onto the exterior surfaces. These adhesives and / or liquids may hinder grasping actions of the robot gripper should the robot gripper grasp these regions. For example, the adhesives may cause the robot gripper to adhere to the container, and the liquids may cause the robot gripper to slide relative to the container.
[0032] The methods and apparatus described herein improves the gripping capabilities of automated diagnostic systems by identifying grasping locations on containers where the robot grippers may grasp the containers with minimal effect on the containers or exterior surfaces of the containers. The identified locations may be referred to as optimal grasp locations of the containers. The methods may consider the degrees-of-freedom of the robot gripper to provide the best possible way (e.g., location) to grasp the containers. The optimal grasp locations may enable safer and reliable grasps while abating potential damage caused to the containers and the system through container breaks and spillage. In addition, grasping rules, such as grasping forces applied by the grippers, may take into account the surface properties of the containers.
[0033] The methods may capture one or more images of a container, wherein each captured image includes image data. The image data may be analyzed to identify one or more surface and / or geometric properties of the container. The geometric / surface properties may include the height of the container, the material from which the containeris made, indicia located on the exterior of the container, surface anomalies, and liquids on the exterior of the container. Based on the analyses, a grasping effect and / or location of a robot gripper grasping the container may be determined. The grasping effects may avoid contacting indicia or a liquid, for example. A grasping location for the robot gripper on the container may be determined in response to the grasping effect. The grasping location may be a location where the gripper will not adversely affect the container or be adversely affected. The grasping location also may be a location that enables the gripper to grasp the container without sliding or sticking to the container. The grasping location also may be a location that is less likely for the robot gripper to break or otherwise damage the container. These and other systems, methods, and devices that determine grasping effects of a robot gripper used in laboratory systems are described in greater detail below in connection with FIGS. 1-11.
[0034] Reference is made to FIG. 1 , which is a perspective view of a laboratory 100. A diagnostic laboratory system 102 may be located within the laboratory 100. The laboratory system 102 may be configured to perform a plurality of analyses or tests on a plurality of different biological samples. For example, the tests may determine levels of constituents or chemicals present in biological samples, such as blood, urine, cerebral fluid, and other biological samples. In other embodiments, the laboratory system 102 may be configured to perform a plurality of different tests on a single biological sample type, such as blood serum. In other embodiments, the laboratory system 102 may be configured to perform a single type of test on a single biological sample type, such as blood serum.
[0035] The laboratory system 102 may include a plurality of diagnostic instruments 104 (a few labelled) that are configured to perform the same or different tests on the biological samples. In some embodiments, the diagnostic instruments 104 may be interconnected by a transport system (e.g., track 216 - FIG. 2). The transport system may be configured to transport the biological samples between the diagnostic instruments 104 and / or other devices in the laboratory system 102, such as centrifuges and decappers. The configuration of the laboratory system 102 may be different than the configuration shown in FIG. 1. In some embodiments, the laboratory system 102 may only include a single one of the diagnostic instruments 104.
[0036] The laboratory system 102 may be coupled to a computer 120 that may be located within the laboratory 100 or external to the laboratory 100. In some embodiments, portions of the computer 120 may be located within the laboratory 100 and other portions of the computer 120 may be located external to the laboratory 100. The computer 120 may include a processor 122 and a memory 124, wherein the memory 124 stores programs 126 configured to be executed or run on the processor 122. In some embodiments, the memory 124 and / or the programs 126 may be located external to the computer 120. For example, the computer 120 may be connected to the Internet to access external data and the like. The programs 126 may operate the diagnostic instruments 104 and process data generated by the diagnostic instruments 104.
[0037] The memory 124 may be any suitable type of memory, such as, but not limited to one or more of a volatile memory and / or a non-volatile memory. The memory 124 may have a plurality programs 126 consisting of instructions stored therein that, when executed by processor 122, cause the processor 122 to perform various actions specified by one or more of the stored instructions. The program instructions may be provided to the processor 122 to perform operations in accordance with the present system and methods specified in the flowcharts and / or diagram blocks described herein. The processor 122 so configured may become a special purpose machine particularly suited for performing in accordance with the present system and methods. The program instructions may be stored in a computer readable medium, such as the memory 124 that can direct the processor 122 to function in a particular manner. The term "memory" as used herein can refer to both non-transitory and transitory memory.
[0038] At least one of the diagnostic instruments 104 or other components may be a sample handler 130, which is described in greater detail below. In the embodiment of FIG. 1 , the sample handler 130 is a component in the laboratory system 102. In other embodiments, the laboratory system 102 may include a plurality of sample handlers. The operations performed by the sample handler 130 may be implemented in one or more of the diagnostic instruments 104. The sample handler 130 may be located in various locations in the laboratory system 102, such as in individual ones of the diagnostic instruments 104.
[0039] The sample handler 130 serves to receive items into the laboratory system 102 and to disperse items from laboratory system 102. The items may include sample containers (e.g., sample containers 210 - FIG. 2) and reagent packages. The sample containers 210 received into the sample handler 130 may contain biological samples that are to be tested by one or more of the diagnostic instruments 104. The sample containers 210 being dispersed from the laboratory system 102 may contain residual liquids after the biological samples have been tested.
[0040] Additional reference is made to FIG. 2, which illustrates a more detailed embodiment of the computer 120 in communication with the sample handler 130. The computer 120 may include a plurality of programs 126 that may be run on the processor 122. One of the programs 126 may be a robot controller 204 that may be configured to generate instructions that cause a robot 206 to move to specific locations as described herein. For example, the instructions may cause the robot 206 or portions of the robot 206 to move within the sample handler 130 and to perform certain operations. The instructions may also cause the robot 206 to move sample containers 210 between sample container carriers 212 and sample movers 214. The sample movers 214 may move the sample containers 210 between diagnostic instruments 104 (FIG. 1 ) by way of a transport system, which in the embodiment of FIG. 2 may be a track 216 configured to move the sample movers 214.
[0041] An image processor 220 may be coupled to the computer 120 and may be configured to receive image data 222 generated by an imaging device 224. In some embodiments, one or more portions of the image processor 220 may be implemented in the imaging device 224. In some embodiments, the imaging device 224 may be configured to capture three-dimensional images of the sample containers 210, the sample container carriers 212, and other items. The image processor 220 may be configured to send instructions to the imaging device 224 that directs the imaging device to capture images, such as images of the sample containers 210 and other items in the laboratory system 102. The imaging device 224 may be a digital camera or a three-dimensional camera, for example.
[0042] The computer 120 may include a container properties identification algorithm 226 that may be a machine learning algorithm, such as a machine learning trained software model. In some embodiments, the container properties identification algorithm may include a convolutional neural network. In other embodiments, the container properties identification algorithm 226 may include a deep neural network. The container properties identification algorithm 226 may be configured to analyze the image data 222. In some embodiments, the container properties identification algorithm 226 may be configured to analyze image data 222 processed by image processor 220, which may output enhanced, consolidated, or optimized image data. The container properties identification algorithm 226 may identify properties such as dimensions of the sample containers 210, container geometry of the sample containers 210, whether the sample containers 210 have caps, barcodes and other indicia, and other items. The other items may be anomalies such as sample contents that have spilled from the sample containers 210, foreign substances or materials, residual or overflow adhesive used to affix barcode labels to the sample containers 210, damage or markings on barcode labels, and the like.
[0043] The container properties identification algorithm 226 may also determine heights of samples in the sample containers 210 by identifying transitions of color or brightness in images of the sample containers 210. Materials (e.g., glass or plastic) of the sample containers 210 may be determined by analyzing surface light reflected from or transmitted through the sample containers 210. When items other than the sample containers 210, such as reagent packages, are imaged, the container properties identification algorithm 226 may analyze properties of these other items.
[0044] An optimal grasping location algorithm 230 may be a program or machine learning software model configured to identify optimal gripper locations on the sample containers 210 or other items where the robot 206 grips the sample containers 210 or the other items. The optimal grasping location algorithm 230 may be a machine learning algorithm, such as a convolutional neural network or rule-based network, to perform the methods described herein. In some embodiments, the optimal grasping location algorithm 230 may include a deep neural network. Some containers may have several optimal grasping locations and the optimal grasping location algorithm 230 may selectone of these optimal grasping locations. The optimal grasping location algorithm 230 may analyze the data generated by the container properties identification algorithm 226 to identify optimal or proper locations where the robot 206 may grasp individual ones of the sample containers 210. The optimal grasping locations may avoid barcode labels, indicia, anomalies, caps, spilled samples and other liquids, and other items that may interfere with proper grasping of the sample containers 210.
[0045] The computer 120 may be coupled to a workstation 240 that enables users to communicate with the computer 120. The workstation 240 may include a display 242 and a keyboard 244 and / or other input devices. Data generated by the computer 120 may be displayed on the display 242. The user may input data to the computer 120 via the keyboard 244 and / or other input devices. The display 242 may be configured to display images captured by the imaging device 224 and / or other imaging devices.
[0046] The computer 120 and / or the laboratory system 102 may be coupled to a laboratory information system (LIS) 250. In some embodiments, the LIS 250 may be a program and may be executed by the computer 120. The LIS 250 may receive data and / or instructions from a hospital information system (HIS) 252, which may be at least partially implemented in a program executed by the computer 120. Medical professionals may enter testing requirements for specific patients into the HIS 252. For example, a doctor may require that blood taken from a first patient be tested for a first chemical and blood taken from a second patient be tested for a second chemical. These testing requirements may be input to the HIS 252. The testing requirements may then be transmitted to the LIS 250, which generates a testing plan to be run by the laboratory system 102 wherein specific diagnostic instruments 104 (FIG. 1 ) perform the tests.
[0047] The embodiment of the sample handler 130 (FIG. 2) is shown holding three of the sample container carriers 212, which are referred to individually as a first container carrier 212A, a second container carrier 212B, and a third container carrier 212C. The sample container carriers 212 may be of different types, such as from different suppliers or manufacturers. Some of the container slots 232 may be occupied with sample containers 210. The occupied container slots are identified as having dark fill. A sample container 210A is shown occupying a container slot in the third container carrier 212Cand will be referenced in examples herein. The devices and methods described herein may be implemented on sample containers having different configurations and surface properties than the sample container 21 OA.
[0048] Additional reference is made to FIG. 3, which illustrates a front perspective view of an embodiment of the robot 206 grasping the sample container 21 OA. The robot 206 may include a gripper 304 (e.g., an end effector) configured to grasp the sample containers 210 and move the sample containers 210 throughout the sample handler 130, including into and out of the sample container carriers 212 (FIG. 2). In the embodiment of FIG. 3, the robot 206 is illustrated moving the sample container 210A into and out of the third container carrier 212C. The robot 206 may be configured to move the sample containers 210 into and out of all the sample container carriers 212 (FIG. 2).
[0049] The robot 206 may include a plurality of gantries that enable the gripper 304 to move in an x-direction, a y-direction, and a z-direction. First gantries 310 may be configured to move the gripper 304 in the Y-direction. A second gantry 312 may be configured to move the gripper 304 in the X-direction. A third gantry 314 may be configured to move the gripper 304 in the Z-direction. The gantries may be controlled by motors (not shown) that may receive instructions generated by the robot controller 204 (FIG. 2).
[0050] The robot 206 may include an arm 320 to which the gripper 304 may be attached. In some embodiments, the arm 320 may be affixed to the third gantry 314. The imaging device 224 also may be affixed to the arm 320. Thus, the robot 206 may be configured to move the imaging device 224 throughout the sample handler 130 to capture images of items, such as the sample containers 210 (FIG. 2), from various viewpoints as described herein.
[0051] Additional reference is made to FIG. 4A, which illustrates an enlarged view of the gripper 304 of FIG. 3. The gripper 304 illustrated in FIG. 4A includes two fingers 400 that are referred to individually as a first finger 400A and a second finger 400B. Ends of the fingers 400 may have nodes 404 that are configured to contact the sample container 210A. Friction between the nodes 404 and the sample container 210A enables thegripper 304 to grasp the sample container 210A and move the sample container 21 OA as described herein. In the embodiment of FIG. 4A, each of the two fingers 400 includes two nodes, which are referred to as node 404A, node 404B, node 404C, and node 404D. The gripper 304 is moved by the robot 206, and movement of the gripper 304 includes movement of the fingers 400 and the nodes 404.
[0052] FIG. 4B illustrates a top plan view of the sample container 210A showing locations where the nodes 404 may contact the exterior surface of the sample container 210A. The gripper 304 may be configured to rotate or pivot in an arc R41 relative to the sample container 210A. The nodes 404 shown as solid lines indicate first grasp locations where the nodes 404 may contact the sample container 210A. The nodes 404 shown as dashed lines indicate second grasp locations where the nodes 404 may contact the sample container 210A. For example, the gripper 304 and thus the fingers 400 may rotate as shown by the arc R41 so that the nodes 404 may contact the sample container 210 at various arcuate locations on the surface of the sample container 210A.
[0053] The gripper 304 and thus the fingers 400 and the nodes 404 may be configured to move in the z-direction to grasp the sample container 210A at various vertical locations or heights. FIG. 4C illustrates the nodes 404 grasping the sample container 210A at a higher vertical position than the nodes 404 grasping the sample container 210A shown in FIG. 4A. In the configuration of FIG. 4C, the gripper 304 and the fingers 400 may have positioned the nodes 404 so that the nodes 404 do not contact the barcode label 410 located on the surface of the sample container 210A. In the configuration of FIG. 4A, the nodes 404 are located vertically lower than in FIG. 4C and are in contact with the barcode label 410. The methods and apparatus described herein may prevent the nodes 404 from contacting barcode labels as shown in FIG. 4A. In some embodiments, the nodes 404 may contact the barcode labels, but may avoid direct contact with the barcodes themselves.
[0054] Additional reference is made to FIG. 4D, which shows the gripper 304 configured with a large number of degrees of freedom, such as six degrees of freedom. The degrees of freedom enable the gripper 304 to have many different poses to grasp and move the sample container 210A. The degrees of freedom may also be referred toas degrees of pose. The gripper 304 may have other degrees of freedom. The gripper 304 of FIG. 4D may be configured to move or rotate in an arc R42. Thus, the gripper 304, the fingers 400, and the nodes 404 may grasp the sample container 210A when the sample container 210 is askew or in a plurality of different poses. The ability of the gripper 304 to move in the arc R42 may be in addition to movements in the z-direction and along the arc R41 as described in FIGS. 4B and 4C. In some embodiments, the gripper 304 may also be configured to move in an arc that is perpendicular to the arc R42.
[0055] The gripper 304 has been described as having two fingers 400 and four nodes 404. The gripper 304 may have different configurations of fingers and nodes. Reference is made to FIG. 4E, which illustrates a top plan view of the sample container 210A where three nodes 412 are contacting the exterior of the sample container 210A. The three nodes 412 may be spaced equally apart around the circumference of the outer surface of the sample container 210A. The gripper 304 may have other numbers of nodes that are configured to contact the outer surface of the sample container 210A.
[0056] Different types of sample containers 210 may be used in the sample handler 130, and the sample containers 210 may have different surface and geometric properties. Additional reference is made to FIGS. 5A-5F, which illustrate elevation views of sample containers 500A-500F that have different surface and geometric properties. The sample containers 500A-F may be grasped and moved throughout the laboratory system 102 (FIG. 2) in a similar manner as the sample container 210A and as determined by the programs 126 (FIG. 2).
[0057] FIG. 5A illustrates an elevation view of a sample container 500A with a barcode label 502 located approximately in the middle of the sample container 500A. The sample container 500A has a height H51 , a width W51 , and a cap 503, which are geometric and / or surface properties of the sample container 500A. Should the gripper 304 grasp the middle section of the sample container 500A, the gripper 304 may contact the barcode label 502 and / or the barcode itself and may possibly damage the barcode label 502 or the barcode as a result. As shown in FIG. 5B, barcode label 502 may be damaged by a blemish 504 caused by the gripper 304 or the nodes 404 (FIG.4A) adversely contacting the barcode label 502. The blemish 504 may prevent the barcode label 502 from being properly read by a barcode reader (not shown). (Unless otherwise indicated, barcode and barcode label may be used interchangeably herein.)
[0058] Additional reference is made to FIG. 5C, which illustrates a sample container 500C with a barcode label 508 located higher on the sample container than the barcode label 502. The sample container 500C has a height H52, a width W52 and does not include a cap, which are all geometric and / or surface properties of the sample container 500C. The height H52 may be shorter than the height H51 and the width W52 may be wider than the width W51. The optimal grasping location of the gripper 304 on the sample container 500C may be different than the optimal grasping location on the sample container 500A to avoid contact with the barcode label 508. For example, the optimal grasping location algorithm 230 may determine to grasp the sample container 500C above the barcode label 508 but not too near the opening that may cause the sample container 500C to break. In other situations, the optimal grasping location algorithm 230 may determine that the sample container 500C should be grasped below the barcode label 508.
[0059] A sample container 500D shown in FIG. 5D has an anomaly 510 located on the sample container 500D above a barcode label 512 and below a cap 514. Some of a sample 516 (e.g., a liquid) located in the sample container 500D may have spilled from the sample container 500D to cause the anomaly 510. Should the gripper 304 contact the anomaly 510, the gripper 304 may be contaminated or the gripper 304 may not be able to properly grasp the sample container 500D. The optimal grasping location algorithm 230 may determine that the sample container 500D should be grasped below the anomaly 510 and above the barcode label 512.
[0060] Referring to FIG. 5E, a barcode label 522 located on the sample container 500E has an anomaly 524. The anomaly 524 may be a portion of the barcode label 522 that is torn or that has exposed adhesive. If the gripper 304 contacts the anomaly 524, the gripper 304 may become contaminated or may further tear or mar the barcode label 522. Accordingly, the optimal grasping location algorithm 230 may determine that the gripper 304 should grasp the sample container 500E such that both the barcode label522 and the anomaly 524 are avoided. For example, optimal grasping location algorithm 230 may determine that the gripper 304 should grasp the sample container above the anomaly 524 and below the cap 526.
[0061] In FIG. 5F, a barcode label 528 is not properly attached to the sample container 500F. If the barcode label 528 is grasped by the gripper 304, the barcode label 528 may become completely detached from the sample container 500F. In other situations, portions of the barcode label 528 may adhere to the gripper 304, which may interfere with the operation of the gripper 304. Accordingly, the optimal grasping location algorithm 230 may determine that the gripper 304 should grasp the sample container 500F above the barcode label 528 to avoid further detaching the barcode label 528.
[0062] The barcode labels affixed to the sample containers may make identification of the geometric properties difficult. Referring to FIG. 5F, the partially detached barcode label 528 may obscure the true geometric properties of the sample container 500F. The container properties identification algorithm 226 may be trained to identify sample containers when their respective barcode labels are not affixed properly. For example, the container properties identification algorithm 226 may be a CNN with the correct sample container geometry as the ground truth.
[0063] While robots described herein are shown grasping the sample containers 210 (FIG. 2), the robots may also be configured to grasp other items used in the laboratory system 102 (FIG. 1 ). For example, the robots may grasp quality control and calibration packages. The robots may also be configured to grasp different types of sample containers, such as capillary tubes, tube-top sample cups, and other containers configured to carry biological samples. The container properties identification algorithm 226 may identify these other containers and objects, and the optimal grasping location algorithm 230 may generate instructions for the gripper 304 to grasp the other containers and objects.
[0064] With additional reference to FIG. 2, the laboratory system 102 (FIG. 1 ) and methods described herein capture images of the sample containers 210, which may be analyzed by the computer 120. For example, the image data 222 may be analyzed by the image processor 220 and / or the container properties identification algorithm 226.The analysis, such as by the container properties identification algorithm 226, may identify surface properties of the sample containers 210 that may interfere with the operations of the gripper 304 or possibly cause damage to a barcode label which would impede processing of the sample container and / or sample therein. The optimal grasping location algorithm 230 may then determine optimal or proper locations on the sample containers 210 for the gripper 304 to grasp.
[0065] The computer 120 may generate grasp parameters or rules that direct the gripper 304 to grasp the sample containers 210. For example, the robot controller 204 may generate instructions that direct the gripper 304 to move to specific locations and grasp sample containers 210 on the optimal grasping locations. The grasp parameters may include, but are not limited to: three-dimensional (3D) positions of grasp points or regions on the surfaces of the sample containers 210; a (3D) vector to indicate the direction of approach of the gripper 304 to the sample containers 210; joint angles of the gripper 304 to achieve the optimal grasp locations within the robot constraints; joint trajectories of the gripper 304 to characterize the approach to the sample containers 210; and forces applied by the gripper 304 to reliably hold the sample containers 210. In some embodiments, the programs 126 may determine that certain surface properties may require certain forces applied to the sample containers 210 by the gripper 304.
[0066] The laboratory system 102 identifies optimal grasp locations of the sample containers 210 for the gripper 304. In some situations, the grasping properties may enable the gripper 304 or another robot to grasp and move other items, such as reagent packages, within the laboratory system 102. The laboratory system 102 and the methods described herein may consider degrees of freedom of the robot 206 to provide the optimal manner of handling the sample containers 210. The optimal manner of grasping the sample containers 210 may provide safe and reliable grasps while abating potential damage to the sample containers 210 (e.g., breaks and spills of the sample containers 210) caused by the gripper 304.
[0067] Additional reference is made to FIGS. 6A-6F, which illustrate examples of images of different sample containers 600A-600G along with optimal grasping locations and conventional grasping locations 602. The optimal grasping locations refer tograsping locations determined by the laboratory system 102 and / or methods described herein, such as by the optimal grasping location algorithm 230. The optimal grasping locations may be locations on the sample containers 600A-600G where the nodes 404 (FIG. 4C) may contact the sample containers 600A-600G. In conventional systems, the robot may grasp sample containers at a single height above the bottoms of the sample containers. The conventional grasping locations 602 are shown by a dashed line, which is the location where the robot gripper would otherwise grasp the sample containers 600A-600G. The optimal grasping locations are shown as solid horizontal lines uniquely placed on each of the sample containers 600A-600G. As shown, the optimal grasping locations are unique based on different surface and / or geometric properties of the individual sample containers 600A-600G.
[0068] The sample container 600A is relatively short, has a barcode label 604 positioned in the middle of the tube portion, and does not have a cap. An anomaly 605 is located on the upper portion of the barcode label 604. The sample container 600A is also mostly full of a sample 606, which is indicated by hatching. The conventional grasping location 602 on the sample container 600A is located very close to the barcode label 604 and on the anomaly 605. Thus, during conventional grasps, the gripper 304 (FIG. 4A) may contact the barcode label 604 and / or the anomaly 605 and may cause the barcode label 604 to be unreadable similar to the barcode label 502 in FIG. 5B. The optimal grasping location 608 has been determined to be located above the barcode label 604 so that the gripper 304 does not contact the barcode label 604 or the anomaly 605. The optimal grasping location 608 is also below the top of the sample container 600A, which is open, to prevent the gripper 304 from breaking the sample container 600A.
[0069] An image of the sample container 600A may be captured by the imaging device 224 (FIG. 2) and analyzed by the image processor 220, which may process the image data 222 generated by the imaging device 224. The container properties identification algorithm 226 may identify the surface and / or geometric properties in the image data 222 or the processed image data 222. The surface properties may include the condition and / or size of the barcode label 604, and the locations of the barcode label 604 and the anomaly 605. The geometric properties may include the height andwidth of the sample container 600A. The surface and / or geometric properties may also include the absence of a cap and height of the sample 606 within the sample container 600A. Based on the surface and / or geometric properties, the optimal grasping location algorithm 230 may identify the optimal grasping location 608. The optimal grasping location 608 may be determined to be higher than the conventional grasping location 602, which may prevent the gripper 304 from damaging the barcode label 604 and contacting the anomaly 605.
[0070] The sample container 600B is relatively tall, includes a cap 610, and has a barcode label 614 located in the upper portion of the tube. The conventional grasping location 602 is on the barcode label 614, which may cause the gripper 304 (FIG. 4C) to damage the barcode label 614 as described above. An image of the sample container 600B may be captured by the imaging device 224 (FIG. 2) and analyzed to identify surface and / or geometric properties of the sample container 600B. The container properties identification algorithm 226 may identify the surface and / or geometric properties in the image data 222 including height and width of the sample container 600B, cap status, barcode label size and position, and any anomalies on the surface of the sample container 600B. Based on the surface and / or geometric properties, the optimal grasping location algorithm 230 may identify the optimal grasping location 616, which may be located below the barcode label 614 to prevent the gripper 304 (FIG. 4C) from damaging the barcode label 614.
[0071] An image of the sample container 600C may be captured and analyzed to identify surface and / or geometric properties of the sample container 600C. The surface properties may include the condition of the barcode label 624, the location of the barcode label 624, and the location and size of an anomaly 625. The geometric properties may include the presence of a cap 628 and height of a sample 629 contained in the sample container 600C. The sample container 600C is the same height as the sample container 600A, but the barcode label 624 on the sample container 600C is lower than the barcode label 604. The optimal grasping location 630 may be determined by the optimal grasping location algorithm 230 to be slightly lower than the conventional grasping location 602, which may prevent the gripper 304 from interfering with the cap 628 and the anomaly 625.
[0072] The sample container 600D is relatively tall and does not include a cap. The container properties identification algorithm 226 may identify the height, width, and / or cap status. Based on these properties, the optimal grasping location algorithm 230 may determine the optimal grasping location 634, which may be below the top of the sample container 600D and above the conventional grasping location 602 to prevent the gripper 304 from damaging the sample container 600D.
[0073] The sample container 600E includes a barcode label 640 that is skewed and not properly attached to the sample container 600E. Image data 222 (FIG. 2) of the sample container 600E may be analyzed by the container properties identification algorithm 226, which may identify the skewed and partially detached barcode label 640. The optimal grasping location algorithm 230 may identify the optimal grasping location 642 to be well above the barcode label 640 in order to prevent the nodes 404 (FIG. 4C) from further damaging or detaching the barcode label 640. In some embodiments, the optimal grasping location algorithm 230 may direct the robot controller 204 (FIG. 2) to generate instructions that prevent the nodes 404 from contacting the barcode label 640 during approach to and retreat from the sample container 600E.
[0074] The sample container 600F is skewed. For example, the sample container 600F may have moved within a sample mover (e.g., one of the sample movers 214 - FIG. 2). The image data 222 (FIG. 2) of the sample container 600F may be analyzed by the container properties identification algorithm 226, which may identify the skewed sample container 600F. The optimal grasping location algorithm 230 may identify the optimal grasping location 648 to be well above a barcode label 646 in order to prevent the nodes 404 (FIG. 4C) from damaging the barcode label 646. The optimal grasping location 648 may be slanted to be perpendicular to the sides of the sample container 600F. In some embodiments, the optimal grasping location algorithm 230 may direct the robot controller 204 (FIG. 2) to generate instructions that direct the gripper 304 to grasp the sample container 600E at an angle or pose that is similar or identical to the angle or pose of the sample container 600F. Thus, the nodes 404 (FIG. 4C) may all contact or be proximate the optimal grasping location 648.
[0075] Reference is made to FIG. 7, which illustrates a first sample container 700 and a second sample container 702 located on or being moved on the track 216. The first sample container 700 is being transported by way of a first sample mover 706 and the second sample container 702 is being transported by way of a second sample mover 708. The sample movers 706, 708 may undergo wear or move abruptly, which may cause the sample container 702 to become skewed, such as shown by the sample container 600F. The first sample container 700 has a perpendicular orientation shown by the dashed line 710 that may be perpendicular to the track 216. The second sample container 702 has a skewed orientation shown by the dashed line 712, which may be a result of movement of the second sample mover 708 starting and stopping or wear and tear on the second sample mover 708.
[0076] With additional reference to the gripper 304 shown in FIG. 4D, the gripper 304 may be moved to grasp the second sample container 702. For example, image data 222 (FIG. 2) of the second sample container 702 may be analyzed by the container properties identification algorithm 226 to identify the skew or pose of the second sample container 702. The optimal grasping location algorithm 230 may direct the robot controller 204 to move the gripper 304 to grasp the skewed second sample container 702 based on the pose of the second sample container 702.
[0077] The laboratory system 102 (FIG. 1 ) may be configured to move the sample movers 214 (FIG. 2) to a transfer location where image data 222 may be generated by the imaging device 224 and the sample containers 700, 702 may be grasped. In the embodiment of FIG. 7, the track 216 has a transfer location 716 where the first sample mover 706 is moved so that the first sample container 700 may be imaged and / or grasped by the robot 206 (FIG. 3). In some cases, the first sample mover 706 may be moved to an offset transfer location 718 instead of the transfer location 716 because of issues with the track 216 or other items in the laboratory system 102. Image data 222 of the first sample container 700 may still be generated when the first sample container 700 is at the offset transfer location 718. The container properties identification algorithm 226 (FIG. 2) may analyze the image data 222 to identify the location of the first sample container 700 at the offset transfer location 718. The optimal grasping location algorithm 230 may then direct the robot controller 204 to move the gripper 304(FIG. 40) to the offset transfer location 718 where the gripper 304 may properly grasp the first sample container 700 as described herein.
[0078] Additional reference is made to FIG. 8, which is a flowchart illustrating a method 800 of grasping sample containers, such as the first sample container 700 in FIG. 7. The method 800 includes in block 802 waiting for a sample container (e.g., first sample container 700) to arrive at the transfer location 716 (and, in some embodiments, to arrive at transfer location 716 within a predetermined offset of the transfer location 716, such as, offset transfer location 718). Processing proceeds to decision block 804 where a determination is made as to whether a sample container is located at the transfer location 716. If no sample container is located at the transfer location 716 the process continues to wait as described in block 802.
[0079] If a sample container is present at the transfer location 716, processing proceeds to block 806 where an image of the first sample container 700 is captured (e.g., by the imaging device 224) and analyzed to detect surface and / or geometric properties. For example, the container properties identification algorithm 226 may analyze the image data 222 to identify surface and / or geometric properties of the first sample container 700. The processing then proceeds to block 808 where the optimal grasp location(s) is determined and grasp parameters of the robot 206, such as the pose of the gripper 304 and grasp location are also determined.
[0080] When the optimal grasp location is determined, processing proceeds to block 810 where the first sample container 700 is grasped by the gripper 304 using the grasp parameters. For example, the robot controller 204 (FIG. 2) may generate instructions that direct the gripper 304 to move as determined by the optimal grasping location algorithm 230.
[0081] Reference is made to FIG. 9, which illustrates a top plan view of the track 216 with the first sample container 700 and the sample mover 706 positioned at the transfer location 716. In the embodiment of FIG. 9, there are three imaging devices 900, referred to individually as a first imaging device 902, a second imaging device 904, and a third imaging device 906, located proximate the track 216. Each of the imaging devices 900 may generate image data 222 (FIG. 2) from different views of the first sample container700. The different views are shown as dashed lines extending from the imaging device 900. The image processor 220 may consolidate or stitch the images generated by the imaging devices 900 to form a single image to analyze a 360 degree view of the first sample container 700. The processing described above may be applied using the 360- degree view of the first sample container 700. For example, the determinations made by the container properties identification algorithm 226 and the optimal grasping location algorithm 230 may be based on the 360-degree view.
[0082] The determinations of the optimal grasping locations may include the type of materials (e.g., glass or plastic) of the items (e.g., sample container) being grasped. For example, if a certain material renders an item susceptible to breaking, the optimal grasping location may be located far from a top opening, which may be where such material may be weakest and where the grasping may cause the item to break. The grasping properties may consider other factors, such as mechanical constraints of the robot 206, which includes joint limits, force / torque considerations, and available workspace for the robot 206. The surface friction of the sample containers 210 at the optimal grasping locations may be a factor in determining a force applied by the gripper 304 to the sample containers 210.
[0083] Reference is now made to FIG. 10, which is a flowchart of a method 1000 of grasping a container (e.g., sample containers 210) in a diagnostic laboratory system (e.g., laboratory system 102) using a robot (e.g., robot 206). The method includes, in block 1002, capturing one or more images of a container in a diagnostic laboratory system, wherein each captured image includes image data (e.g., image data 222). The method 1000 includes, in block 1004, analyzing the image data to identify one or more surface properties of the container. The surface properties may be indicia, such as barcodes, barcode labels, anomalies, such as adhesives from barcode labels, and spilled liquids. The surface properties may also be caps on the sample containers. The method 1000 includes, in block 1006, determining a grasping location (e.g., grasping locations 608, 616, 630) on the container for a gripper (e.g., gripper 304) of a robot (e.g., robot 206) in the diagnostic laboratory system to grasp the container in response to the analyzing. The grasping location may be an optimal grasping location that avoids surface properties that may hinder the grasping. Thus, the gripper 304 may be made toavoid contacting barcodes, barcode labels, liquids, adhesives, and anomalies on the surfaces of the containers. The method 1000 includes, in block 1008, grasping the container at the grasping location using the gripper.
[0084] Reference is now made to FIG. 11 , which illustrates a flowchart of a method 1100 of grasping a sample container (e.g., sample containers 210) in a diagnostic laboratory system (e.g., laboratory system 102) using a robot (e.g., robot 26). The method 1100 includes, in block 1102, capturing one or more images of a sample container in a diagnostic laboratory system, wherein each captured image includes image data (e.g., image data 222), and wherein the sample container is configured to hold a biological sample. The method 1100 includes, in block 1104, analyzing the image data to identify one or more surface properties of the sample container, the one or more surface properties including at least one of an indicia (e.g., barcode label 410), a liquid, and an anomaly (e.g., anomaly 510) on the surface of the sample container. The method 1100 includes, in block 1106, determining a grasping location (e.g., grasping locations 608, 616, 630) on the sample container for a gripper (e.g., gripper 304) of a robot (e.g., robot 206) in the diagnostic laboratory system to grasp the sample container that avoids the one or more surface properties. The method 1100 includes, in block 1108, grasping the sample container at the grasping location using the gripper.NON-LIMITING ILLUSTRATIVE EMBODIMENTS
[0085] Illustrative embodiment 1 . A method of grasping a container in a diagnostic laboratory system using a robot, the method comprising: capturing one or more images of a container in a diagnostic laboratory system, wherein each captured image includes image data; analyzing the image data to identify one or more surface properties of the container; determining a grasping location on the container for a gripper of a robot in the diagnostic laboratory system to grasp in response to the analyzing; and grasping the container at the grasping location using the gripper.
[0086] Illustrative embodiment 2. The method of illustrative embodiment 1 , further comprising generating instruction to direct the gripper to grasp the container at the grasping location.
[0087] Illustrative embodiment 3. The method according to one of the preceding embodiments, wherein the container is a sample container configured to hold a biological sample.
[0088] Illustrative embodiment 4. The method according to one of the preceding embodiments, wherein the container is configured to hold a chemical consumed in the diagnostic laboratory system.
[0089] Illustrative embodiment 5. The method according to one of the preceding embodiments, wherein: the gripper has at least one degree of freedom; and the determining a grasping location comprises determining a grasping location for the gripper in response to the at least one degree of freedom.
[0090] Illustrative embodiment 6. The method according to one of the preceding embodiments, wherein the container has one or more geometric properties and wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the one or more geometric properties.
[0091] Illustrative embodiment 7. The method according to one of the preceding embodiments, further comprising determining a material of the container, wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the determining the material of the container.
[0092] Illustrative embodiment 8. The method according to one of the preceding embodiments, wherein the analyzing comprises identifying a location of an indicia on the container, wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the determining the location of the indicia.
[0093] Illustrative embodiment 9. The method according to one of the preceding embodiments, wherein the grasping location is not on the indicia.
[0094] Illustrative embodiment 10. The method according to one of the preceding embodiments, wherein the analyzing comprises identifying an anomaly on the container and wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the identifying the anomaly.
[0095] Illustrative embodiment 11 . The method according to one of the preceding embodiments, wherein the grasping location is not on the anomaly.
[0096] Illustrative embodiment 12. The method according to one of the preceding embodiments, wherein the anomaly is liquid.
[0097] Illustrative embodiment 13. The method according to one of the preceding embodiments, wherein the anomaly is a portion of a label affixed to the container.
[0098] Illustrative embodiment 14. The method according to one of the preceding embodiments, wherein the analyzing comprises identifying a pose of the container and further comprising adjusting a pose of the gripper in response to identifying the pose of the container.
[0099] Illustrative embodiment 15. The method according to one of the preceding embodiments, wherein the analyzing determines whether the container has a cap.
[0100] Illustrative embodiment 16. The method according to one of the preceding embodiments, wherein at least one of the analyzing the image data or the determining a grasping location comprises using a deep neural network.
[0101] Illustrative embodiment 17. The method according to one of the preceding embodiments, wherein the determining a grasping location on the container for a gripper of a robot comprises determining a height on the container for the gripper to grasp the container.
[0102] Illustrative embodiment 18. A diagnostic laboratory system comprising: an imaging device configured to capture an image of a container; a robot comprising a gripper configured to grasp the container and move the container within the diagnostic laboratory system; and a computer configured to: receive image data generated by the imaging device; analyze the image data to identify one or more surface properties of the container; determine a grasping location on the container for the gripper in response to the analyzing; and generate instructions to direct the gripper to grasp the container at the grasping location.
[0103] Illustrative embodiment 19. The diagnostic laboratory system of illustrative embodiment 18, wherein the container is a sample container configured to hold a biological sample.
[0104] Illustrative embodiment 20. A method of grasping a sample container in a diagnostic laboratory system using a robot, the method comprising: capturing one or more images of a sample container in a diagnostic laboratory system, wherein each captured image includes image data, and wherein the sample container is configured to hold a biological sample; analyzing the image data to identify one or more surface properties of the sample container, the one or more surface properties including at least one of an indicia, a liquid, and an anomaly on the surface of the sample container; determining a grasping location on the sample container for a gripper of a robot in the diagnostic laboratory system to grasp that avoids the one or more surface properties; and grasping the sample container at the grasping location using the gripper.
[0105] While the disclosure is susceptible to various modifications and alternative forms, specific method and apparatus embodiments have been shown by way of example in the drawings and are described in detail herein. It should be understood, however, that the particular methods and apparatus disclosed herein are not intended to limit the disclosure.
Claims
WHAT IS CLAIMED IS:1 . A method of grasping a container in a diagnostic laboratory system using a robot, the method comprising: capturing one or more images of a container in a diagnostic laboratory system, wherein each captured image includes image data; analyzing the image data to identify one or more surface properties of the container; determining a grasping location on the container for a gripper of a robot in the diagnostic laboratory system to grasp in response to the analyzing; and grasping the container at the grasping location using the gripper.
2. The method of claim 1 , further comprising generating instruction to direct the gripper to grasp the container at the grasping location.
3. The method of claim 1 , wherein the container is a sample container configured to hold a biological sample.
4. The method of claim 1 , wherein the container is configured to hold a chemical consumed in the diagnostic laboratory system.
5. The method of claim 1 , wherein: the gripper has at least one degree of freedom; and the determining a grasping location comprises determining a grasping location for the gripper in response to the at least one degree of freedom.
6. The method of claim 1 , wherein the container has one or more geometric properties and wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the one or more geometric properties.
7. The method of claim 1 , further comprising determining a material of the container, wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the determining the material of the container.
8. The method of claim 1 , wherein the analyzing comprises identifying a location of an indicia on the container, wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the determining the location of the indicia.
9. The method of claim 8, wherein the grasping location is not on the indicia.
10. The method of claim 1 , wherein the analyzing comprises identifying an anomaly on the container and wherein the determining a grasping location comprises determining a grasping location for the gripper in response to the identifying the anomaly.11 . The method of claim 10, wherein the grasping location is not on the anomaly.
12. The method of claim 10, wherein the anomaly is liquid.
13. The method of claim 10, wherein the anomaly is a portion of a label affixed to the container.
14. The method of claim 1 , wherein the analyzing comprises identifying a pose of the container and further comprising adjusting a pose of the gripper in response to identifying the pose of the container.
15. The method of claim 1 , wherein the analyzing determines whether the container has a cap.
16. The method of claim 1 , wherein at least one of the analyzing the image data or the determining a grasping location comprises using a deep neural network.
17. The method of claim 1 , wherein the determining a grasping location on the container for a gripper of a robot comprises determining a height on the container for the gripper to grasp the container.
18. A diagnostic laboratory system comprising: an imaging device configured to capture an image of a container; a robot comprising a gripper configured to grasp the container and move the container within the diagnostic laboratory system; and a computer configured to: receive image data generated by the imaging device; analyze the image data to identify one or more surface properties of the container; determine a grasping location on the container for the gripper in response to the analyzing; and generate instructions to direct the gripper to grasp the container at the grasping location.
19. The diagnostic laboratory system of claim 18, wherein the container is a sample container configured to hold a biological sample.
20. A method of grasping a sample container in a diagnostic laboratory system using a robot, the method comprising: capturing one or more images of a sample container in a diagnostic laboratory system, wherein each captured image includes image data, and wherein the sample container is configured to hold a biological sample; analyzing the image data to identify one or more surface properties of the sample container, the one or more surface properties including at least one of an indicia, a liquid, and an anomaly on the surface of the sample container;determining a grasping location on the sample container for a gripper of a robot in the diagnostic laboratory system to grasp that avoids the one or more surface properties; and grasping the sample container at the grasping location using the gripper.