Compliant control method, system and equipment for composite robot and medium

Abstract

The invention provides a compliance control method, system and device for a composite robot and a medium. The method comprises the steps of obtaining a target motion track of the composite robot and discretizing the target motion track into a plurality of path discrete points; establishing an interaction relationship between the mechanical arm of the composite robot and the obstacle for collision interaction, and determining an adjustment direction and an adjustment granularity of the compliance control parameters according to the motion state information of the composite robot and the interaction relationship; performing iterative optimization on the compliance control parameter at each path discrete point based on the adjustment direction and the adjustment granularity to obtain an optimal compliance control parameter meeting a preset constraint condition and a corresponding cooperative control quantity; and generating a composite motion instruction sequence of the composite robot from the mobile platform to the mechanical arm according to the optimal compliance control parameters at the discrete points of each path and the corresponding cooperative control quantity. According to the technical scheme provided by the invention, stable and compliant control can be carried out on the composite robot in a complex and changeable working environment.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and more specifically, to a compliant control method, system, device, and medium for a composite robot. Background Technology

[0002] Robot control is the core support for enabling robots to perform tasks accurately. Its development stems from the dual drive of industrial automation and precision motion requirements. In the early days, it mainly relied on relay control and pneumatic / hydraulic drive, which could only complete simple repetitive actions with limited accuracy and flexibility. In recent years, with the integration of sensor technology (such as vision and force sensors) and artificial intelligence, the micron-level positioning requirements of robots have been effectively met. At the same time, relying on 5G and edge computing to achieve multi-robot collaborative control has further expanded the application boundaries of robot control technology.

[0003] In existing robot control, environmental parameters and the robot's own state (such as position and velocity) are first collected by sensor modules. Then, the controller performs real-time calculations and planning based on the task objective, perceived information, and internal models (such as kinematic and dynamic models) to generate the desired motion trajectory or force commands, thereby achieving robot control. However, in the compliant control of composite robots, existing robot compliant control methods have poor adaptability to uncertain contact surfaces that are easily deformable and weakly rigid, and it is difficult to balance motion accuracy and compliance in complex and ever-changing working environments, making it impossible to achieve smooth compliant control of composite robots. Therefore, how to achieve smooth compliant control of composite robots in complex and ever-changing working environments has become a difficult problem for the industry. Summary of the Invention

[0004] This application provides a compliant control method, system, device, and medium for composite robots, which can provide smooth and compliant control of composite robots in complex and ever-changing working environments.

[0005] In a first aspect, this application provides a compliant control method for a composite robot, comprising the following steps: The target motion trajectory of the composite robot when performing the task is obtained, and the target motion trajectory is discretized into multiple path discrete points; Based on the obstacle space information in the working environment of the composite robot, an interaction relationship is established between the composite robot arm and the obstacle collision. Based on the motion state information of the composite robot and the interaction relationship, the motion trend of the robotic arm is predicted and analyzed, thereby determining the adjustment direction and adjustment granularity of the compliance control parameters when the robotic arm moves. Based on the adjustment direction and the adjustment granularity, the compliant control parameters at each discrete point of the path are iteratively optimized. In each iteration, the cooperative control quantity of the mobile platform pose and the joint angle of the robotic arm is solved by the composite robot inverse kinematics model until the optimal compliant control parameters and the corresponding cooperative control quantity that satisfy the preset constraints are obtained. Based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point along the path, a composite motion command sequence from the mobile platform to the robotic arm is generated for the composite robot.

[0006] In some embodiments, discretizing the target motion trajectory into multiple path discrete points specifically includes: Set the discretization step size of the target motion trajectory; Select initial path discrete points and termination path discrete points on the target motion trajectory; Based on the initial path discrete point, the termination path discrete point, and the discretization step size, intermediate path discrete points are generated on the target motion trajectory, thereby obtaining multiple path discrete points.

[0007] In some embodiments, establishing the interaction relationship between the composite robot arm and obstacles based on obstacle spatial information in the composite robot's operating environment specifically includes: Extract the geometric feature parameters of obstacles based on the obstacle spatial information of the composite robot's operating environment; Establish the coordinate transformation relationship between the robotic arm's end effector and the obstacle; The minimum distance between the end effector of the robotic arm and the obstacle is calculated based on the geometric feature parameters and the coordinate transformation relationship, and the minimum distance is used as the interaction relationship for the collision between the composite robot robotic arm and the obstacle.

[0008] In some embodiments, predicting and analyzing the motion trend of the robotic arm based on the motion state information of the composite robot and the interaction relationship, and then determining the adjustment direction and granularity of the compliance control parameters during the robotic arm's movement, specifically includes: The pose of the mobile platform of the composite robot, the joint angles of the robotic arm, and the motion speed of each joint are collected as motion state information. Based on the motion state information, a forward kinematics model of the robotic arm is constructed, and then the real-time pose and motion speed of the end effector of the robotic arm are solved according to the forward kinematics model of the robotic arm. The rate of change of distance between the robotic arm end effector and the obstacle is obtained based on the interaction relationship; Based on the real-time pose, the movement speed, and the distance change rate, predict the predicted movement trajectory of the robotic arm within a future preset time window; The collision trend of the robotic arm is analyzed based on the predicted motion trajectory and the corresponding target motion trajectory, and the adjustment direction and granularity of the compliance control parameters are determined according to the collision trend.

[0009] In some embodiments, generating a composite motion command sequence from the mobile platform to the robotic arm for the composite robot based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point along the path specifically includes: Based on the temporal relationship of the discrete points along the path, the optimal compliant control parameters and cooperative control quantities of each discrete point along the path are sorted to form the composite control information of the composite robot. The composite control information is converted into control signals that conform to the driving protocols of the mobile platform and the robotic arm, thereby obtaining the composite motion command sequence of the composite robot.

[0010] In some embodiments, the target motion trajectory of the composite robot when performing a task can be obtained through a simulation database in the composite robot.

[0011] In some embodiments, the composite motion command sequence includes control signals for discrete points along different paths.

[0012] Secondly, this application provides a composite robot compliance control system, comprising: The acquisition module is used to acquire the target motion trajectory of the composite robot when performing the task, and to discretize the target motion trajectory into multiple path discrete points; The processing module is used to establish the interaction relationship between the composite robot arm and the obstacle based on the obstacle space information in the composite robot's working environment, and to predict and analyze the motion trend of the robotic arm based on the motion state information of the composite robot and the interaction relationship, thereby determining the adjustment direction and adjustment granularity of the compliance control parameters when the robotic arm moves. The processing module is also used to iteratively optimize the compliant control parameters at each discrete point of the path based on the adjustment direction and the adjustment granularity. In each iteration, the cooperative control quantity of the mobile platform pose and the joint angle of the robotic arm is solved by the inverse kinematics model of the composite robot until the optimal compliant control parameters and the corresponding cooperative control quantity that satisfy the preset constraints are obtained. The execution module is used to generate a sequence of composite motion commands from the mobile platform to the robotic arm for the composite robot based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point on the path.

[0013] Thirdly, this application provides a computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described composite robot compliant control method.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described compliant control method for a composite robot.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The compliant control method, system, device, and medium for composite robots provided in this application firstly acquire the target motion trajectory of the composite robot when performing a task, and discretize the target motion trajectory into multiple path discrete points; secondly, establish the interaction relationship between the composite robot's manipulator and obstacles based on the obstacle spatial information in the composite robot's working environment, and predict and analyze the motion trend of the manipulator based on the composite robot's motion state information and the interaction relationship, thereby determining the adjustment direction and granularity of the compliant control parameters during the manipulator's movement; then, iteratively optimize the compliant control parameters at each path discrete point based on the adjustment direction and the adjustment granularity, solving the cooperative control quantity of the mobile platform pose and the manipulator joint angle through the inverse kinematics model of the composite robot in each iteration until the optimal compliant control parameters and corresponding cooperative control quantity that satisfy the preset constraints are obtained; finally, generate a composite motion command sequence for the composite robot from the mobile platform to the manipulator based on the optimal compliant control parameters and corresponding cooperative control quantity at each path discrete point.

[0016] Therefore, this application demonstrates that it can provide smooth and compliant control for composite robots in complex and ever-changing working environments. Firstly, by acquiring the target motion trajectory of the composite robot and discretizing it into multiple path discrete points, the continuous trajectory, which is difficult to directly optimize parameters and coordinate control, can be transformed into discrete units that can be analyzed point by point, avoiding insufficient control accuracy caused by processing the entire continuous trajectory. Secondly, by establishing the interaction relationship between the robotic arm and obstacles based on obstacle spatial information and combining it with motion state information to determine the adjustment direction and granularity of compliant control parameters, the compliant control can closely adapt to the obstacle constraints of the working environment and the real-time motion state of the robot, avoiding collision risks or trajectory deviations caused by blind parameter adjustments, thus paving the way for subsequent... Parameter optimization provides precise guidance; then, based on the adjustment direction and granularity, the compliant control parameters of each path discrete point are iteratively optimized, and the cooperative control quantity is solved through the inverse kinematics model. The optimal compliant control parameters and cooperative control quantity that meet the preset constraints can be obtained, realizing the smooth compliant control of the composite robot; finally, a composite motion command sequence is generated based on the optimal parameters and cooperative control quantity of each path discrete point, which can ensure the coordinated action of the mobile platform and the robotic arm, realize the accurate tracking of the target trajectory of the composite robot and the effective control of compliant interaction, and ensure that the composite robot can stably complete the task; in summary, the technical solution provided in this application can perform smooth compliant control of the composite robot in complex and ever-changing working environments. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a composite robot compliant control method according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of interaction relationships according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of a composite robot compliant control system according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for implementing a compliant control method for a composite robot according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a compliant control method for a composite robot according to some embodiments of this application. The compliant control method for a composite robot mainly includes the following steps: In step 101, the target motion trajectory of the composite robot when performing the task is obtained, and the target motion trajectory is discretized into multiple path discrete points.

[0020] In practice, the target motion trajectory of the composite robot when performing a task can be obtained through the simulation database in the composite robot. The simulation database refers to the motion trajectory data generated and stored by simulating different tasks through simulation software based on the three-dimensional digital model of the composite robot (including the mobile platform and the robotic arm) and the work scene model. The target motion trajectory can be quickly retrieved according to the task requirements to provide the motion target data of the composite robot.

[0021] It should be noted that, in this application, the target motion trajectory refers to the complete motion path from the start state to the target state pre-planned by the composite robot to achieve the task. The target motion trajectory covers the coordinated motion parameters of the mobile platform and the robotic arm, providing a data basis for the subsequent compliant control of the composite robot.

[0022] In some embodiments, discretizing the target motion trajectory into multiple path discrete points can be achieved by the following steps: Set the discretization step size of the target motion trajectory; Select initial path discrete points and termination path discrete points on the target motion trajectory; Based on the initial path discrete point, the termination path discrete point, and the discretization step size, intermediate path discrete points are generated on the target motion trajectory, thereby obtaining multiple path discrete points.

[0023] In specific implementation, firstly, the discretization step size can be determined by the ratio of the total length of the target motion trajectory to the preset number of discrete points. The preset number of discrete points can be set according to actual needs and is not limited here. The discretization step size refers to the distance between two adjacent path discrete points along the target motion trajectory. Then, the starting point of the target motion trajectory is used as the initial path discrete point, and the ending point of the target motion trajectory is used as the ending path discrete point. The initial path discrete point refers to the coordinates of the starting point of the target motion trajectory, and the ending path discrete point refers to the coordinates of the ending point of the target motion trajectory. Finally, intermediate path discrete points are generated on the target motion trajectory based on the initial path discrete point, the ending path discrete point, and the discretization step size, thereby obtaining multiple path discrete points. That is, based on the initial path discrete point, the ending path discrete point, and the discretization step size, the trajectory between the initial path discrete point and the ending path discrete point is evenly divided according to the discretization step size, and the division points are used as intermediate path discrete points, thereby obtaining multiple path discrete points. The intermediate path discrete point refers to the spatial coordinate point generated between the initial path discrete point and the ending path discrete point according to the discretization step size.

[0024] It should be noted that, in this application, the path discrete points refer to all spatial coordinate points obtained after discretizing the target motion trajectory. By determining the path discrete points, the real-time performance of compliant control can be guaranteed. The control system can focus on the control quantity of the current discrete point in each control cycle based on the temporal relationship of the discrete points, and quickly adjust the motion commands in combination with environmental force feedback, so as to achieve the control objective of the robot to accurately track the trajectory and have flexible interaction capabilities in complex working environments.

[0025] In step 102, an interaction relationship between the composite robot arm and the obstacle is established based on the obstacle space information in the composite robot's working environment. The motion trend of the robotic arm is predicted and analyzed based on the motion state information of the composite robot and the interaction relationship, thereby determining the adjustment direction and granularity of the compliance control parameters when the robotic arm moves.

[0026] In some embodiments, reference Figure 2 As shown in the figure, this is an exemplary flowchart of determining interaction relationships according to some embodiments of this application. In this embodiment, the interaction relationship between the composite robot arm and the obstacle collision interaction can be established based on the obstacle spatial information in the composite robot's working environment by the following steps: First, in step 1021, the geometric feature parameters of the obstacles are extracted based on the obstacle spatial information of the composite robot's working environment; Then, in step 1022, the coordinate transformation relationship between the robotic arm end effector and the obstacle is established; Finally, in step 1023, the minimum distance between the end effector of the robotic arm and the obstacle is calculated based on the geometric feature parameters and the coordinate transformation relationship, and the minimum distance is used as the interaction relationship for the collision between the composite robot robotic arm and the obstacle.

[0027] In specific implementation, firstly, obstacle spatial information of the composite robot's operating environment is acquired through a vision sensor. This obstacle spatial information includes three-dimensional obstacle data. The geometric feature parameters of the obstacles are extracted from this three-dimensional obstacle data using the data processing tool Python. These geometric feature parameters include the length, width, height, and geometric center coordinates representing the area occupied by the obstacle. These geometric feature parameters are quantitative parameters used to describe the spatial dimensions and position of the obstacle. Then, using the robot arm's base coordinate system as a reference, and combining the joint angle data read by the robot arm's joint encoder, the pose matrix of the robot arm's end effector in the base coordinate system is calculated using forward kinematics. Simultaneously, based on the pre-completed calibration results of the world coordinate system and the robot arm's base coordinate system, a coordinate system from the world coordinate system of the obstacle to the robot arm is constructed. The homogeneous transformation matrix of the base coordinate system is used to perform coordinate transformation operations by substituting the geometric center coordinates of the obstacle into the homogeneous transformation matrix and then combining it with the pose matrix of the end effector to obtain the coordinate transformation relationship between the end effector of the robot arm and the obstacle. The coordinate transformation relationship refers to the mathematical mapping that realizes the position association between the two in the same coordinate system. Finally, based on the extracted geometric feature parameters, the spatial bounding box of the obstacle is constructed using the existing axis-aligned bounding box. At the same time, the corresponding spatial bounding box is constructed based on the three-dimensional solid parameters of the end effector of the robot arm under the coordinate transformation relationship. The Euclidean distance between all vertices on the surfaces of the two spatial bounding boxes is calculated and the minimum value is taken to obtain the minimum distance between the end effector of the robot arm and the obstacle. The minimum distance is used as the interaction relationship for the collision interaction between the composite robot arm and the obstacle.

[0028] It should be noted that the interaction relationship in this application refers to the relationship between the robotic arm and the obstacle that causes collisions. It is used to characterize the degree of collision risk between the robotic arm (especially the end effector and key links) and the obstacle. Determining the collision interaction relationship between the robotic arm and the obstacle can transform the abstract spatial information of the obstacle in the working environment into a quantitative basis that can guide control decisions. It provides accurate environmental feedback information for the adjustment of compliant control parameters and the optimization of motion coordination. This makes the compliant control of the composite robot no longer an independent adjustment detached from the environment, but a dynamic adaptive control that can be closely integrated with the interaction state of the obstacle. This ensures that the composite robot can stably perform its work tasks in complex obstacle environments, while avoiding rigid collisions with obstacles that could lead to equipment damage or work failure.

[0029] In some embodiments, predicting and analyzing the motion trend of the robotic arm based on the motion state information of the composite robot and the interaction relationship, and then determining the adjustment direction and granularity of the compliance control parameters during the robotic arm's movement, can be achieved through the following steps: The pose of the mobile platform of the composite robot, the joint angles of the robotic arm, and the motion speed of each joint are collected as motion state information. Based on the motion state information, a forward kinematics model of the robotic arm is constructed, and then the real-time pose and motion speed of the end effector of the robotic arm are solved according to the forward kinematics model of the robotic arm. The rate of change of distance between the robotic arm end effector and the obstacle is obtained based on the interaction relationship; Based on the real-time pose, the movement speed, and the distance change rate, predict the predicted movement trajectory of the robotic arm within a future preset time window; The collision trend of the robotic arm is analyzed based on the predicted motion trajectory and the corresponding target motion trajectory, and the adjustment direction and granularity of the compliance control parameters are determined according to the collision trend.

[0030] In specific implementation, firstly, the pose of the mobile platform is acquired using a wheeled odometer mounted on the mobile platform, and the joint angles of the robotic arm are acquired using encoders configured on each joint. Simultaneously, the motion velocity of each joint is calculated using the pulse signals output by the encoders combined with the sampling period, and these are integrated to obtain the motion state information of the composite robot. This motion state information refers to the set of parameters describing the position and pose of the mobile platform, the joint angles of the robotic arm, and the joint motion velocities. Secondly, a forward kinematics model of the robotic arm is constructed using the DH parameter method. The joint angles of the robotic arm from the motion state information are substituted into the forward kinematics model, and the motion state of the robotic arm end effector in the base coordinate system is solved by multiplying the homogeneous transformation matrices in the forward kinematics model. The real-time pose of the robotic arm is obtained, and the motion velocities of each joint are substituted into the Jacobian matrix of the forward kinematics. The motion velocity of the end effector (specifically including linear velocity and angular velocity) is calculated through matrix multiplication. The forward kinematics model of the robotic arm refers to the mathematical model for solving the pose and motion velocity of the end effector based on the joint angles of the robotic arm. The real-time pose refers to the posture of the end effector of the robotic arm in the base coordinate system at a certain moment, and the motion velocity refers to the velocity of the end effector of the robotic arm in the base coordinate system. Furthermore, based on the minimum distance data between the end effector of the robotic arm and the obstacle in the interaction relationship, a first-order numerical differential algorithm is used to calculate the distance between the end effector of the robotic arm and the obstacle by the ratio of the minimum distance difference between adjacent sampling times to the sampling period. The distance change rate is defined as the change in the minimum distance between the robotic arm's end effector and the obstacle per unit time. Then, the duration of a preset time window is determined (specifically, it can be set to match the sampling period of the composite robot control system), and this time window is evenly divided into multiple consecutive time steps (the specific time step can be set according to actual needs, and is not limited here). Using the real-time pose of the robotic arm's end effector as the prediction starting state, and combining the end effector's motion velocity with basic kinematic relationships, the position increment (determined by the product of the linear velocity and the time step) and attitude increment (determined by the product of the angular velocity and the time step) within each time step are calculated. When the distance... When the distance change rate is negative, the position increment of the corresponding time step is reduced according to the absolute value of the distance change rate. When the distance change rate is positive, the position increment and attitude increment are calculated according to the original motion speed. Starting from the predicted initial state, the position increment is superimposed on the predicted position of the previous moment and the attitude increment is superimposed on the predicted attitude of the previous moment for each time step to obtain the predicted pose at the end of each time step. The predicted poses corresponding to all time steps are connected in chronological order to form the predicted motion trajectory of the robot arm in the future within a preset time window. The predicted motion trajectory refers to the motion path of the robot arm end effector in the future. The preset time window refers to a fixed time interval used to predict the motion trend of the robot arm end effector.Finally, the predicted motion trajectory is compared point-by-point with the pre-planned target motion trajectory. The positional deviation between the two at the same moment is calculated. Simultaneously, the distance change rate is used to determine whether the deviation will further decrease (i.e., increase the collision tendency) or increase (i.e., decrease the collision tendency) the minimum distance between the end effector and the obstacle. If the positional deviation increases and the distance change rate is negative, the collision tendency is determined to intensify, and the adjustment direction of the compliance control parameters is set to increase the damping coefficient and the adjustment granularity is set to the magnitude of the deviation growth rate. If the positional deviation decreases and the distance change rate is positive, the collision tendency is determined to ease, and the adjustment direction is determined to maintain the original damping coefficient and the adjustment granularity is set to the original adjustment granularity. The collision tendency refers to the direction of change in the probability of future collisions of the robotic arm, based on the predicted motion trajectory.

[0031] It should be noted that in this application, the adjustment direction refers to the direction of adjustment of the compliant control parameter, and the adjustment granularity refers to the magnitude of the adjustment of the compliant control parameter. Determining the adjustment direction and adjustment granularity is the core link to achieve precise and flexible response of the robotic arm. Its role is to provide a clear and executable control basis for the dynamic adjustment of the compliant control parameter, avoid the blindness and over-correction of parameter adjustment, and enable the composite robot to avoid collision risks in real time through compliant control in complex environments, while maintaining the stability of movement and the accuracy of operation.

[0032] In step 103, the compliant control parameters at each discrete point on the path are iteratively optimized based on the adjustment direction and the adjustment granularity. In each iteration, the cooperative control quantities of the mobile platform pose and the joint angle of the robotic arm are solved by the inverse kinematics model of the composite robot until the optimal compliant control parameters and the corresponding cooperative control quantities that satisfy the preset constraints are obtained.

[0033] In some embodiments, the compliant control parameters at each discrete point on the path are iteratively optimized based on the adjustment direction and the adjustment granularity. In each iteration, the cooperative control quantities of the mobile platform pose and the robotic arm joint angles are solved using a composite robot inverse kinematics model until the optimal compliant control parameters and corresponding cooperative control quantities satisfying preset constraints are obtained. This can be achieved through the following steps: For each discrete point on the path, initialize the initial value of the compliant control parameter at the discrete point on the path; The initial value of the compliance control parameter is corrected based on the adjustment direction and the adjustment granularity to obtain the corrected compliance control parameter. The corrected compliant control parameters are input into the inverse kinematics model of the composite robot to solve the cooperative control quantities of the mobile platform pose and the joint angles of the robotic arm. The deviation between the actual motion trajectory of the robotic arm and the target motion trajectory is calculated based on the collaborative control variables. Determine whether the deviation meets the preset constraint conditions. If not, adjust the compliant control parameters again based on the deviation and adjustment granularity until the deviation meets the preset constraint conditions, and obtain the optimal compliant control parameters and the corresponding cooperative control quantity.

[0034] In specific implementation, firstly, for each discrete point on the path, the initial value of the compliant control parameter (i.e., the damping coefficient characterizing motion damping) is pre-set according to the design parameters of the composite robot and the task type. The initial value of the compliant control parameter is a pre-set reference value of the damping coefficient. Secondly, based on the determined adjustment direction and adjustment granularity, the initial value of the compliant control parameter is adjusted using a proportional correction method. That is, when the adjustment direction is to increase the damping coefficient, the damping coefficient is increased according to the adjustment granularity to obtain the corrected compliant control parameter. The corrected compliant control parameter refers to the damping coefficient parameter updated after the adjustment direction and adjustment granularity. Further, the corrected compliant control parameter is input as a constraint condition into the inverse kinematics model of the composite robot. The inverse kinematics model of the composite robot solves for the pose of the mobile platform and the joint angle of the robotic arm that enable the end effector to reach the discrete point on the path, thereby obtaining the cooperative control quantity of the pose of the mobile platform and the joint angle of the robotic arm. The inverse kinematics model of the composite robot refers to a mathematical model that solves for the pose of the mobile platform and the joint angle of the robotic arm based on the desired pose of the end effector. Specifically, it can adopt the existing technology. The robot inverse kinematics model is not limited here. The cooperative control quantity refers to the set of pose and joint angle parameters that satisfy the motion matching relationship between the mobile platform and the robotic arm. Then, the cooperative control quantity is substituted into the forward kinematics model of the robotic arm to calculate the actual motion trajectory point of the robotic arm end effector. The Euclidean distance between the actual motion trajectory point and the target motion trajectory point corresponding to the discrete point of the path is calculated to obtain the deviation between the actual motion trajectory and the target motion trajectory. The deviation refers to the degree of deviation between the actual motion trajectory and the target motion trajectory in spatial position. Finally, the preset constraint is the maximum allowable deviation value set based on the operation accuracy requirements. The calculated deviation is compared with the constraint. If the deviation exceeds the constraint, the adjustment granularity is adjusted proportionally according to the magnitude of the deviation, and the compliant control parameters are corrected again according to the original adjustment direction. The cooperative control quantity solution and deviation calculation are repeated until the deviation is less than or equal to the preset constraint. The compliant control parameters obtained at this time are the optimal compliant control parameters, and the corresponding cooperative control quantity is the optimal cooperative control quantity. The preset constraint is the maximum allowable deviation value set to ensure operation accuracy.

[0035] It should be noted that, in this application, the optimal compliant control parameter refers to the final damping coefficient that satisfies the preset constraints, and the cooperative control quantity refers to the pose of the mobile platform and the joint angle of the robotic arm that match the optimal compliant control parameter. The determination of the optimal compliant control parameter can accurately match the interaction requirements of different work scenarios. When facing an obstacle environment, the damping coefficient in the optimal compliant control parameter can buffer the potential collision force between the robotic arm and the obstacle, avoiding equipment damage or environmental disturbance caused by rigid impact. By determining the cooperative control quantity, the work task and environmental constraints can be accurately adapted, allowing the robotic arm to maintain the working posture of the end effector while avoiding obstacles.

[0036] In step 104, a composite motion command sequence for the composite robot from the mobile platform to the robotic arm is generated based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point on the path.

[0037] In some embodiments, generating a composite motion command sequence for the composite robot from the mobile platform to the robotic arm based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point along the path can be achieved through the following steps: Based on the temporal relationship of the discrete points along the path, the optimal compliant control parameters and cooperative control quantities of each discrete point along the path are sorted to form the composite control information of the composite robot. The composite control information is converted into control signals that conform to the driving protocols of the mobile platform and the robotic arm, thereby obtaining the composite motion command sequence of the composite robot.

[0038] In specific implementation, firstly, the optimal compliant control parameters and cooperative control quantities of each discrete point on the path are sorted according to the temporal relationship of the discrete points to form the composite control information of the composite robot. The composite control information refers to the optimal compliant control parameters and cooperative control quantities arranged in the motion sequence following the target trajectory. Finally, a protocol conversion module commonly used in the industrial control field is used to convert the digital parameters (such as the optimal compliant control parameters and cooperative control quantities) in the composite control information into control signals (i.e., voltage signals) that conform to the driving protocol of the mobile platform and the robotic arm. Then, the converted control signals are arranged in temporal order to obtain the composite motion command sequence of the composite robot. The composite motion command sequence contains control signals of different discrete points on the path.

[0039] It should be noted that the composite motion command sequence in this application refers to a complete set of control commands that can be issued to the composite robot by integrating them according to the motion timing. The determination of the composite motion command sequence is the key to the compliant control of the composite robot from decision-making to execution. The composite motion command sequence integrates the control signals of the mobile platform and the robotic arm according to the timing to ensure that the two move synchronously and avoid decoupling. The optimal compliant control parameters are embedded in the commands to achieve real-time adjustment to ensure safety buffering, so as to enable the composite robot to achieve effective compliant control.

[0040] In another aspect, in some embodiments, this application provides a composite robot compliant control system, with reference to... Figure 3 The figure is a schematic diagram of the structure of a composite robot compliant control system according to some embodiments of this application. The composite robot compliant control system includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire the target motion trajectory of the composite robot when performing the task, and to discretize the target motion trajectory into multiple path discrete points. The processing module 202 in this application is mainly used to establish the interaction relationship between the composite robot arm and the obstacle based on the obstacle space information in the composite robot's working environment, and to predict and analyze the motion trend of the robotic arm based on the motion state information of the composite robot and the interaction relationship, thereby determining the adjustment direction and adjustment granularity of the compliance control parameters when the robotic arm moves. The processing module 202 is further configured to iteratively optimize the compliant control parameters at each discrete point of the path based on the adjustment direction and the adjustment granularity. In each iteration, the cooperative control quantity of the mobile platform pose and the joint angle of the robotic arm is solved by the inverse kinematics model of the composite robot until the optimal compliant control parameters and the corresponding cooperative control quantity that satisfy the preset constraints are obtained. The execution module 203 in this application is mainly used to generate a composite motion command sequence from the mobile platform to the robotic arm of the composite robot based on the optimal compliant control parameters and the corresponding cooperative control quantities at each discrete point of the path.

[0041] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described composite robot compliant control method.

[0042] In some embodiments, reference Figure 4 The figure is a schematic diagram of the structure of a computer device implementing a compliant control method for a composite robot according to some embodiments of this application. The compliant control method for a composite robot in the above embodiments can... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0043] The processor 301 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the compliant control method for the composite robot in this application.

[0044] The communication bus 302 can be used to transmit information between the aforementioned components.

[0045] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0046] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the compliant control method for the composite robot can be achieved through the processor 301 and one or more software modules in the program code in the memory 303.

[0047] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0048] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0049] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0050] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described compliant control method for a composite robot.

[0051] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0052] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A compliant control method for a composite robot, characterized in that, Includes the following steps: The target motion trajectory of the composite robot when performing the task is obtained, and the target motion trajectory is discretized into multiple path discrete points; Based on the obstacle space information in the working environment of the composite robot, an interaction relationship is established between the composite robot arm and the obstacle collision. Based on the motion state information of the composite robot and the interaction relationship, the motion trend of the robotic arm is predicted and analyzed, thereby determining the adjustment direction and adjustment granularity of the compliance control parameters when the robotic arm moves. Based on the adjustment direction and the adjustment granularity, the compliant control parameters at each discrete point of the path are iteratively optimized. In each iteration, the cooperative control quantity of the mobile platform pose and the joint angle of the robotic arm is solved by the composite robot inverse kinematics model until the optimal compliant control parameters and the corresponding cooperative control quantity that satisfy the preset constraints are obtained. Based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point along the path, a composite motion command sequence from the mobile platform to the robotic arm is generated for the composite robot.

2. The method as described in claim 1, characterized in that, Discretizing the target trajectory into multiple path discrete points specifically includes: Set the discretization step size of the target motion trajectory; Select initial path discrete points and termination path discrete points on the target motion trajectory; Based on the initial path discrete point, the termination path discrete point, and the discretization step size, intermediate path discrete points are generated on the target motion trajectory, thereby obtaining multiple path discrete points.

3. The method as described in claim 1, characterized in that, Establishing the interaction relationship between the composite robot arm and obstacles based on the obstacle spatial information in the composite robot's operating environment specifically includes: Extract the geometric feature parameters of obstacles based on the obstacle spatial information of the composite robot's operating environment; Establish the coordinate transformation relationship between the robotic arm's end effector and the obstacle; The minimum distance between the end effector of the robotic arm and the obstacle is calculated based on the geometric feature parameters and the coordinate transformation relationship, and the minimum distance is used as the interaction relationship for the collision between the composite robot robotic arm and the obstacle.

4. The method as described in claim 1, characterized in that, Based on the motion state information of the composite robot and the interaction relationship, the motion trend of the robotic arm is predicted and analyzed, and then the adjustment direction and granularity of the compliance control parameters during the movement of the robotic arm are determined, specifically including: The pose of the mobile platform of the composite robot, the joint angles of the robotic arm, and the motion speed of each joint are collected as motion state information. Based on the motion state information, a forward kinematics model of the robotic arm is constructed, and then the real-time pose and motion speed of the end effector of the robotic arm are solved according to the forward kinematics model of the robotic arm. The rate of change of distance between the robotic arm end effector and the obstacle is obtained based on the interaction relationship; Based on the real-time pose, the movement speed, and the distance change rate, predict the predicted movement trajectory of the robotic arm within a future preset time window; The collision trend of the robotic arm is analyzed based on the predicted motion trajectory and the corresponding target motion trajectory, and the adjustment direction and granularity of the compliance control parameters are determined according to the collision trend.

5. The method as described in claim 1, characterized in that, Based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point along the path, a composite motion command sequence for the robot from the mobile platform to the robotic arm is generated, specifically including: Based on the temporal relationship of the discrete points along the path, the optimal compliant control parameters and cooperative control quantities of each discrete point along the path are sorted to form the composite control information of the composite robot. The composite control information is converted into control signals that conform to the driving protocols of the mobile platform and the robotic arm, thereby obtaining the composite motion command sequence of the composite robot.

6. The method as described in claim 1, characterized in that, The target motion trajectory of the composite robot when performing its tasks is obtained through the simulation database in the composite robot.

7. The method as described in claim 1, characterized in that, The composite motion command sequence includes control signals for discrete points along different paths.

8. A compliant control system for a composite robot, characterized in that, The system includes: The acquisition module is used to acquire the target motion trajectory of the composite robot when performing the task, and to discretize the target motion trajectory into multiple path discrete points; The processing module is used to establish the interaction relationship between the composite robot arm and the obstacle based on the obstacle space information in the composite robot's working environment, and to predict and analyze the motion trend of the robotic arm based on the motion state information of the composite robot and the interaction relationship, thereby determining the adjustment direction and adjustment granularity of the compliance control parameters when the robotic arm moves. The processing module is also used to iteratively optimize the compliant control parameters at each discrete point of the path based on the adjustment direction and the adjustment granularity. In each iteration, the cooperative control quantity of the mobile platform pose and the joint angle of the robotic arm is solved by the inverse kinematics model of the composite robot until the optimal compliant control parameters and the corresponding cooperative control quantity that satisfy the preset constraints are obtained. The execution module is used to generate a sequence of composite motion commands from the mobile platform to the robotic arm for the composite robot based on the optimal compliant control parameters and corresponding cooperative control quantities at each discrete point on the path.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the composite robot compliant control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the compliant control method for a composite robot as described in any one of claims 1 to 7.

Images