Compound robot arm motion path planning method, device and computer equipment

Abstract

This application relates to a method, apparatus, computer device, storage medium, and computer program product for planning the motion path of a composite robot arm. The method includes: acquiring real-time chassis positioning data of a moving object; when an error is detected between the real-time chassis positioning data and preset target chassis positioning data, acquiring chassis positioning error data and initial motion path data of the robot arm; determining motion path compensation data based on the chassis positioning error data; updating the initial motion path data based on the motion path compensation data; obtaining target motion path data for the robot arm; and controlling the robot arm to move according to the path generated by the target motion path data. This method can improve the accuracy of robot arm path planning.

Description

Technical Field

[0001] This application relates to the field of machine control, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for planning the motion path of a composite robot arm. Background Technology

[0002] With the development of machine control technology, the application range of composite robot systems combining AGVs (Automated Guided Vehicles) and robotic arms is becoming increasingly wide. An AGV is a mobile transportation tool that loads goods or workpieces manually or automatically onto a vehicle, automatically travels to a designated location, and then unloads the goods or workpieces. It can be used in many fixed or non-fixed transportation situations. The robotic arm installed on the AGV can carry various execution systems at the end effector according to the needs of the work task, completing various application scenarios such as material handling, assembly and processing, and inspection.

[0003] In traditional technologies, when using AGV (Automated Guided Vehicle) robot systems to transport goods or workpieces, the navigation and positioning functions of these systems are required to achieve long-distance, trouble-free navigation. There are many navigation and positioning methods available, such as QR code navigation, LiDAR-SLAM navigation, and inertial navigation.

[0004] However, current navigation and positioning methods all have some positioning errors, which makes the movement path of the robotic arm mounted on the AGV inaccurate. This causes the robotic arm to have positional deviations when performing preset photo taking or grasping tasks, ultimately affecting the operation effect of the composite robot system. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for planning the motion path of a composite robot arm, which can improve the accuracy of the motion path of the composite robot arm, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a motion path planning method for a composite robot arm. The method includes:

[0007] Acquire real-time chassis positioning data of the moving object;

[0008] If an error is detected between the real-time chassis positioning data and the preset target chassis positioning data, the chassis positioning error data and the initial motion path data of the robotic arm of the moving object are obtained.

[0009] Based on the chassis positioning error data, determine the motion path compensation data;

[0010] Based on the motion path compensation data, the initial motion path data is updated to obtain the target motion path data of the robotic arm;

[0011] The robotic arm is controlled to move along the path generated by the target motion path data.

[0012] In one embodiment, determining motion path compensation data based on the chassis positioning error data includes:

[0013] Based on the chassis positioning error data, a positioning error pose transformation matrix is ​​constructed;

[0014] The motion path compensation data is obtained based on the positioning error pose transformation matrix.

[0015] In one embodiment, the chassis positioning error data includes translation error and rotation error;

[0016] The step of constructing a positioning error pose transformation matrix based on the chassis positioning error data includes:

[0017] Based on the translation error, construct the translation error matrix;

[0018] Based on the rotation error, construct the rotation error matrix;

[0019] The positioning error pose transformation matrix is ​​determined based on the translation error matrix and the rotation error matrix.

[0020] In one embodiment, the initial motion path data includes the starting end-effector pose of the robotic arm, and the step of updating the initial motion path data according to the motion path compensation data to obtain the target motion path data of the robotic arm includes:

[0021] Based on the positioning error pose transformation matrix, a spatial matrix transformation is performed on the initial end pose to determine the target initial end pose of the robotic arm.

[0022] The target motion path data includes the target's initial and final poses;

[0023] The target's initial and final poses are used as the starting position in the target's motion path data.

[0024] In one embodiment, the initial motion path data includes an initial pose transformation matrix, and the motion path compensation data includes the positioning error pose transformation matrix;

[0025] The step of updating the initial motion path data based on the motion path compensation data to obtain the target motion path data of the robotic arm includes:

[0026] Obtain the inverse matrix of the positioning error pose transformation matrix;

[0027] Based on the inverse matrix, pose compensation is performed on the initial pose transformation matrix to obtain the target pose transformation matrix used to characterize the target motion path;

[0028] Based on the target pose transformation matrix, the target motion path data is obtained.

[0029] Secondly, this application also provides a motion path planning device for a composite robot arm. The device includes:

[0030] The initial information acquisition module is used to acquire real-time chassis positioning data of the moving object;

[0031] The error acquisition module is used to acquire chassis positioning error data and initial motion path data of the robotic arm of the moving object when an error is detected between the real-time chassis positioning data and the preset target chassis positioning data.

[0032] The error compensation module is used to determine motion path compensation data based on the chassis positioning error data.

[0033] The path update module is used to update the initial motion path data according to the motion path compensation data to obtain the target motion path data of the robotic arm; and to control the robotic arm to move according to the path generated by the target motion path data.

[0034] In one embodiment, the error compensation module is further configured to construct a positioning error pose transformation matrix based on the chassis positioning error data; and obtain the motion path compensation data based on the positioning error pose transformation matrix.

[0035] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the embodiments of the above-described composite robot arm motion path planning methods.

[0036] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps in the embodiments of the above-described composite robot arm motion path planning methods.

[0037] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps in the embodiments of the above-described composite robot arm motion path planning methods.

[0038] The aforementioned method, apparatus, computer equipment, storage medium, and computer program for planning the motion path of a composite robot arm acquire real-time chassis positioning data of the moving object. When an error is detected between the real-time chassis positioning data and preset target chassis positioning data, the system acquires chassis positioning error data and initial motion path data including the starting and ending poses of the robot arm. Further, based on the chassis positioning error data, motion path compensation data is determined, and the initial motion path data is updated accordingly. Finally, the expected target motion path of the robot arm is obtained, thereby controlling the robot arm to move along the path generated by the target motion path data. This scheme fully considers the positioning error of the moving object and, when a positioning error is detected, can compensate for the initial motion path data based on the motion error, updating the motion path in a timely manner to adapt to changes in chassis position. This results in more accurate and expected target motion path data, enabling the robot arm to perform its task as intended. Therefore, this scheme can dynamically plan and adjust the robot arm's motion path, improving its accuracy. Attached Figure Description

[0039] Figure 1 This is an application environment diagram of the motion path planning method for a composite robot arm in one embodiment;

[0040] Figure 2 This is a flowchart illustrating the motion path planning method for a composite robot arm in one embodiment;

[0041] Figure 3 This is a flowchart illustrating the motion path planning method for a composite robot arm in another embodiment;

[0042] Figure 4 This is a flowchart illustrating the motion path planning method for a composite robot arm in yet another embodiment;

[0043] Figure 5 This is a flowchart illustrating the motion path planning method for a composite robot arm in another embodiment;

[0044] Figure 6 This is a reference schematic diagram of a motion path planning method for a composite robot arm in one embodiment;

[0045] Figure 7 Here is a flowchart illustrating the motion path planning method for a composite robot arm in one embodiment;

[0046] Figure 8 This is a structural block diagram of a motion path planning device for a composite robot arm in one embodiment;

[0047] Figure 9This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] The composite robot arm motion path planning method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown is illustrated. The moving object can be a robot, drone, or other moving device equipped with a robotic arm. The moving object 102 communicates with the control terminal 104. The data storage system can store motion path data generated when the control terminal 104 controls the motion path of the moving object. The data storage system can be integrated into the control terminal 104 or located in the cloud or on other servers. Specifically, the moving object 102 uploads real-time chassis positioning data to the control terminal 104. When the control terminal 104 detects an error in the real-time chassis positioning, it acquires the chassis positioning error data and the initial motion path data of the robotic arm of the moving object 102. Further, the control terminal 104 determines motion path compensation data based on the chassis positioning error data and updates the initial motion path data based on the motion path compensation data to obtain the target motion path data of the robotic arm. The moving object 102 can be, but is not limited to, various robots, drones, etc., and the control terminal 104 can be implemented using an independent controller or a controller cluster composed of multiple controllers. It is understood that the composite robot robotic arm motion path planning method provided in this application embodiment can also be applied to controllers built into the moving object itself.

[0050] In one embodiment, such as Figure 2 As shown, a motion path planning method for a composite robot arm is provided, which is then applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0051] S200: Acquire real-time chassis positioning data of the moving object.

[0052] The moving object can include, but is not limited to, robots, drones, or other moving objects equipped with robotic arms. Real-time chassis positioning data refers to the real-time position and attitude data of the moving object itself. Taking a robot as an example, real-time chassis positioning data includes the spatial position and orientation of the robot's chassis center. Real-time chassis positioning data can be obtained through various sensors or sensing units, or through measurements using LiDAR, inertial measurement units, etc.

[0053] Initial motion path data refers to the trajectory information of a moving object in space. For example, for a composite robot system containing a robot body and a robotic arm, the initial motion path data can include the direction, distance, and velocity of the robot body's movement in a spatial coordinate system, as well as the angle and position of the robotic arm relative to the robot body. The initial end-effector pose of the robotic arm describes the position and orientation information of the end effector in the initial stage, such as the coordinates and rotational state of the end effector in three-dimensional space. Initial motion path data can be pre-stored in the server's data storage system to guide the movement of the object in space.

[0054] S400, when it detects an error in the real-time chassis positioning data relative to the preset target chassis positioning data, acquires chassis positioning error data and the initial motion path data of the robotic arm of the moving object.

[0055] In the actual process of controlling a moving object, inaccurate positioning may occur due to measurement errors of the object's chassis sensors, environmental factors, or defects in the object's own components. For example, the robot may not accurately stop at the pre-set position, resulting in a positioning error. Therefore, when an error is detected in the real-time chassis positioning data of the moving object, it is necessary to obtain the chassis positioning error data.

[0056] For example, taking an AGV composite robot system as an example, if the robot's expected parking position is point A, but the actual parking position is point B, it indicates that there is an error in the robot's chassis positioning data. In this case, the chassis positioning error data can be obtained through positioning devices such as sensors and gyroscopes installed on the bottom of the robot.

[0057] S600 determines motion path compensation data based on chassis positioning error data.

[0058] Among them, motion path compensation data can be a set of adjustment values ​​used to correct the initial motion path data of the moving object in order to cope with chassis positioning errors.

[0059] Following the steps above, after obtaining the chassis positioning error data, the moving object can feed back the chassis positioning error data to the control terminal in real time. Because of the positioning error at this point, the original initial motion path data is no longer sufficient to control the moving object to reach the target position. Taking an AGV plus robotic arm composite robot system as an example, the robotic arm is mounted on the robot body. Therefore, the end effector of the robotic arm has a fixed spatial coordinate relative to the center of the robot chassis and a spatial orientation relative to the direction of the robot chassis. When the robot chassis positioning has an error, the spatial pose of the end effector of the robotic arm will also deviate, causing the robotic arm to fail to perform tasks such as grasping as expected. At this time, motion path compensation data for the moving object can be analyzed based on the chassis positioning error data to compensate for the initial motion path data of the moving object, enabling the moving object to move to the accurate target position. In this embodiment, the motion compensation data includes adjustments to the position of the moving object and adjustments to its direction. Furthermore, the motion compensation data may also include adjustments to the moving object's speed to ensure smooth path compensation.

[0060] S800 updates the initial motion path data based on the motion path compensation data to obtain the target motion path data of the robotic arm, and controls the robotic arm to move according to the path generated by the target motion path data.

[0061] The target motion path data refers to the motion path data obtained after compensating and calibrating the initial motion path data. In other words, the target motion path data is the motion path data that enables the robotic arm to accurately perform its task as expected, even when the moving object experiences chassis positioning errors.

[0062] Following the steps above, after determining the motion path compensation data, this data is incorporated into the initial motion path data. The compensation process may include compensating for parameters such as position coordinates, motion direction, and motion speed, and generating target motion path data. Furthermore, the target motion path data is applied to the moving object to promptly correct chassis positioning errors, more accurately plan the object's motion path, and thus control the robotic arm to move according to the path generated by the target motion path data.

[0063] The aforementioned method for planning the motion path of a composite robot arm acquires real-time chassis positioning data of the moving object, as well as initial motion path data including the starting and ending poses of the robot arm. Furthermore, when errors are detected in the real-time chassis positioning data, chassis positioning error data is acquired. Further, motion path compensation data is determined based on the chassis positioning error data, and the initial motion path data is updated accordingly. Finally, the target motion path of the robot arm is obtained, thereby controlling the robot arm to move along the path generated by the target motion path data. It is evident that this scheme fully considers the positioning error of the moving object and compensates for the initial motion path based on the motion error to obtain the target motion path, indicating that the robot arm can accurately reach the target position. Therefore, this scheme can improve the accuracy of robot arm motion path planning.

[0064] In one embodiment, such as Figure 3 As shown, S600 includes:

[0065] S620 constructs a positioning error pose transformation matrix based on chassis positioning error data, and obtains motion path compensation data based on the positioning error pose transformation matrix.

[0066] The positioning error pose transformation matrix can be used to describe the pose transformation relationship of a moving object during task execution due to chassis positioning errors, that is, to describe the difference between the actual position and orientation of the robotic arm's end effector and its desired position and orientation. In this embodiment, it can be represented by a 4*4 homogeneous transformation matrix.

[0067] In constructing the positioning error pose transformation matrix, we can first analyze the chassis positioning error data of the moving object, such as positional and angular deviations. Therefore, the constructed positioning error pose transformation matrix can represent adjustments to the position and angle of the moving object. Furthermore, motion path compensation data can include the positioning error pose transformation matrix. In practical applications, the positioning error pose transformation matrix contains information on the moving object's path adjustment, thus helping the moving object adapt to chassis positioning errors and accurately perform tasks according to the correct motion path.

[0068] In this embodiment, by constructing a positioning error pose transformation matrix based on chassis positioning error data, the difference between the actual position and posture of the robotic arm end effector and the desired position and posture can be accurately described. The magnitude and direction of the positioning error can be precisely quantified and represented, which can comprehensively and accurately describe the positioning error of the robotic arm and provide strong support for error analysis, compensation and control, thereby improving the positioning accuracy and performance of the robotic arm.

[0069] In one embodiment, the chassis positioning error data includes translational error and rotational error, such as Figure 4 As shown, S620 includes:

[0070] S622, construct the translation error matrix based on the translation error.

[0071] Chassis positioning error data includes the translational error of the chassis of the moving object, that is, an error occurred in the chassis's positioning in space. For example, if the direction of the moving object's movement is taken as the X-axis, the vertical direction of the traveling plane is taken as the Z-axis, and the Y-axis is determined using the right-hand rule, the translational error is denoted as... , , Based on this translation error, a translation error matrix is ​​constructed. The matrix expression is as follows:

[0072]

[0073] in, , and These represent the deviations of the moving object in the X, Y, and Z directions, respectively, assuming the ground is flat. Translation error matrix It represents the translational transformation of a moving object in space.

[0074] S624, construct the rotation error matrix based on the rotation error.

[0075] In addition to translational error, chassis positioning error can also include rotational error. In this embodiment, it refers to the rotational error about the Z-axis that occurs when the moving object stops at the parking position. The rotation angle is denoted as... Based on the rotation error, construct the rotation error matrix. The matrix expression is as follows:

[0076]

[0077] Wherein, in the above matrix expression It is the rotation angle in the clockwise direction. If the rotation angle is counterclockwise, then apply... replace .

[0078] Furthermore, if the moving object is a drone, rotational errors around the X and Y axes may also occur. For example, if the rotational error is generated around the X-axis, the rotation angle is denoted as... Then the corresponding rotation error matrix It can be represented as:

[0079]

[0080] If the rotation error is generated around the Y-axis, the rotation angle is denoted as... Then the corresponding rotation error matrix It can be represented as:

[0081]

[0082] Similarly, the above rotation angle and All rotation angles are clockwise. If the rotation angle is counterclockwise, then apply... , replace , .

[0083] S626, determine the positioning error pose transformation matrix based on the translation error matrix and the rotation error matrix.

[0084] After constructing the translation error matrix and the rotation error matrix, the translation error matrix and the rotation error matrix can be integrated to obtain the positioning error pose transformation matrix, which can be used to describe the translation error and rotation error of the moving object.

[0085] Following the steps above, after determining the translation error matrix and rotation error matrix, the positioning error pose transformation matrix can be further determined. Positioning error pose transformation matrix The expression is:

[0086]

[0087] Taking the rotation error as an example, the positioning error pose transformation matrix is... The specific expression is:

[0088]

[0089] The motion path compensation data includes the positioning error pose transformation matrix, which can be used as part of subsequent compensation to compensate for chassis positioning errors by dynamically adjusting the target position and orientation of the moving object.

[0090] In this embodiment, translation error matrices and rotation error matrices are constructed based on the translation and rotation errors during the positioning process of the moving object. Furthermore, the translation error matrices and rotation error matrices are integrated into a positioning error pose transformation matrix. This matrix is ​​applied to compensate and adjust the initial motion path data of the moving object, thereby enabling real-time pose adjustment of the moving object and improving the accuracy of the robotic arm's motion path planning.

[0091] In one embodiment, the initial motion path data includes the starting end-effector pose of the robotic arm, such as... Figure 5As shown, S800 includes:

[0092] S810 performs a spatial matrix transformation on the starting and ending poses based on the positioning error pose transformation matrix to determine the target starting and ending poses of the robotic arm, and uses the target starting and ending poses as the starting position in the target motion path data.

[0093] The target start-end pose is the start-end pose obtained after compensation and calibration of the start-end pose. This target start-end pose enables the robotic arm to accurately perform the grasping task as expected.

[0094] In this embodiment, the starting and ending poses of the moving object can be transformed by a spatial matrix based on the positioning error pose transformation matrix to determine the target starting and ending poses of the robotic arm.

[0095] For example, refer to the schematic diagram Figure 6 When the chassis positioning of the moving object is error-free, the chassis positioning coordinates are point A. Since the robotic arm of the moving object has a specific spatial position relative to the object itself, and this spatial position is related to the grasping task to be performed by the robotic arm, the end effector pose of the robotic arm at this point is recorded as the initial end effector pose. However, in reality, due to the chassis positioning error, the actual chassis positioning coordinates should be point B, and thus the end effector pose of the robotic arm will also change. This end effector pose of the robotic arm at this point is recorded as the target initial end effector pose. The positioning error pose transformation matrix is ​​then used. Multiplying the chassis positioning coordinates corresponding to point A by the coordinates of point B, we can determine the actual chassis positioning coordinates of the moving object. If the coordinates of point A are (…), then… Then the coordinates of point B can be expressed as:

[0096]

[0097] After determining the actual chassis positioning coordinates of the moving object, the target starting and ending poses of the moving object's robotic arm can be determined based on the spatial positional relationship between the robotic arm and the moving object itself.

[0098] Understandably, in the actual process of controlling a moving object, taking a robot as an example, the same principle applies. Figure 6When the robot chassis has no positioning error, its actual chassis position is point A, and point C is the target chassis position. In this case, the control unit sends motion path data to the robot, instructing the chassis to move from A to C, and the robot's robotic arm can complete the task. However, due to chassis positioning errors, the robot's actual chassis position is point B. If the robot moves according to the original motion path data, it will move to point D, causing the end effector pose of the robotic arm to deviate from the preset position, ultimately preventing it from performing the task. Through the calculations described above, the coordinates of the robot's true chassis position, point B, can be obtained. The control unit can then send motion path data to the robot, instructing the chassis to move from B to C, enabling the robotic arm to complete the task.

[0099] In this embodiment, the starting and ending poses of the moving object are corrected by the positioning error pose transformation matrix when there is a chassis positioning error, so as to obtain the target starting and ending poses. Moreover, the above process can be dynamic, and the moving object can also dynamically adapt to the chassis positioning error during the movement of the moving object. This helps the moving object to compensate for the chassis positioning error in a timely manner and improves the accuracy of the robotic arm's path planning.

[0100] In one embodiment, the initial motion path data includes an initial pose transformation matrix, and the motion path compensation data includes a positioning error pose transformation matrix, such as... Figure 7 As shown, S800 includes:

[0101] S820, obtain the inverse matrix of the positioning error pose transformation matrix.

[0102] S840, based on the inverse matrix, perform pose compensation on the initial pose transformation matrix to obtain the target pose transformation matrix used to characterize the target motion path. The target motion path data includes the target pose transformation matrix. Based on the target pose transformation matrix, the target motion path data is obtained.

[0103] Following the steps above, the initial motion path data includes an initial pose transformation matrix. However, due to errors in the chassis positioning of the moving object, if the moving object moves according to the motion path indicated by the initial pose transformation matrix, it will be unable to accurately reach the target position, thus preventing the robotic arm from performing its task as expected. Therefore, in this embodiment, the initial pose transformation matrix is ​​compensated for using a positioning error transformation matrix to determine the correct target pose transformation matrix, thereby guiding the moving object to move along an accurate motion path and reach the target position.

[0104] Specifically, the inverse matrix of the positioning error pose transformation matrix is ​​first obtained. This is because the presence of positioning error of the moving object's chassis at the starting and ending poses of the moving object's robotic arm causes deviations in the starting and ending poses, so the inverse matrix of the positioning error pose transformation matrix needs to be used for compensation.

[0105] For example, refer to the schematic diagram Figure 6 Following the above embodiments, the control terminal can send motion path data to the robot to instruct the robot chassis to move from point B to point C, enabling the robot's robotic arm to complete the corresponding task. The initial motion path data may include an initial pose transformation matrix. Used to instruct the robot chassis to move from point A to point C, from the schematic diagram. Figure 6 As can be seen from this, the spatial relationship between points A and C is consistent with the spatial relationship between points B and D. Therefore, the initial pose transformation matrix can be used. The spatial position change from point B to point D can be represented by the following formula:

[0106]

[0107] Furthermore, the spatial transformation matrix from point D to point C can be represented by the inverse of the spatial transformation matrix from point A to point B, expressed by the formula:

[0108]

[0109] in, Represents the pose transformation matrix of the positioning error. The inverse matrix of C, combined with the above relationship, gives the spatial transformation relationship between point C and point B:

[0110]

[0111] In this embodiment, the initial pose transformation matrix is ​​compensated by using the inverse matrix of the positioning error pose transformation matrix, thereby obtaining the target pose transformation matrix used to characterize the target motion path. This fully considers the errors generated by the moving object during the positioning process, and the initial pose transformation matrix in the initial motion path data is compensated based on the errors, thereby improving the accuracy of the robotic arm path planning.

[0112] To provide a clearer explanation of the motion path planning method for the composite robot arm provided in this application, a specific embodiment and accompanying drawings are described below. Figure 7 The specific embodiment includes the following steps:

[0113] S200: Acquire real-time chassis positioning data of the moving object.

[0114] S400, when it detects an error in the real-time chassis positioning data relative to the preset target chassis positioning data, acquires chassis positioning error data and the initial motion path data of the robotic arm of the moving object.

[0115] S622, construct the translation error matrix based on the translation error.

[0116] S624, construct the rotation error matrix based on the rotation error.

[0117] S626, determine the positioning error pose transformation matrix based on the translation error matrix and the rotation error matrix.

[0118] S810 performs a spatial matrix transformation on the starting and ending poses based on the positioning error pose transformation matrix to determine the target starting and ending poses of the robotic arm, and uses the target starting and ending poses as the starting position in the target motion path data.

[0119] S820, obtain the inverse matrix of the positioning error pose transformation matrix;

[0120] S840, based on the inverse matrix, perform pose compensation on the initial pose transformation matrix to obtain the target pose transformation matrix used to characterize the target motion path, and based on the target pose transformation matrix, obtain the target motion path data.

[0121] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0122] Based on the same inventive concept, this application also provides a composite robot arm motion path planning device for implementing the above-mentioned composite robot arm motion path planning method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the composite robot arm motion path planning device provided below can be found in the limitations of the composite robot arm motion path planning method described above, and will not be repeated here.

[0123] In one embodiment, such as Figure 8As shown, a composite robot arm motion path planning device 800 is provided, including: an initial information acquisition module 810, an error acquisition module 820, an error compensation module 830, and a path update module 840, wherein:

[0124] Initial information acquisition module 810 is used to acquire real-time chassis positioning data of the moving object;

[0125] The error acquisition module 820 is used to acquire chassis positioning error data and initial motion path data of the robotic arm of the moving object when an error is detected between the real-time chassis positioning data and the preset target chassis positioning data.

[0126] The error compensation module 830 is used to determine motion path compensation data based on chassis positioning error data.

[0127] The path update module 840 is used to update the initial motion path data according to the motion path compensation data, obtain the target motion path data of the robotic arm, and control the robotic arm to move according to the path generated by the target motion path data.

[0128] In one embodiment, the error compensation module 830 is further configured to construct a positioning error pose transformation matrix based on the chassis positioning error data, and obtain motion path compensation data based on the positioning error pose transformation matrix.

[0129] In one embodiment, the chassis positioning error data includes translation error and rotation error. The error compensation module 830 is further configured to construct a translation error matrix based on the translation error, construct a rotation error matrix based on the rotation error, and determine the positioning error pose transformation matrix based on the translation error matrix and the rotation error matrix.

[0130] In one embodiment, the initial motion path data includes the starting end-effector pose of the robotic arm. The path update module 840 is further configured to perform a spatial matrix transformation on the starting end-effector pose according to the positioning error pose transformation matrix to determine the target starting end-effector pose of the robotic arm, and use the target starting end-effector pose as the starting position in the target motion path data.

[0131] In one embodiment, the initial motion path data includes an initial pose transformation matrix, the motion path compensation data includes a positioning error pose transformation matrix, and the path update module 840 is further used to obtain the inverse matrix of the positioning error pose transformation matrix, perform pose compensation on the initial pose transformation matrix according to the inverse matrix, obtain a target pose transformation matrix for characterizing the target motion path, and obtain the target motion path data based on the target pose transformation matrix.

[0132] Each module in the aforementioned composite robot arm motion path planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0133] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores initial motion path data, positioning error data, etc. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a motion path planning method for a composite robot arm.

[0134] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0135] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the embodiments of the above-described composite robot arm motion path planning methods.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the embodiments of the above-described composite robot arm motion path planning methods.

[0137] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the embodiments of the composite robot arm motion path planning methods described above.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0139] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0140] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for planning the motion path of a composite robot arm, characterized in that, The method includes: Acquire real-time chassis positioning data of the moving object; If an error is detected between the real-time chassis positioning data and the preset target chassis positioning data, chassis positioning error data and initial motion path data of the robotic arm of the moving object are acquired; wherein, the initial motion path data includes an initial pose transformation matrix, which is used to indicate the transformation of the chassis of the moving object from the starting point pose to the target point pose. Based on the chassis positioning error data, a positioning error pose transformation matrix is ​​constructed; Based on the positioning error pose transformation matrix, motion path compensation data is obtained; Based on the motion path compensation data, the initial motion path data is updated to obtain the target motion path data of the robotic arm; The robotic arm is controlled to move along the path generated by the target motion path data; The step of updating the initial motion path data based on the motion path compensation data to obtain the target motion path data of the robotic arm includes: Obtain the inverse matrix of the positioning error pose transformation matrix; Based on the inverse matrix, pose compensation is performed on the initial pose transformation matrix to obtain the target pose transformation matrix used to characterize the target motion path; Based on the target pose transformation matrix, the target motion path data is obtained.

2. The method according to claim 1, characterized in that, The chassis positioning error data includes translation error and rotation error; The step of constructing a positioning error pose transformation matrix based on the chassis positioning error data includes: Based on the translation error, construct the translation error matrix; Based on the rotation error, construct the rotation error matrix; The positioning error pose transformation matrix is ​​determined based on the translation error matrix and the rotation error matrix.

3. The method according to claim 2, characterized in that, The initial motion path data includes the starting end pose of the robotic arm; The step of updating the initial motion path data based on the motion path compensation data to obtain the target motion path data of the robotic arm includes: Based on the positioning error pose transformation matrix, a spatial matrix transformation is performed on the initial end pose to determine the target initial end pose of the robotic arm. The target's initial and final poses are used as the starting position in the target's motion path data.

4. The method according to claim 1, characterized in that, The moving object includes robots or drones.

5. A motion path planning device for a composite robot arm, characterized in that, The device includes: The initial information acquisition module is used to acquire real-time chassis positioning data of the moving object; The error acquisition module is used to acquire chassis positioning error data and initial motion path data of the robotic arm of the moving object when an error is detected between the real-time chassis positioning data and the preset target chassis positioning data; wherein, the initial motion path data includes an initial pose transformation matrix, which is used to indicate the transformation of the chassis of the moving object from the starting point pose to the target point pose. The error compensation module is used to construct a positioning error pose transformation matrix based on the chassis positioning error data; and to obtain motion path compensation data based on the positioning error pose transformation matrix. The path update module is used to update the initial motion path data according to the motion path compensation data to obtain the target motion path data of the robotic arm; and control the robotic arm to move according to the path generated by the target motion path data. The path update module is further configured to obtain the inverse matrix of the positioning error pose transformation matrix; perform pose compensation on the initial pose transformation matrix according to the inverse matrix to obtain a target pose transformation matrix for characterizing the target motion path; and obtain the target motion path data based on the target pose transformation matrix.

6. The apparatus according to claim 5, characterized in that, The error compensation module is further configured to construct a translation error matrix based on the translation error; construct a rotation error matrix based on the rotation error; and determine a positioning error pose transformation matrix based on the translation error matrix and the rotation error matrix.

7. The apparatus according to claim 5, characterized in that, The path update module is further configured to perform a spatial matrix transformation on the starting end pose according to the positioning error pose transformation matrix to determine the target starting end pose of the robotic arm; and to use the target starting end pose as the starting position in the target motion path data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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