Multi-robotic-arm collaborative control method, system and storage medium

Abstract

A multi-robotic-arm collaborative control method. The method comprises the steps of: according to process requirements of an execution task and on the basis of a plurality of robotic arms, constructing a multi-arm coupled dynamics model, the multi-arm coupled dynamics model being used for position decomposition and force distribution for the plurality of robotic arms; on the basis of the multi-arm coupled dynamics model, decomposing the execution task to form a plurality of sub-task segments; on the basis of the sub-task segments, forming a collaborative controller group, the collaborative controller group consisting of controllers corresponding to a plurality of robotic arms under a master terminal; and, by means of the collaborative controller group, controlling each robotic arm to execute a path of a sub-task segment. The present application solves the problems of poor coordination and adaptability among various robotic arms when confronted with unknown working environments and uncertainties of task objectives during cooperative operation of the multiple robotic arms. Further provided are a multi-robotic-arm collaborative robot system and a storage medium.

Description

A method, system and storage medium for collaborative control of multiple robotic arms Technical Field

[0001] This application relates to the field of intelligent robot technology, and in particular to a multi-robotic arm collaborative control method, system and storage medium. Background Technology

[0002] Existing mobile robotic arms sometimes employ multiple robotic arms working together during operations. Multi-arm collaborative operation typically refers to multiple robotic arms coordinating and controlling to complete a complex task, such as grasping, assembling, or transporting. Multi-arm collaborative operation offers advantages such as high efficiency, high flexibility, and high reliability. However, it currently faces some challenges. Current multi-arm collaborative operations often involve operator control for predetermined tasks, exhibiting poor performance in autonomously handling unstructured work environments and uncertain task objectives.

[0003] Therefore, existing mobile robots, when operating in multi-arm collaborative mode, suffer from uncertainties in unknown working environments and task objectives, as well as poor coordination and adaptability among the arms.

[0004] Therefore, existing technologies still need to be improved and developed.

[0005] Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of this application is to provide a multi-manipulator collaborative control method, system and storage medium, which solves the problems of uncertainty in unknown working environment and task objectives, and poor coordination and adaptability among the manipulators when multiple manipulators work together in the prior art.

[0007] On the one hand, this application provides a multi-robotic arm cooperative control method, wherein the method includes the following steps:

[0008] Based on the process requirements of the task, a multi-arm coupled dynamics model is constructed, which is used for the position decomposition and force distribution of multiple robotic arms.

[0009] Based on the multi-arm coupled dynamics model, the execution task is decomposed into multiple sub-task segments;

[0010] Based on each subtask segment, a collaborative controller group is formed, which is composed of controllers corresponding to multiple robotic arms under a master end;

[0011] The collaborative controller group controls each robotic arm to execute the path of each sub-task segment.

[0012] Optionally, in the step of constructing a multi-arm coupled dynamics model of multiple robotic arms according to the process requirements of the task to be performed:

[0013] Establish a world coordinate system to enable information exchange between robots and between workpieces and robots;

[0014] Establish the base coordinate system of the robot base to determine the robot's installation pose;

[0015] Establish the tool coordinate system of the end effector to perform the task, and determine the pose of the end effector relative to the robot base;

[0016] Establish the workpiece's body coordinate system to determine the workpiece's position and orientation.

[0017] Optionally, in the step of constructing a multi-arm coupled dynamics model of multiple robotic arms according to the process requirements of the task to be performed:

[0018] By analyzing the dynamic characteristics and constraints of the multi-arm coupled dynamics model, the kinematic relationships between the motion joints, drive joints, and path planning of each robotic arm in the multi-arm coupled dynamics model are determined.

[0019] Based on kinematic relationships, the linear momentum and angular momentum of each robotic arm are obtained in the inertial coordinate system;

[0020] Based on the linear and angular momentum of each robotic arm, a Jacobi matrix kinematic equation for multi-robotic arm operation is constructed, and the effective workspace for multi-robotic arm collaborative operation is obtained through the Monte Carlo method.

[0021] Optionally, in the step of decomposing the task into multiple sub-task segments:

[0022] The multiple sub-task segments include: an initial segment, an execution segment, and a return segment. The initial segment, execution segment, and return segment are all coupled and controlled by multiple robotic arms and each has a degree of coupling. The degree of coupling of the initial segment and the return segment is less than that of the execution segment.

[0023] Optionally, in the step of decomposing the execution task into multiple sub-task segments, the planning of the initial segment includes the following steps:

[0024] Read the joint states of multiple robotic arms to obtain the current initial joint positions and the first planning time;

[0025] Based on the initial joint positions, the position of the initial target point is calculated using an inverse kinematics algorithm;

[0026] Based on the fifth-order polynomial programming algorithm, the planned path of the initial segment is obtained by using the initial joint position, the first planning time, and the position of the initial target point.

[0027] Optionally, in the step of decomposing the execution task into multiple sub-task segments, the planning of the execution segments includes the following steps:

[0028] Based on the location of the initial target point and the location of the predetermined execution point, plan the movement path of the workpiece;

[0029] Based on the process parameters of the task, determine the tool coordinate system of each robotic arm in collaborative operation, and plan the expected force of each robotic arm.

[0030] The tool coordinate system is transformed to the base coordinate system through homogeneous transformation.

[0031] Optionally, in the step of decomposing the execution task into multiple sub-task segments, the planning of the return segment includes the following steps:

[0032] Retrieve the home point location from the predefined parameters;

[0033] Read the joint states of multiple robotic arms, obtain the current position of the second joint and the second planning time;

[0034] Based on the fifth-order polynomial programming algorithm, the planned path of the initial segment is obtained by using the position of the second joint, the second planning time, and the position of the home point.

[0035] Optionally, in the step of controlling each robotic arm to execute the path of each sub-task segment via a collaborative controller group:

[0036] By applying a control signal to the master terminal, the master terminal controls each controller in the collaborative controller group to send communication signals. The control signal includes the applied force value, and the communication signal includes the component force value. The applied force value is composed of multiple component force values.

[0037] The system detects the feedback force of each robotic arm in the working environment, and each controller adjusts the corresponding robotic arm based on the feedback force.

[0038] On the other hand, this application also proposes a multi-arm collaborative robot system, which includes: multiple robotic arms and a processor;

[0039] The processor executes the multi-robotic arm cooperative control method described above to control multiple robotic arms to perform coordinated movements.

[0040] Thirdly, this application also proposes a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, which can be executed by one or more processors to realize the above-mentioned multi-robotic arm collaborative control method.

[0041] Beneficial Effects: This application presents a multi-arm collaborative control method, system, and storage medium. By constructing a multi-arm coupled dynamics model, it completes the decomposition of arm positions and force distribution. Based on the multi-arm coupled dynamics model and workpiece force decomposition, it proposes a multi-arm collaborative operation strategy oriented towards the process requirements of the task execution. By decomposing the task execution into multiple sub-task segments, segmented planning is achieved. Based on segmented planning, the consistency of collaborative control in the multi-arm robot system is analyzed. Due to the highly complex topology of centralized controller strategies, segmented planning greatly simplifies the redundancy of communication links. Therefore, based on segmented planning, a collaborative controller group is further constructed, forming a master-slave control mode, thereby realizing a distributed adaptive collaborative control strategy based on segmented planning. This allows the multi-arm collaborative mobile robot to autonomously coordinate and adaptively adjust among the arms when facing unknown working environments and uncertainties in task objectives, thereby controlling each arm to stably execute the paths of its respective sub-task segments. The multi-robotic arm collaborative control method in this scheme can realize multiple dynamic target tasks and multi-robotic arm collaborative strategies, thereby completing the corresponding work process for executing tasks. Attached Figure Description

[0042] Figure 1 is a flowchart of the main steps of a multi-robotic arm collaborative control method according to an embodiment of this application;

[0043] Figure 2 is a flowchart showing the detailed steps of a multi-robotic arm collaborative control method according to an embodiment of this application;

[0044] Figure 3 is a schematic diagram illustrating the principle of establishing a coordinate system in a multi-robotic arm collaborative control method according to an embodiment of this application;

[0045] Figure 4 is a schematic diagram of the decomposition principle of the execution task of a multi-robotic arm collaborative control method according to an embodiment of this application;

[0046] Figure 5 is a schematic diagram of the collaborative controller group of a multi-robotic arm collaborative control method according to an embodiment of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer and more explicit, the following detailed description of this application is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0048] Due to the uncertainty of unknown working environments and task objectives, existing mobile robots, when operating in multi-arm collaborative operations, not only suffer from poor coordination and adaptability among the arms, but also from collisions, unstructured working environments, and uncertain task objectives. Therefore, this application proposes a multi-arm collaborative control method. This method involves constructing dynamic models of connection and installation processes under various unknown environments, conducting research on connection and installation process trajectory planning, force compliance control, and related multi-arm collaborative control methods, to address the crucial issue of how to achieve cross-space data interaction and collaborative operation strategies based on multi-source sensing.

[0049] The specific embodiments of this application are as follows:

[0050] Example 1

[0051] As shown in Figure 1, this embodiment proposes a multi-robotic arm cooperative control method, applied to a robot system with multiple robotic arms (manipulators). This method enables multi-robotic arm cooperative operation, where multiple robotic arms work together through coordinated control to complete a complex task. Taking a multi-robotic arm construction robot as an example, the task could be curtain wall installation, workpiece welding, etc.

[0052] The multi-robotic arm cooperative control method in this embodiment mainly includes the following steps:

[0053] Step S100: Based on the process requirements of the task, construct a multi-arm coupled dynamics model, which is used for the position decomposition and force distribution of multiple robotic arms.

[0054] In practice, to enable construction robots to collaboratively connect and install building modules (such as curtain wall installation) and improve construction efficiency, a multi-arm coupled dynamics model needs to be constructed. This requires coupling the various robotic arms and establishing a coordinate system so that each robot, workpiece, robot base, task-performing tool, and workpiece being processed can be identified in position and orientation under a single standard. The specific process is as follows:

[0055] As shown in Figure 3, a world coordinate system is established to enable information exchange between robots and between the workpiece and the robot. A base coordinate system is established for the robot base to determine the robot's mounting pose. A tool coordinate system is established for the end effector to determine its pose relative to the robot base. A body coordinate system is established for the workpiece to determine its position and orientation. This allows for the position decomposition and force distribution of multiple robotic arms under a unified standard.

[0056] As shown in Figure 3, taking the example of a robot with four robotic arms mounted on one base: Establish a world coordinate system ∑ W (e.g., X in the illustration) W Y W Z W The resulting coordinate system enables information exchange between robots and between workpieces and robots; each robot establishes a base coordinate system. (e.g., X in the illustration) a Y a Z a The resulting coordinate system is used to determine the robot's mounting pose, and simultaneously establishes the tool coordinate system. (For example, the coordinate system formed by X1, Y1, and Z1 in the diagram) is used to describe the pose of the end effector relative to the robot base; a body coordinate system ∑ is established for the workpiece. A (Not shown in the illustration) to describe the position and orientation of the workpiece.

[0057] As shown in Figures 1 and 2, step S100 specifically includes the following steps:

[0058] S110. Construct a multi-arm coupled dynamics model and complete the position decomposition and force distribution of multiple robotic arms in the entire multi-arm coupled dynamics model.

[0059] S120. By analyzing the dynamic characteristics and constraints of the multi-arm coupled dynamic model, the kinematic relationships between the motion joints, drive joints, and path planning of each robotic arm in the multi-arm coupled dynamic model are determined.

[0060] S130. Based on kinematic relationships, obtain the linear momentum and angular momentum of each robotic arm in the inertial coordinate system.

[0061] S140. Based on the linear and angular momentum of each robotic arm, construct the Jacobian matrix kinematic equations for multi-robotic arm operation, and obtain the effective workspace for multi-robotic arm collaborative operation through the Monte Carlo method.

[0062] Through the above process, when the robotic arms perform actions according to the process requirements of the task within the effective workspace, collisions between multiple robotic arms will not occur, enabling stable collaborative work among them. This improves the coordination and adaptability between the robotic arms.

[0063] As shown in Figures 1 and 2, in step S200, based on the multi-arm coupled dynamics model, the task is decomposed into multiple sub-task segments.

[0064] As shown in Figure 4, after establishing the multi-arm coupled dynamics model, the effective workspace for multi-arm collaborative operation is obtained. Within the effective workspace, the execution tasks are decomposed to achieve segmented planning. Based on the segmented planning, the collaborative control consistency of the multi-arm robot is analyzed. Since the topology of the centralized controller strategy is very complex, the execution tasks are segmented into sub-task segments to achieve a distributed strategy, which greatly simplifies the redundancy of the communication link.

[0065] Taking the execution of welding or installation processes as an example, the task consists of multiple sub-task segments: an initial segment, an execution segment, and a return segment. The initial segment, execution segment, and return segment are all coupled and controlled by multiple robotic arms and each has a degree of coupling. The coupling degree of the initial segment and the return segment is less than that of the execution segment.

[0066] As shown in Figure 4, the execution process is divided into three segments. The first segment is the initial segment: the zero point or joint position moves to the initial target point (the initial point of the process execution segment). The second segment is the execution segment: the initial target point moves to the predetermined execution point (also the end point of the process trajectory, which is the position the workpiece or tool needs to reach, predetermined according to the requirements of the execution task). The third segment is the return segment: the end point of the process trajectory returns to the home point (the home point is a safety protection point or zero point, which is a pre-set point). The first and third segments are loosely coupled control, while the second segment is tightly coupled control. Both loose coupling and tight coupling are quantitative manifestations of coupling. Loose coupling indicates a low dependence strength between the robotic arms (small coupling), while tight coupling indicates a high dependence strength between the robotic arms (large coupling). Therefore, segmented planning control is implemented, and the consistency of collaborative control of the multi-arm robot system is analyzed. Since the topology of the centralized controller strategy is very complex, the distributed strategy greatly simplifies the redundancy of the communication link.

[0067] As shown in Figures 2 and 4, the planning of the initial segment in step S200 specifically includes the following steps:

[0068] Step S210: Read the joint states of multiple robotic arms and obtain the current initial joint position and the first planning time.

[0069] In the specific steps, the current initial joint position q is used. e The starting point is determined, which may be the zero point.

[0070] Step S211: Calculate the position of the initial target point using the inverse kinematics algorithm.

[0071] In the specific process, the initial target point is the initial point of the execution segment. The planning objective of the initial segment is to move from the zero point to the initial target point, preparing for the subsequent process trajectory of the execution segment. Obtain the initial joint position q.e Establish an inverse kinematics algorithm: q e =ikine( b T I [0]), where ikine is the inverse kinematic function, T I [0] is the initial target point. The initial target point can be calculated by using the inverse kinematics algorithm, based on the initial target point T. I [0] and initial joint position q e (Possibly the initial zero point position), thus obtaining the initial segment q init .

[0072] Step S212: Based on the fifth-order polynomial programming algorithm, obtain the planned path of the initial segment through the initial joint position, the first planning time, and the position of the initial target point.

[0073] A quintic polynomial programming algorithm is used to plan the motion trajectories of multiple robotic arms. Quintic polynomial trajectory planning can generate smooth motion curves, ensuring the continuity of the speed and acceleration of multiple robotic arms.

[0074] The planning of the execution phase includes the following steps:

[0075] Step S220: Based on the position of the initial target point and the position of the predetermined execution point, plan the movement path of the workpiece.

[0076] The initial target point T can be obtained through the above steps. I [0], and the location of the execution point is predetermined. For example, during curtain wall installation, the position that the installed workpiece needs to reach is the location of the execution point, which can be predetermined according to the building construction requirements. The initial target point T is known. I [0] and the position of the execution point can be used to obtain the movement path of the workpiece.

[0077] Step S221: Based on the process parameters of the task, determine the tool coordinate system of each robotic arm in the collaborative operation, and plan the expected force of each robotic arm.

[0078] In practice, the task to be executed can be a welding program or an installation program. Different tasks have different process requirements and parameters. During the execution along the workpiece's movement path, the tool coordinate system of each robotic arm involved in the process is determined. The desired forces of each robotic arm are then planned and allocated. For example, if three robotic arms are involved in the process, the planned tool coordinate systems would be: σ T1 σ T2 σ T3 The expected power of the plan is as follows: F d1 F d2 F d3 .

[0079] Step S222: Transform the tool coordinate system to the base coordinate system through homogeneous transformation.

[0080] In the specific process:

[0081] The tool coordinate system σ T1 Transform to the base coordinate system;

[0082] The tool coordinate system σ T2 Transform to the base coordinate system;

[0083] The tool coordinate system σ T2 Transform to the base coordinate system.

[0084] By transforming multiple robotic arms (such as the three robotic arms mentioned above) to the base coordinate system, the position of the end effector of each robotic arm can be determined, thereby enabling the cooperation between multiple robotic arms.

[0085] After determining the workpiece's movement path, the desired force of each robotic arm, and the position of the robotic arm's end effector, the workpiece can be moved along the movement path by controlling the coordinated action of the robotic arms.

[0086] The specific steps involved in planning the return segment are as follows:

[0087] Step S230: Retrieve the home point location from the predetermined parameters.

[0088] The home point is usually a pre-set zero point or safety point in the system, which can be retrieved directly from system parameters. The home point is the target point of the current return segment path: q e =q home .

[0089] Step S231: Read the joint states of multiple robotic arms, and obtain the current position of the second joint and the second planning time.

[0090] The second joint position is the end point of the execution segment, such as the predetermined execution point. The return segment's movement path starts from the second joint position q. b to target point q e (home point).

[0091] Step S232: Based on the fifth-order polynomial programming algorithm, obtain the planned path of the initial segment using the second joint position, the second planning time, and the home point position. The robotic arm motion trajectory planned by the fifth-order polynomial programming algorithm can generate a smooth motion curve, ensuring the continuity of speed and acceleration.

[0092] Therefore, through the segmented planning process described above, starting from the initial position, and through the planning of the execution and return segments, the task is completed and the robot returns to the initial position, thus successfully completing the collaborative execution process of the robotic arm. The entire process, through segmented planning of the initial segment, the process stage, and the return stage, achieves collaborative control of the multi-arm robot in processes such as welding and installation. The distributed adaptive collaborative control strategy effectively simplifies the complexity of the system and improves the flexibility and stability of control.

[0093] As shown in Figures 2 and 5, in step S300, a collaborative controller group is formed based on each sub-task segment, wherein the collaborative controller group is composed of controllers corresponding to multiple robotic arms under a master end.

[0094] In practice, after path planning is achieved, it is necessary to control the robotic arms to perform coordinated execution. This solution adopts a master-slave concept to build a collaborative controller group. The master end controls multiple robotic arms through the controller, thereby realizing the coordinated operation of the robotic arms at the slave end and planning the corresponding trajectory and force control according to the process requirements.

[0095] As shown in Figures 2 and 5, in step S400, the collaborative controller group controls each robotic arm to execute the path of each sub-task segment.

[0096] In practice, by applying control signals to the master end, the master end controls each controller in the collaborative controller group to send communication signals. The control signals include the applied force value, and the communication signals include component force values. The applied force value is synthesized from multiple component force values. This achieves reasonable force distribution, ensuring that the force applied by multiple robotic arms in the collaborative action during task execution meets the force value requirements, and guaranteeing the stability of task execution during the collaborative process.

[0097] Furthermore, during the collaborative execution of tasks by various robotic arms, the feedback force of each robotic arm in the working environment can be detected, and each controller adjusts the corresponding robotic arm based on the feedback force. Through the feedback force signals received by the controller, the controller can adjust the component force of the corresponding robotic arm in real time, so that the component force applied by each robotic arm can still meet the required force value.

[0098] As shown in Figure 5, in the specific collaborative control process: the operator controls the master terminal, and the input control signal includes force F. h (Applied force value). The master end communicates with multiple controllers, and each controller in turn communicates with its corresponding robotic arm, forming multiple slave ends. The robotic arms at the slave ends adjust the various applied force components based on environmental feedback forces F1 and F2. Thus, the collaborative controller group controls each robotic arm to execute the path of each sub-task segment.

[0099] Therefore, the method in this embodiment simplifies the redundancy of the communication link by adopting a distributed strategy. Based on the distributed form, a collaborative controller is constructed using the concept of one master and multiple slaves, forming a segmented planning distributed adaptive collaborative control strategy.

[0100] Step S500: Construct a database based on real-time perception information of multiple robotic arms and the external environment, and make autonomous learning and decisions to optimize the path of each robotic arm to execute each sub-task segment.

[0101] In the specific process, we explored the connection and installation strategies and corresponding operation procedures for multi-dynamic target multi-manipulator collaboration, constructed a database based on real-time perception information of multiple manipulators and the external environment, and used methods such as neural networks, hierarchical reinforcement learning, and meta-reinforcement learning, combined with experience playback and target network technologies, to realize the autonomous learning and decision-making of the multi-manipulator system, laying the foundation for the intelligent application of robots.

[0102] Therefore, this invention proposes a multi-robotic arm cooperative control method that can realize multi-robotic arm cooperative operation in building module connection and installation processes. A tightly coupled multi-arm system model is constructed to complete the multi-arm position decomposition and force distribution; based on the kinematic model of the multi-arm system and the workpiece force decomposition, a segmented planning distributed adaptive cooperative control strategy is adopted. During the welding and installation processes performed by the multi-robotic arm robots, the entire process is divided into three segments: the first segment is the initial segment (moving from zero point to the initial point of the process trajectory segment); the second segment is the process segment (executing the process trajectory from the initial point); and the third segment is the return segment (returning to zero point from the end point of the process trajectory). The first and third segments are loosely coupled control, while the second segment is tightly coupled, making the path planning process simpler and more efficient. A cooperative controller is constructed using a master-slave concept, forming a segmented planning distributed adaptive cooperative control strategy.

[0103] Example 2

[0104] This embodiment proposes a multi-arm collaborative robot system, including multiple robotic arms and a processor. The processor executes the multi-arm collaborative control method described above to control the multiple robotic arms to perform coordinated movements. In this embodiment, the multi-arm collaborative robot system can autonomously plan paths when facing unknown working environments and uncertainties in task objectives, enabling higher coordination and adaptive collaborative effects among the robotic arms. During multi-arm collaborative operations, this system explores connection and installation strategies and corresponding work processes for multi-dynamic target multi-arm collaboration, thereby realizing a segmented planning distributed adaptive collaborative control strategy for multiple robotic arms.

[0105] Example 3

[0106] This embodiment proposes a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, which can be executed by one or more processors to realize the above-mentioned multi-robotic arm collaborative control method.

[0107] In summary, the multi-robotic arm collaborative control method, system, and storage medium of this application explore connection and installation strategies and corresponding work processes for multi-dynamic target multi-robotic arm collaboration during multi-robot collaborative operations. This results in a distributed adaptive collaborative control strategy for multi-robotic arms based on segmented planning. The entire process, through segmented planning of the initial stage, process stage, and return stage, achieves collaborative control of multi-arm robots in welding, installation, and other processes. The distributed adaptive collaborative control strategy effectively simplifies the system's complexity and improves control flexibility and stability.

[0108] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-robotic arm collaborative control method, characterized in that, The method includes the following steps: Based on the process requirements of the task, a multi-arm coupled dynamics model is constructed based on multiple robotic arms, wherein the multi-arm coupled dynamics model is used for the position decomposition and force distribution of multiple robotic arms; Based on the aforementioned multi-arm coupled dynamics model, the execution task is decomposed into multiple sub-task segments; Based on each of the sub-task segments, a collaborative controller group is formed, wherein the collaborative controller group is composed of controllers corresponding to multiple robotic arms under a master end; The collaborative controller group controls each robotic arm to execute the path of each sub-task segment.

2. The multi-robotic arm cooperative control method according to claim 1, characterized in that, In the step of constructing a multi-arm coupled dynamics model of multiple robotic arms based on the process requirements of the task to be performed: Establish a world coordinate system to enable information exchange between robots and between workpieces and robots. Establish a base coordinate system for the robot base to determine the robot's installation pose. Establish a tool coordinate system for the end effector to determine the pose of the end effector relative to the robot base. Establish a body coordinate system for the workpiece to determine the position and orientation of the workpiece.

3. The multi-robotic arm cooperative control method according to claim 2, characterized in that, The step of constructing a multi-arm coupled dynamics model of multiple robotic arms based on the process requirements of the task to be performed also includes: By analyzing the dynamic characteristics and constraints of the multi-arm coupled dynamic model, the kinematic relationships between the motion joints, drive joints, and path planning of each robotic arm in the multi-arm coupled dynamic model are determined. Based on the kinematic relationship, the linear momentum and angular momentum of each robotic arm are obtained in the inertial coordinate system; Based on the linear and angular momentum of each robotic arm, a Jacobi matrix kinematic equation for multi-robotic arm operation is constructed, and the effective workspace for multi-robotic arm collaborative operation is obtained through the Monte Carlo method.

4. The multi-robotic arm cooperative control method according to claim 3, characterized in that, In the step of decomposing the execution task into multiple sub-task segments: The multiple sub-task segments include an initial segment, an execution segment, and a return segment. The initial segment, the execution segment, and the return segment are all coupled and controlled by multiple robotic arms and each has a degree of coupling. The degree of coupling of the initial segment and the return segment is less than the degree of coupling of the execution segment.

5. The multi-robotic arm cooperative control method according to claim 4, characterized in that, The step of decomposing the execution task into multiple sub-task segments includes the following steps in planning the initial segment: Read the joint states of multiple robotic arms to obtain the current initial joint positions and the first planning time; Based on the initial joint positions, the position of the initial target point is calculated using an inverse kinematics algorithm; Based on the fifth-order polynomial programming algorithm, the planned path of the initial segment is obtained by using the initial joint position, the first planning time, and the position of the initial target point.

6. The multi-robotic arm cooperative control method according to claim 1, characterized in that, The step of decomposing the execution task into multiple sub-task segments includes the following steps in planning the execution segments: Based on the location of the initial target point and the location of the predetermined execution point, plan the movement path of the workpiece; Based on the process parameters of the task, determine the tool coordinate system of each robotic arm in collaborative operation, and plan the expected force of each robotic arm. The tool coordinate system is transformed to the base coordinate system through homogeneous transformation.

7. The multi-robotic arm cooperative control method according to claim 1, characterized in that, The step of decomposing the execution task into multiple sub-task segments includes the following steps for planning the return segment: Retrieve the home point location from the predefined parameters; Read the joint states of multiple robotic arms, obtain the current position of the second joint and the second planning time; Based on the fifth-order polynomial programming algorithm, the planned path of the initial segment is obtained by using the position of the second joint, the second planning time, and the position of the home point.

8. The multi-robotic arm cooperative control method according to claim 5, characterized in that, In the step of controlling each robotic arm to execute the path of each sub-task segment through the cooperative controller group: By applying a control signal to the master terminal, the master terminal controls each controller in the collaborative controller group to send communication signals. The control signal includes an applied force value, and the communication signal includes component force values. The first applied force is composed of multiple component forces. The system detects the feedback force of each robotic arm in the working environment, and each controller adjusts the corresponding robotic arm based on the feedback force.

9. A multi-arm collaborative robot system, characterized in that, include: Multiple robotic arms and processors; The processor executes the multi-robotic arm cooperative control method as described in any one of claims 1-8 to control multiple of the... The robotic arm performs coordinated movements.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the multi-robotic arm collaborative control method as described in any one of claims 1-8.

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