A method, device, equipment and storage medium for a mechanical arm to grab a part

Abstract

The application discloses a kind of mechanical arm part grabbing method, device, equipment and storage medium.The method comprises: based on the camera equipment installed in the central part of the end gripper of multi-axis mechanical arm equipment, the part image information corresponding to the part to be grabbed is obtained;Based on the device posture of the multi-axis mechanical arm equipment, according to the part image information, mechanical arm coordinate system and camera coordinate system, the target rotation translation matrix is determined, wherein the device posture includes initial posture and other postures;According to the target rotation translation matrix, the multi-axis mechanical arm equipment is controlled to grab the part to be grabbed, the mechanical arm coordinate system and the camera coordinate system can be combined, the multi-axis mechanical arm is controlled by visual image, and then the automation degree of multi-axis mechanical arm is improved, and use experience is improved.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to a method, apparatus, device, and storage medium for a robotic arm to grasp parts. Background Technology

[0002] The emergence of multi-axis robotic arms has greatly improved production efficiency and product quality while reducing production costs. Because of their high degree of freedom, they can perform many complex tasks.

[0003] However, existing multi-axis robotic arms determine their position through visual images, but the camera deployment position is different from the location of the multi-axis robotic arm. This results in the low level of automation caused by controlling the movement of the multi-axis robotic arm through visual images. It is impossible to automatically control the movement of the robotic arm according to the actual scene, which reduces the working efficiency of the robotic arm and the accuracy of moving to the target position is poor, thus failing to meet the needs of actual situations. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for a robotic arm to grasp parts, thereby merging the robotic arm coordinate system and the camera coordinate system to achieve control of a multi-axis robotic arm through visual images, thus improving the automation level of the multi-axis robotic arm and enhancing the user experience.

[0005] According to one aspect of the present invention, a method for a robotic arm to grasp a part is provided, the method comprising:

[0006] Based on the camera device installed in the center of the end gripper of the multi-axis robotic arm, the image information of the part to be gripped is obtained;

[0007] Based on the device posture of the multi-axis robotic arm, the target rotation and translation matrix is ​​determined according to the part image information, the robotic arm coordinate system, and the camera coordinate system. The device posture includes the initial posture and other postures.

[0008] The multi-axis robotic arm is controlled to grasp the part to be grasped based on the target rotation and translation matrix.

[0009] According to another aspect of the present invention, a robotic arm device for grasping parts is provided, the device comprising:

[0010] The part image information acquisition module is used to acquire part image information corresponding to the part to be grasped based on the camera device installed in the center of the end gripper of the multi-axis robotic arm device;

[0011] The rotation and translation matrix determination unit is used to determine the target rotation and translation matrix based on the device posture of the multi-axis robotic arm device, according to the part image information, the robotic arm coordinate system and the camera coordinate system, wherein the device posture includes an initial posture and other postures;

[0012] The robotic arm grasping execution module is used to control the multi-axis robotic arm to grasp the part to be grasped according to the target rotation and translation matrix.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the robotic arm grasping method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the robotic arm grasping part method according to any embodiment of the present invention.

[0018] The technical solution of this invention acquires part image information corresponding to the part to be grasped by using a camera device installed at the center of the end effector of a multi-axis robotic arm. Based on the posture of the multi-axis robotic arm, a target rotation and translation matrix is ​​determined according to the part image information, the robotic arm coordinate system, and the camera coordinate system. The device posture includes an initial posture and other postures. Based on the target rotation and translation matrix, the multi-axis robotic arm is controlled to grasp the part to be grasped. This allows the robotic arm coordinate system and the camera coordinate system to be merged, enabling control of the multi-axis robotic arm through visual images, thereby improving the automation level of the multi-axis robotic arm and enhancing the user experience.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1This is a flowchart of a method for a robotic arm to grasp parts according to Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart of a method for a robotic arm to grasp parts according to Embodiment 2 of the present invention;

[0023] Figure 3 This is a flowchart of a method for a robotic arm to grasp parts according to Embodiment 3 of the present invention;

[0024] Figure 4 This is a structural diagram of a robotic arm gripping part device according to Embodiment 4 of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the robotic arm grasping method of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a method for a robotic arm to grasp parts, provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where a robotic arm automatically grasps parts based on a view image. This method can be executed by a robotic arm part-grabbing device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0030] S101. Based on the camera device installed in the center of the end gripper of the multi-axis robotic arm, acquire the part image information corresponding to the part to be gripped.

[0031] Among them, part image information can refer to image information of the part to be grasped captured by the camera device.

[0032] For example, a camera device installed at the center of the end gripper of a multi-axis robotic arm can capture and obtain part image information corresponding to the part to be gripped.

[0033] S102. Based on the device posture of the multi-axis robotic arm, determine the target rotation and translation matrix according to the part image information, the robotic arm coordinate system, and the camera coordinate system.

[0034] The device posture includes an initial posture and other postures. The initial posture can refer to the posture of the multi-axis robotic arm during initialization. Other postures can refer to postures before initialization. The robotic arm coordinate system can refer to a coordinate system established based on the robotic arm. The camera coordinate system is a coordinate system established based on the camera.

[0035] Specifically, based on the device posture of the multi-axis robotic arm, sample points are determined in the part image information, and the coordinates of these sample points in the robotic arm coordinate system and the camera coordinate system are determined respectively, thereby determining the target rotation and translation coordinate system between the robotic arm coordinate system and the camera coordinate system.

[0036] In this invention, the construction of the robotic arm coordinate system and the camera coordinate system can be selected according to the actual situation. Preferably, they are constructed in the following manner:

[0037] For the camera device, the optical center of the imaging plane of the camera device is taken as the origin O. The X-axis and Y-axis are constructed on the imaging plane of the camera device, and the Z-axis is constructed in the optical axis direction of the camera device. The X-axis and Y-axis are perpendicular to each other, and the Z-axis is perpendicular to the XOY plane.

[0038] For the front end base of the robotic arm device, based on the ground, an X-axis parallel to the ground is constructed in the robotic arm coordinate system. A Y-axis perpendicular to the X-axis is constructed in the same plane containing the X-axis. A Z-axis perpendicular to both the X-axis and the Y-axis is constructed at the intersection of the X-axis and the Y-axis. The front end base is perpendicular to the ground.

[0039] In other words, the camera coordinate system is a coordinate system centered on the camera to measure the target's position. The camera coordinate system uses the camera's optical center as its origin O, with the X and Y axes defined by the imaging plane, and the Z axis as the optical axis, perpendicular to the XOY plane. The robotic arm coordinate system is set with the X-axis parallel to the ground, the Y-axis perpendicular to the ground, and the Z-axis perpendicular to both the X and Y axes. Furthermore, the rotation angle around the X-axis can be defined as the pitch angle, and the rotation angle around the Y-axis as the yaw angle.

[0040] S103. Based on the target rotation and translation matrix, control the multi-axis robotic arm to grasp the part to be grasped.

[0041] Specifically, after determining the target rotation and translation matrix, the three-dimensional coordinates of the camera coordinate system in the robotic arm coordinate system can be calculated using the target rotation and translation matrix. Then, based on the three-dimensional coordinates, control commands are generated to control the multi-axis robotic arm to grasp the part to be grasped.

[0042] The technical solution of this invention acquires part image information corresponding to the part to be grasped by using a camera device installed at the center of the end effector of a multi-axis robotic arm. Based on the posture of the multi-axis robotic arm, a target rotation and translation matrix is ​​determined according to the part image information, the robotic arm coordinate system, and the camera coordinate system. The device posture includes an initial posture and other postures. Based on the target rotation and translation matrix, the multi-axis robotic arm is controlled to grasp the part to be grasped. This allows the robotic arm coordinate system and the camera coordinate system to be merged, enabling control of the multi-axis robotic arm through visual images, thereby improving the automation level of the multi-axis robotic arm and enhancing the user experience.

[0043] Example 2

[0044] Figure 2 This is a flowchart of a method for a robotic arm to grasp parts according to Embodiment 2 of the present invention. Based on the above embodiments, this embodiment further refines the process of determining the target rotation and translation matrix when the multi-axis robotic arm is in its initial posture. For example... Figure 2 As shown, the method includes:

[0045] S201. Based on the camera device installed in the center of the end gripper of the multi-axis robotic arm, acquire the part image information corresponding to the part to be gripped.

[0046] S202. When the multi-axis robotic arm is in its initial posture, determine the relative position information between the head base of the multi-axis robotic arm and the camera device based on the part image information.

[0047] Specifically, when the multi-axis mechanical device is in its initial posture, the positional distance between the end base of the robotic arm and the camera in the gripper at the beginning of the robotic arm is fixed, thereby determining the relative positional information between the end base and the camera device.

[0048] For example, determining the relative position information of the multi-axis robotic arm device and the camera device based on the part image information includes:

[0049] Based on the part image information, determine the camera calibration sample point in the camera device; determine the sample point coordinate information of the camera calibration sample point in the robot arm coordinate system; based on the sample point coordinate information, determine the relative position information of the device between the head end base of the multi-axis robot arm device and the camera device.

[0050] Among them, camera calibration sample points can be the sample points that the calibration sample points are mapped in the camera device.

[0051] Specifically, after the camera device captures image information of the part, pre-calibrated calibration sample points in the part image information are determined, and the mapping points of these calibration sample points in the camera device are identified as camera calibration sample points. Further, the coordinate information of the camera calibration sample points in the robotic arm coordinate system is determined. Based on the sample point coordinate information, the relative position information between the head base of the multi-axis robotic arm and the camera device is determined.

[0052] S203. Based on the relative position information of the device, determine the first rotation matrix and the first translation vector between the robotic arm coordinate system and the camera coordinate system.

[0053] Specifically, after determining the relative position information between the head base and the camera device, the rotation matrix and translation vector between the robotic arm coordinate system and the camera coordinate system can be determined based on the relative device information, and these can be defined as the first rotation matrix and the first translation matrix.

[0054] S204. Based on the first rotation matrix and the first translation vector, determine the target rotation and translation matrix.

[0055] Specifically, the first rotation matrix is ​​expanded into a 4x4 matrix, and then the first translation vector is also expanded into a 4x4 matrix. Multiplying these two 4x4 matrices yields the final target rotation and translation matrix.

[0056] S205. Based on the target rotation and translation matrix, control the multi-axis robotic arm to grasp the part to be grasped.

[0057] The technical solution of this invention, when the multi-axis robotic arm is in its initial posture, determines the relative position information between the head end base of the multi-axis robotic arm and the camera device based on the part image information. Based on the relative position information, a first rotation matrix and a first translation vector are determined between the robotic arm coordinate system and the camera coordinate system. Based on the first rotation matrix and the first translation vector, a target rotation and translation matrix is ​​determined. This allows the target rotation and translation matrix to be determined based on the relative position information between the head end base of the multi-axis robotic arm and the camera device, thereby improving the accuracy of coordinate system transformation, and ultimately enhancing the automation and accuracy of the multi-axis robotic arm, improving the user experience.

[0058] Example 3

[0059] Figure 3 This is a flowchart of a method for a robotic arm to grasp a part according to Embodiment 3 of the present invention. Based on the above embodiments, this embodiment further refines the process of determining the target rotation and translation matrix when the multi-axis robotic arm is in its initial posture. For example... Figure 3 As shown, the method includes:

[0060] S301. Based on the camera device installed in the center of the end gripper of the multi-axis robotic arm, acquire the part image information corresponding to the part to be gripped.

[0061] S302. When the multi-axis robotic arm is in another posture, determine the axis movement information of the multi-axis robotic arm.

[0062] The robotic arm device of the present invention is a multi-axis robotic arm. When the multi-axis robotic arm device moves, that is, when it is not in the initial posture, the movement information of each axis is acquired, and then the axis movement information of the multi-axis robotic arm device can be determined.

[0063] For example, determining the arm movement information of the multi-axis robotic arm device includes:

[0064] For each axis of the multi-axis robotic arm, the rotation angle information of the axis around the X-axis, Y-axis and Z-axis of the robotic arm coordinate system, as well as the translation information along the axis direction, are obtained; based on all the rotation angle information and the translation information, the axis movement information of the multi-axis robotic arm is determined.

[0065] Specifically, the rotation angle information of each robotic arm axis around the X-axis, Y-axis, and Z-axis of the robotic arm coordinate system is collected separately. The rotation information of each axis is then summarized to determine the rotation angle information. Similarly, the translation information of each axis is summarized to determine the translation amount information. Based on the translation and rotation information, the axis movement information of the multi-axis robotic arm device is determined.

[0066] S303. Based on the arm movement information and the part image information, determine the second rotation matrix and the second translation vector between the robotic arm coordinate system and the camera coordinate system.

[0067] Specifically, the relative positional relationship between the robotic arm's end-effector base and the end-effector camera is determined based on the part image information in the initial posture. Further, the camera movement information of the robotic arm's end-effector camera can be determined based on the arm's movement information. The relative positional relationship and camera movement information allow for the determination of the actual relative information between the robotic arm's end-effector camera and the base at the current moment. Then, based on the actual relative information, the second rotation matrix and the second translation vector between the robotic arm coordinate system and the camera coordinate system are determined.

[0068] For example, determining the second rotation matrix between the robotic arm coordinate system and the camera coordinate system based on the arm movement information and the part image information includes: constructing an X-axis rotation matrix corresponding to the X-axis, a Y-axis rotation matrix corresponding to the Y-axis, and a Z-axis rotation matrix corresponding to the Z-axis based on the rotation angle information in the arm movement information; and determining the second rotation matrix between the robotic arm coordinate system and the camera coordinate system based on the part image information, the X-axis rotation matrix, the Y-axis rotation matrix, and the Z-axis rotation matrix.

[0069] Specifically, the X-axis rotation matrix, Y-axis rotation matrix, and Z-axis rotation matrix are as follows:

[0070]

[0071]

[0072]

[0073] Then, the second rotation matrix is ​​R = R(z,θ) × R(y,θ) × R(x,θ).

[0074] S304. Determine the target rotation and translation matrix based on the second rotation matrix and the second translation vector.

[0075] Specifically, based on the same principle, the target rotation and translation matrix is ​​calculated according to the second rotation matrix and the second translation vector.

[0076] Specifically, this is achieved by adjusting the rotation angles α, β, and θ of the 3D real-world image around the x, y, and z axes, as well as the translation amount t along the three axes. x t y t z This ensures that the captured target coincides with the target in the model, where is the rotation and translation parameter of the transformation matrix M. The target rotation and translation matrix is ​​then as follows:

[0077]

[0078] The transformation matrix M was obtained through camera calibration, which can transform the 3D coordinates obtained by the depth camera to the robot arm coordinate system. The transformation formula is as follows:

[0079] P robot =M×P camera

[0080] Among them, P robot It can refer to the robot arm coordinate system, P camera It could refer to the camera coordinate system.

[0081] S305. Based on the target rotation and translation matrix, control the multi-axis robotic arm to grasp the part to be grasped.

[0082] The technical solution of this invention determines the axis movement information of the multi-axis robotic arm device when the device is in other postures; based on the axis movement information and the part image information, it determines a second rotation matrix and a second translation vector between the robotic arm coordinate system and the camera coordinate system; based on the second rotation matrix and the second translation vector, and based on the axis movement information, it further determines the target rotation and translation matrix, thereby improving the accuracy of coordinate system transformation, thus improving the automation level and accuracy of the multi-axis robotic arm and enhancing the user experience.

[0083] Example 4

[0084] Figure 4 This is a schematic diagram of a robotic arm gripping device provided in Embodiment 4 of the present invention. Figure 4 As shown, the device includes:

[0085] The part image information acquisition module 401 is used to acquire part image information corresponding to the part to be grasped based on the camera device installed in the center of the end gripper of the multi-axis robotic arm device;

[0086] The rotation and translation matrix determination module 402 is used to determine the target rotation and translation matrix based on the device posture of the multi-axis robotic arm device, according to the part image information, the robotic arm coordinate system and the camera coordinate system, wherein the device posture includes an initial posture and other postures;

[0087] The robotic arm grasping execution module 403 is used to control the multi-axis robotic arm device to grasp the part to be grasped according to the target rotation and translation matrix.

[0088] The technical solution of this invention acquires part image information corresponding to the part to be grasped by using a camera device installed at the center of the end effector of a multi-axis robotic arm. Based on the posture of the multi-axis robotic arm, a target rotation and translation matrix is ​​determined according to the part image information, the robotic arm coordinate system, and the camera coordinate system. The device posture includes an initial posture and other postures. Based on the target rotation and translation matrix, the multi-axis robotic arm is controlled to grasp the part to be grasped. This allows the robotic arm coordinate system and the camera coordinate system to be merged, enabling control of the multi-axis robotic arm through visual images, thereby improving the automation level of the multi-axis robotic arm and enhancing the user experience.

[0089] Optionally, the robotic arm coordinate system and the camera coordinate system are constructed in the following manner:

[0090] For the camera device, the optical center of the imaging plane of the camera device is taken as the origin O. The X-axis and Y-axis are constructed on the imaging plane of the camera device, and the Z-axis is constructed in the optical axis direction of the camera device. The X-axis and Y-axis are perpendicular to each other, and the Z-axis is perpendicular to the XOY plane.

[0091] For the front end base of the robotic arm device, based on the ground, an X-axis parallel to the ground is constructed in the robotic arm coordinate system. A Y-axis perpendicular to the X-axis is constructed in the same plane containing the X-axis. A Z-axis perpendicular to both the X-axis and the Y-axis is constructed at the intersection of the X-axis and the Y-axis. The front end base is perpendicular to the ground.

[0092] Optionally, the rotation and translation matrix determination module 402 is used for:

[0093] The relative position information determination unit is used to determine the relative position information between the head end base of the multi-axis robotic arm and the camera device based on the part image information when the multi-axis robotic arm is in its initial posture.

[0094] The relative position information parsing unit is used to determine the first rotation matrix and the first translation vector between the robot arm coordinate system and the camera coordinate system based on the relative position information of the device.

[0095] The rotation and translation matrix determination unit is used to determine the target rotation and translation matrix based on the first rotation matrix and the first translation vector.

[0096] Optionally, the rotation and translation matrix determination unit is used for:

[0097] Based on the image information of the parts, determine the camera calibration sample points in the camera device;

[0098] Determine the coordinate information of the camera calibration sample point in the robotic arm coordinate system;

[0099] Based on the coordinate information of the sample points, the relative position information of the device between the head base of the multi-axis robotic arm and the camera device is determined.

[0100] Optionally, the rotation and translation matrix determination module 402 is specifically used for:

[0101] The arm movement information determination unit is used to determine the arm movement information of the multi-axis robotic arm device when the multi-axis robotic arm device is in other postures;

[0102] The arm movement information parsing unit is used to determine the second rotation matrix and the second translation vector between the robot arm coordinate system and the camera coordinate system based on the arm movement information and the part image information.

[0103] A rotation and translation matrix determination unit is used to determine the target rotation and translation matrix based on the second rotation matrix and the second translation vector.

[0104] Optionally, the arm movement information determination unit is specifically used for:

[0105] For each of the multi-axis robotic arm devices, the rotation angle information of the arm around the X-axis, Y-axis and Z-axis of the robotic arm coordinate system, as well as the translation information along the arm direction, are obtained.

[0106] Based on all the rotation angle information and the translation information, the axis movement information of the multi-axis robotic arm device is determined.

[0107] Optionally, the rotation and translation matrix determination unit is specifically used for:

[0108] Based on the rotation angle information in the arm movement information, construct the X-axis rotation matrix corresponding to the X-axis, the Y-axis rotation matrix corresponding to the Y-axis, and the Z-axis rotation matrix corresponding to the Z-axis.

[0109] Based on the part image information, the X-axis rotation matrix, the Y-axis rotation matrix, and the Z-axis rotation matrix, a second rotation matrix is ​​determined between the robotic arm coordinate system and the camera coordinate system.

[0110] The robotic arm part-grabbing device provided in the embodiments of the present invention can execute the robotic arm part-grabbing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0111] Example 5

[0112] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0113] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0114] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0115] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method of a robotic arm grasping a part.

[0116] In some embodiments, the method of a robotic arm grasping a part can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method of a robotic arm grasping a part described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method of a robotic arm grasping a part by any other suitable means (e.g., by means of firmware).

[0117] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0121] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0122] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0123] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0124] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for a robotic arm to grasp parts, characterized in that, include: Based on the camera device installed in the center of the end gripper of the multi-axis robotic arm, the image information of the part to be gripped is obtained; Based on the device posture of the multi-axis robotic arm, the target rotation and translation matrix is ​​determined according to the part image information, the robotic arm coordinate system, and the camera coordinate system. The device posture includes the initial posture and other postures. Based on the target rotation and translation matrix, the multi-axis robotic arm is controlled to grasp the part to be grasped. When the device is in its initial orientation, determining the target rotation and translation matrix based on the part image information, the robotic arm coordinate system, and the camera coordinate system includes: With the multi-axis robotic arm in its initial posture, the relative position information between the head base of the multi-axis robotic arm and the camera device is determined based on the part image information. Based on the relative position information of the device, determine the first rotation matrix and the first translation vector between the robotic arm coordinate system and the camera coordinate system; The target rotation and translation matrix is ​​determined based on the first rotation matrix and the first translation vector; The step of determining the relative position information of the multi-axis robotic arm device and the camera device based on the part image information includes: Based on the image information of the parts, determine the camera calibration sample points in the camera device; Determine the coordinate information of the camera calibration sample point in the robotic arm coordinate system; Based on the coordinate information of the sample points, the relative position information of the devices between the head base of the multi-axis robotic arm and the camera device is determined.

2. The method according to claim 1, characterized in that, The robotic arm coordinate system and the camera coordinate system are constructed in the following manner: For the camera device, the optical center of the imaging plane of the camera device is taken as the origin O. The X-axis and Y-axis are constructed on the imaging plane of the camera device, and the Z-axis is constructed in the optical axis direction of the camera device. The X-axis and Y-axis are perpendicular to each other, and the Z-axis is perpendicular to the XOY plane. For the front end base of the robotic arm device, based on the ground, an X-axis parallel to the ground is constructed in the robotic arm coordinate system. A Y-axis perpendicular to the X-axis is constructed in the same plane containing the X-axis. A Z-axis perpendicular to both the X-axis and the Y-axis is constructed at the intersection of the X-axis and the Y-axis. The front end base is perpendicular to the ground.

3. The method according to claim 1, characterized in that, When the device is in another orientation, determining the target rotation and translation matrix based on the part image information, the robotic arm coordinate system, and the camera coordinate system includes: When the multi-axis robotic arm is in other postures, determine the axis movement information of the multi-axis robotic arm; Based on the arm movement information and the part image information, determine the second rotation matrix and the second translation vector between the robot arm coordinate system and the camera coordinate system; The target rotation and translation matrix is ​​determined based on the second rotation matrix and the second translation vector.

4. The method according to claim 3, characterized in that, Determining the axis movement information of the multi-axis robotic arm includes: For each of the multi-axis robotic arm devices, the rotation angle information of the arm around the X-axis, Y-axis and Z-axis of the robotic arm coordinate system, as well as the translation information along the arm direction, are obtained. Based on all the rotation angle information and the translation information, the axis movement information of the multi-axis robotic arm device is determined.

5. The method according to claim 4, characterized in that, The step of determining the second rotation matrix between the robotic arm coordinate system and the camera coordinate system based on the arm movement information and the part image information includes: Based on the rotation angle information in the arm movement information, construct the X-axis rotation matrix corresponding to the X-axis, the Y-axis rotation matrix corresponding to the Y-axis, and the Z-axis rotation matrix corresponding to the Z-axis. Based on the part image information, the X-axis rotation matrix, the Y-axis rotation matrix, and the Z-axis rotation matrix, a second rotation matrix is ​​determined between the robotic arm coordinate system and the camera coordinate system.

6. A robotic arm device for grasping parts, characterized in that, include: The part image information acquisition module is used to acquire part image information corresponding to the part to be grasped based on the camera device installed in the center of the end gripper of the multi-axis robotic arm device; The rotation and translation matrix determination unit is used to determine the target rotation and translation matrix based on the device posture of the multi-axis robotic arm device, according to the part image information, the robotic arm coordinate system and the camera coordinate system, wherein the device posture includes an initial posture and other postures; The robotic arm grasping execution module is used to control the multi-axis robotic arm device to grasp the part to be grasped according to the target rotation and translation matrix; The rotation and translation matrix determination module includes: The relative position information determination unit is used to determine the relative position information between the head end base of the multi-axis robotic arm and the camera device based on the part image information when the multi-axis robotic arm is in its initial posture. The relative position information parsing unit is used to determine the first rotation matrix and the first translation vector between the robot arm coordinate system and the camera coordinate system based on the relative position information of the device. A rotation and translation matrix determination unit is used to determine the target rotation and translation matrix based on the first rotation matrix and the first translation vector; The rotation and translation matrix determination unit is used for: Based on the image information of the parts, determine the camera calibration sample points in the camera device; Determine the coordinate information of the camera calibration sample point in the robotic arm coordinate system; Based on the coordinate information of the sample points, the relative position information of the devices between the head base of the multi-axis robotic arm and the camera device is determined.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the robotic arm grasping method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the robotic arm grasping method of any one of claims 1-5.

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