Composite robot and control method

Abstract

The application provides a composite robot and a control method. Motion coupling characteristics of a chassis and a mechanical arm of the composite robot are extracted from multi-modal operating state data, and a reference trajectory of the composite robot during collaborative control is determined based on the motion coupling characteristics and task requirements of the composite robot. Dynamic system parameters of the composite robot in a load change scenario are extracted from the multi-modal operating state data, and a lag control amount in the collaborative control process is determined through the dynamic system parameters. Motion stability indicators of the composite robot under environmental fluctuations are determined based on the interactive influence relationship between environmental disturbances in environmental perception data and motion accuracy of the composite robot. The control trajectory of the composite robot is corrected in real time according to the lag control amount and the motion stability indicators. The scheme of the application can realize real-time compensation for internal dynamic changes and active suppression of external disturbances in the collaborative control of the composite robot during high-precision operation under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and more specifically, to a composite robot and its control method. Background Technology

[0002] Composite robots are intelligent robot systems that integrate a mobile chassis and a robotic arm. With their ability to move flexibly and operate precisely, they have shown great application potential in fields such as industrial manufacturing, warehousing and logistics, and intelligent inspection. Their core lies in achieving coordinated control of the chassis and the robotic arm to complete complex mobile operation tasks.

[0003] In practical applications, composite robots typically need to handle multi-task operations in complex environments. Their operation involves multiple stages, including chassis motion planning, robotic arm trajectory generation, multi-sensor fusion, and actuator coordination. The coordination and stability of the system directly affect task execution efficiency and operational accuracy. However, in existing composite robot control methods, the chassis and robotic arm are mostly relatively independent control architectures, and collaborative control relies on fixed models or preset rules, lacking real-time perception and response capabilities. Especially when composite robots perform tasks requiring simultaneous movement and fine manipulation, attitude disturbances caused by chassis movement are transmitted to the robotic arm, resulting in end-effector trajectory deviation and positional errors. This makes it difficult for the control system to maintain high-precision trajectory tracking while ensuring the overall system's motion stability and anti-interference capabilities when facing multiple complex factors. This leads to decreased collaborative operation accuracy and motion instability in complex conditions. Therefore, how to achieve collaborative control that can compensate for internal dynamic changes in real time and actively suppress external interference during high-precision operations of composite robots in complex conditions has become a challenge for the industry. Summary of the Invention

[0004] This application provides a composite robot and a control method that enables collaborative control to achieve real-time compensation of internal dynamic changes and active suppression of external interference during high-precision operations performed by the composite robot under complex working conditions.

[0005] In a first aspect, this application provides a control method for a composite robot, comprising the following steps: Acquire multimodal operational status data of the composite robot; The motion coupling features between the composite robot chassis and the robotic arm are extracted from the multimodal operating state data. Based on the motion coupling features and the task requirements of the composite robot, the reference trajectory of the composite robot when performing cooperative control is determined. The dynamic system parameters of the composite robot under load change scenarios are extracted from the multimodal operation status data, and then the hysteresis control quantity in the cooperative control process is determined by the obtained dynamic system parameters. Collect environmental perception data within the operating area of ​​the composite robot, and determine the motion stability index of the composite robot under environmental fluctuations based on the interaction between environmental interference and the motion accuracy of the composite robot in the environmental perception data. The control trajectory of the composite robot is corrected in real time based on the hysteresis control quantity and the motion stability index.

[0006] In some embodiments, extracting the motion coupling features between the composite robot chassis and the robotic arm from the multimodal operating state data specifically includes: The chassis inertial measurement unit data and the robotic arm joint angle data are extracted from the multimodal operating status data; The dynamic coupling effect of the composite robot arm's motion on the chassis is determined based on the data from the chassis inertial measurement unit and the joint angle data of the robotic arm. The motion coupling characteristics of the composite robot chassis and the robotic arm are extracted from the aforementioned dynamic coupling effect.

[0007] In some embodiments, determining the reference trajectory of the composite robot during cooperative control based on the motion coupling characteristics and the task requirements of the composite robot specifically includes: Generate the initial task trajectory of the robotic arm end effector based on the task requirements of the composite robot; Using the aforementioned motion coupling characteristics as dynamic constraints, the initial task trajectory is optimized. The baseline trajectory for collaborative control of the composite robot is determined based on the trajectory optimization results.

[0008] In some embodiments, extracting the dynamic system parameters of the composite robot under load variation scenarios from the multimodal operating state data specifically includes: The joint current and angle data of the composite robot arm are obtained from the multimodal operating status data; Based on the joint current and angle data, dynamic system parameters of the composite robot under load variation scenarios are extracted.

[0009] In some embodiments, determining the hysteresis control quantity in the cooperative control process using the obtained dynamic system parameters specifically includes: The dynamic model of the composite robot is updated based on the obtained dynamic system parameters; Based on the updated dynamic model, the theoretical driving torque required for the robotic arm to track the reference trajectory of the composite robot for cooperative control is determined; The hysteresis control quantity in the cooperative control process is determined by the theoretical driving torque.

[0010] In some embodiments, determining the motion stability index of the composite robot under environmental fluctuations based on the interaction between environmental disturbances and the motion accuracy of the composite robot in the environmental perception data specifically includes: Extract environmental interference from the environmental perception data; The potential impact on the motion accuracy of the composite robot is determined based on the interaction between the environmental disturbances and the motion accuracy of the composite robot. The motion stability index of the composite robot under environmental fluctuations is determined based on the potential impact level.

[0011] In some embodiments, real-time correction of the composite robot control trajectory based on the hysteresis control quantity and the motion stability index specifically includes: Preliminary control commands are generated from the reference trajectory of the composite robot during collaborative control. The hysteresis control quantity is superimposed on the initial control command as a feedforward compensation term. The motion stability index is converted into feedback control parameters, and the initial control command is dynamically adjusted. The actuators of the composite robot are driven according to the final control commands obtained from the adjustment.

[0012] Secondly, this application provides a composite robot, including a control unit, the control unit comprising: The acquisition module is used to acquire multimodal operational status data of the composite robot; The processing module is used to extract the motion coupling characteristics of the composite robot chassis and the robotic arm from the multimodal operating state data, and determine the reference trajectory of the composite robot when performing cooperative control based on the motion coupling characteristics and the task requirements of the composite robot. The processing module is also used to extract dynamic system parameters of the composite robot under load change scenarios from the multimodal operating state data, and then determine the hysteresis control quantity in the cooperative control process through the obtained dynamic system parameters. The processing module is also used to collect environmental perception data within the working area of ​​the composite robot, and to determine the motion stability index of the composite robot under environmental fluctuations based on the interaction between environmental interference and motion accuracy of the composite robot in the environmental perception data. The execution module is used to correct the control trajectory of the composite robot in real time based on the hysteresis control quantity and the motion stability index.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device executes the above-described control method for the composite robot.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned control method for the composite robot.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In this application, multimodal operational status data of a composite robot is acquired; motion coupling characteristics of the composite robot chassis and robotic arm are extracted from the multimodal operational status data, and a reference trajectory for collaborative control of the composite robot is determined based on the motion coupling characteristics and the task requirements of the composite robot; dynamic system parameters of the composite robot under load variation scenarios are extracted from the multimodal operational status data, and the hysteresis control quantity in the collaborative control process is determined based on the obtained dynamic system parameters; environmental perception data within the working area of ​​the composite robot is collected, and the motion stability index of the composite robot under environmental fluctuations is determined based on the interaction between environmental disturbances and the motion accuracy of the composite robot in the environmental perception data; the control trajectory of the composite robot is corrected in real time according to the hysteresis control quantity and the motion stability index.

[0016] Therefore, in this application, firstly, based on the motion coupling characteristics and the task requirements of the composite robot, a reference trajectory is determined for the composite robot during collaborative control. This allows trajectory planning to adjust in advance for changes in system linkage, avoiding trajectory deviation of the robotic arm caused by chassis movement. This enables the composite robot to maintain motion coordination and trajectory continuity when performing complex tasks. Secondly, by determining the hysteresis control quantity in the collaborative control process through the obtained dynamic system parameters, the response lag caused by load fluctuations can be synchronously addressed during execution, avoiding the accumulation of posture deviations and the spread of trajectory errors, thus ensuring motion consistency and trajectory stability under internal dynamic changes of the composite robot. Then, based on the interaction between environmental interference and the motion accuracy of the composite robot in the environmental perception data, motion stability indicators of the composite robot under environmental fluctuations are determined, enabling... The system possesses proactive environmental risk perception and early protection capabilities, enabling the composite robot to maintain high stability and operational continuity in dynamic environments. This reduces error propagation and ensures the accuracy and reliability of the composite robot when performing tasks in complex environments. Finally, based on the hysteresis control quantity and the motion stability index, the control trajectory of the composite robot is corrected in real time. This allows the composite robot to suppress error introduction and deviation amplification during execution, achieving dynamic trajectory optimization and autonomous motion correction to cope with complex and multivariable environments and task conditions. This ensures the stability and high precision of continuous operation, thereby enabling the composite robot to possess intelligent adaptive and highly stable collaborative control capabilities under complex working conditions. In summary, this scheme can achieve collaborative control that compensates for internal dynamic changes in real time and actively suppresses external interference during high-precision operations of the composite robot under complex working conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary flowchart of a control method for a composite robot according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of hysteresis control quantities according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating real-time control trajectory correction according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a control unit according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a control method for a composite robot according to some embodiments of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] refer to Figure 1 The figure is an exemplary flowchart of a control method for a composite robot according to some embodiments of this application. The control method for the composite robot mainly includes the following steps: In step 101, the multimodal operating status data of the composite robot is acquired.

[0021] In specific implementation, the multimodal operational status data of the composite robot can be obtained in the following ways: Three-axis acceleration, angular velocity, and displacement data of the chassis can be collected using an inertial measurement unit and wheel encoder installed on the composite robot chassis; absolute angles and real-time drive currents of the joints can be collected using encoders and current sensors installed on each joint of the composite robot arm; simultaneously, point cloud image data, visual image data, and force / torque data of the interaction between the end effector and the outside world can be collected using the LiDAR, depth camera, and six-dimensional force / torque sensor integrated into the composite robot; finally, the composite robot's main control computer can perform millisecond-level synchronization and data encapsulation of all collected data through hardware interrupts or software timestamps to form a unified time-series multimodal operational status data stream; other methods can also be used in other embodiments, and no specific limitations are made here.

[0022] It should be noted that the multimodal operational state data in this application refers to a multi-source data set used to characterize the robot's own motion state and its interaction with the external environment during the operation of the composite robot.

[0023] In step 102, the motion coupling features of the composite robot chassis and the robotic arm are extracted from the multimodal operating state data, and the reference trajectory of the composite robot for collaborative control is determined based on the motion coupling features and the task requirements of the composite robot.

[0024] In some embodiments, extracting the motion coupling features between the composite robot chassis and the robotic arm from the multimodal operating state data can be achieved using the following steps: The chassis inertial measurement unit data and the robotic arm joint angle data are extracted from the multimodal operating status data; The dynamic coupling effect of the composite robot arm's motion on the chassis is determined based on the data from the chassis inertial measurement unit and the joint angle data of the robotic arm. The motion coupling characteristics of the composite robot chassis and the robotic arm are extracted from the aforementioned dynamic coupling effect.

[0025] In specific implementation, the chassis inertial measurement unit data and the robotic arm joint angle data can be parsed from the multimodal operating state data in the following way: by accessing the synchronized and encapsulated multimodal operating state data stream, and according to the predefined data structure identifier, the three-axis acceleration and three-axis angular velocity data sequence from the chassis inertial measurement unit can be separated as chassis inertial measurement unit data, and the absolute angle value data sequence from the encoders of each joint of the robotic arm can be separated as robotic arm joint angle data.

[0026] In specific implementation, the dynamic coupling effect of the composite robot arm's motion on the chassis, based on the chassis inertial measurement unit data and the robotic arm joint angle data, can be determined in the following way: the robotic arm joint angle data can be input into the known dynamic model of the composite robot, and the dynamic coupling force and torque acting on the chassis connection interface due to the motion of each link of the robotic arm can be solved by Newton-Euler iterative algorithm or computational dynamics method; at the same time, using the acceleration and angular velocity data measured by the chassis inertial measurement unit in the chassis inertial measurement unit data, the dynamic coupling force and torque calculated above can be verified and compensated online by momentum-based observer or inertial parameter identification method, and finally output a force and torque vector characterizing the net dynamic disturbance generated by the robotic arm motion on the chassis, that is, the coupling effect quantity, so as to obtain the dynamic coupling effect of the composite robot arm's motion on the chassis.

[0027] In specific implementation, the motion coupling characteristics of the composite robot chassis and the robotic arm can be extracted from the dynamic coupling effect in the following way: the coupling effect quantity in the dynamic coupling effect, that is, the force and torque vector of the net dynamic disturbance, can be subjected to time domain or frequency domain characteristic analysis; specifically, the mean amplitude, peak value, rate of change and main frequency components of the force / torque vector in a specific control cycle can be calculated, and the physical quantities that can quantify the intensity and dynamic characteristics of the motion interaction between the chassis and the robotic arm can be used as the motion coupling characteristics of the composite robot chassis and the robotic arm; other methods can also be used in other embodiments, which are not limited here.

[0028] It should be noted that the chassis inertial measurement unit data in this application refers to physical quantity data reflecting the motion state of the composite robot chassis itself; the manipulator joint angle data in this application refers to physical quantity data describing the real-time configuration of the composite robot manipulator in space; the dynamic coupling effect in this application refers to the physical characterization of the dynamic load and disturbance generated on the chassis by the motion of the manipulator through dynamic interaction during the motion of the composite robot, which is used to quantitatively reveal the dynamic interaction relationship between the chassis and the manipulator; the motion coupling feature in this application refers to the index that quantitatively characterizes the intensity of the motion interaction influence between the composite robot chassis and the manipulator, which is used to transform complex dynamic interactions into feature parameters that can be used for real-time control decision-making.

[0029] In some embodiments, determining the reference trajectory of the composite robot for cooperative control based on the motion coupling characteristics and the task requirements of the composite robot can be achieved through the following steps: Generate the initial task trajectory of the robotic arm end effector based on the task requirements of the composite robot; Using the aforementioned motion coupling characteristics as dynamic constraints, the initial task trajectory is optimized. The baseline trajectory for collaborative control of the composite robot is determined based on the trajectory optimization results.

[0030] In specific implementation, the initial task trajectory of the robotic arm end effector can be generated according to the task requirements of the composite robot in the following way: by parsing the task instructions issued by the upper control system of the composite robot, the target pose sequence to be reached by the robotic arm end effector and the corresponding motion time constraints are obtained; then, a smooth and continuous initial path of the robotic arm end effector can be generated in the joint space or Cartesian space based on the obtained target pose sequence and the corresponding motion time constraints using a fifth-order polynomial interpolation algorithm or a B-spline curve fitting method. This path can ensure that the robotic arm can pass through all critical path points from the starting point without collision and finally reach the target position, while satisfying the basic kinematic constraints. Finally, the generated path is used as the initial task trajectory of the robotic arm end effector.

[0031] In specific implementation, the initial task trajectory optimization can be achieved by using the motion coupling characteristics as dynamic constraints. This can be done in the following way: using the motion coupling characteristics as dynamic constraints, i.e., as key constraints of the optimization problem; establishing an optimization function with the goal of minimizing chassis attitude disturbance or system energy consumption, which can be achieved by using sequential quadratic programming or model predictive control algorithms. Under the premise of ensuring the key path points of the initial task trajectory, the motion angles, angular velocities, and angular accelerations of each joint of the robotic arm are replanned, thereby generating a new trajectory that can actively reduce the strong dynamic coupling of the robotic arm motion to the chassis.

[0032] In specific implementation, the reference trajectory for collaborative control of the composite robot based on the trajectory optimization results can be determined in the following way: the new trajectory output by trajectory optimization, namely the time series of the optimized joint angles, angular velocities, and angular accelerations of the robotic arm, can be used as the reference trajectory for collaborative control of the composite robot; this reference trajectory can be synchronously sent to the chassis motion controller and the robotic arm joint controller through the internal communication bus of the composite robot, serving as a unified reference command for their collaborative motion, so as to complete the preset task and reduce the dynamic coupling interference inside the composite robot through the active design of the trajectory shape; other methods can also be used to determine the trajectory in other embodiments, which are not limited here.

[0033] It should be noted that the task requirements in this application refer to the high-level instruction information of the specific operational objectives and execution conditions that the composite robot needs to complete; the initial task trajectory in this application refers to the preliminary trajectory planned based on the task requirements and kinematic constraints, describing the expected motion path of the robotic arm's end effector; the dynamic constraints in this application refer to the boundary conditions used to limit the dynamic behavior of the system during trajectory optimization to ensure the physical realizability of the motion; the trajectory optimization result in this application refers to the new motion trajectory generated after the initial task trajectory has been optimized by dynamic constraints; and the reference trajectory in this application refers to the unified reference instruction that coordinates the motion of the chassis and the robotic arm after global optimization. It is used as a common high-order tracking target for both the chassis controller and the robotic arm controller to fundamentally guide the composite robot to perform efficient and stable collaborative operations.

[0034] In step 103, dynamic system parameters of the composite robot under load change scenarios are extracted from the multimodal operating state data, and then the hysteresis control quantity in the collaborative control process is determined by the obtained dynamic system parameters.

[0035] In some embodiments, extracting the dynamic system parameters of the composite robot under load variation scenarios from the multimodal operating state data can be achieved by the following steps: The joint current and angle data of the composite robot arm are obtained from the multimodal operating status data; Based on the joint current and angle data, dynamic system parameters of the composite robot under load variation scenarios are extracted.

[0036] In specific implementation, the joint current and angle data of the composite robot arm can be obtained from the multimodal operation status data in the following way: by parsing the synchronously encapsulated multimodal operation status data stream, and according to the predefined data identifier, the real-time motor current readings from the servo drivers of each joint of the robot arm and the joint angle position data from the absolute encoders of each joint can be separated, thereby obtaining the one-to-one corresponding joint current and joint angle data.

[0037] In specific implementation, the dynamic system parameters of the composite robot under load variation scenarios can be extracted based on the joint current and angle data in the following manner: The obtained joint current and joint angle data, together with the known dynamic model of the robotic arm structure, can be input into a recursive least squares algorithm or a model reference adaptive control framework; then, by constructing a system observation matrix and calculating the error between the actual current measurement value and the model predicted current value, the dynamic system parameters that best reflect the current load state of the composite robot can be estimated and updated online iteratively. These dynamic system parameters include, but are not limited to, the equivalent mass of each link and load of the robotic arm, the center of gravity position, and the viscous friction coefficient of the joints; finally, the physical parameters that change with load variation identified in real time are output as the dynamic system parameters of the composite robot under load variation scenarios. Other methods can also be used in other embodiments, which are not limited here.

[0038] It should be noted that the joint current and angle data in this application refer to physical quantity data that characterize the driving state and real-time position configuration of each joint actuator of the composite robot arm; the dynamic system parameters in this application refer to indicators that describe the real-time changes in the dynamic characteristics of the composite robot under load changes. They are used to reflect the dynamic trend of the performance of the composite robot execution system with the change of external load, and provide a basis for calculating the feedforward control quantity to compensate for the dynamic effects caused by load changes.

[0039] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flowchart for determining the hysteresis control quantity in some embodiments of this application. In this embodiment, determining the hysteresis control quantity in the cooperative control process using the obtained dynamic system parameters can be achieved through the following steps: First, in step 1031, the dynamic model of the composite robot is updated based on the obtained dynamic system parameters; Secondly, in step 1032, the theoretical driving torque required for the composite robot arm to track the reference trajectory of the composite robot for collaborative control is determined based on the updated dynamic model. Finally, in step 1033, the hysteresis control quantity in the cooperative control process is determined by the theoretical driving torque.

[0040] In specific implementation, updating the dynamic model of the composite robot based on the obtained dynamic system parameters can be achieved in the following way: the obtained dynamic system parameters, including the equivalent mass of each link and load, the position of the center of gravity, and the joint friction coefficient, can be directly updated in the corresponding parameter values ​​of the composite robot dynamic model through parameter replacement; wherein, the dynamic model can be established using the Newton-Euler recursive formula or the Lagrange method, and its update process is achieved by writing the new parameter values ​​into the corresponding variables of the model calculation module, thereby ensuring that the dynamic model can reflect the dynamic characteristics of the system under the current load state in real time.

[0041] In specific implementation, the theoretical driving torque required for the composite robot arm to track the reference trajectory of the composite robot during collaborative control, based on the updated dynamic model, can be achieved in the following way: The updated dynamic model and the reference trajectory of the composite robot during collaborative control, i.e., the optimized time series of the angles, angular velocities, and angular accelerations of each joint of the robot arm, can be simultaneously input into the inverse dynamics calculation module; This module uses the Newton-Euler inverse dynamics algorithm or the torque calculation method to sequentially calculate the theoretical driving torque required by each joint of the composite robot arm to track the reference trajectory. This theoretical driving torque includes all torque components required to overcome system inertia, Coriolis force, centrifugal force, and gravity.

[0042] In specific implementation, the lag control quantity in the coordinated control process can be determined by the theoretical driving torque as follows: the calculated theoretical driving torque is compared with the reference torque calculated based on the nominal parameter dynamic model, and the difference is taken as the lag control quantity in the coordinated control process. This lag control quantity is the feedforward torque compensation value required to compensate for the change in system dynamic characteristics caused by load changes. It can be directly superimposed on the subsequent control command as a feedforward signal to offset the trajectory tracking lag caused by parameter changes. Other methods can also be used to determine this in other embodiments, which are not limited here.

[0043] It should be noted that the dynamic model in this application refers to the mathematical representation describing the mechanical behavior of the composite robot during motion. It is used to predict and calculate the theoretical force or torque required to generate the motion based on the composite robot's motion state (e.g., position, velocity, acceleration) and physical parameters. It is the theoretical basis for constructing a precise control model and torque feedforward compensation. The theoretical driving torque in this application refers to the ideal driving torque value that each joint of the composite robot's manipulator should theoretically output. It represents the torque reference that can achieve perfect trajectory tracking under the ideal model and is used to reflect the actual dynamic requirements of the composite robot under the current load and motion state. The hysteresis control quantity in this application refers to the feedforward torque compensation value specifically calculated to compensate for the hysteresis of the system's dynamic response caused by load changes, etc. It is used to actively offset the trajectory tracking error caused by the deviation between the actual system dynamic characteristics and the nominal model through the feedforward control channel, thereby improving the response speed and accuracy of the composite robot's cooperative control.

[0044] In step 104, environmental perception data within the working area of ​​the composite robot is collected, and the motion stability index of the composite robot under environmental fluctuations is determined based on the interaction between environmental interference and the motion accuracy of the composite robot in the environmental perception data.

[0045] In specific implementation, the environmental perception data within the working area of ​​the composite robot can be collected in the following ways: The environment within the working area can be horizontally scanned using the LiDAR integrated into the composite robot to obtain the distance and contour information of surrounding obstacles, generating two-dimensional or three-dimensional point cloud data. Simultaneously, RGB visual images and their corresponding depth information within the working area can be collected using a depth camera for identifying specific objects and reconstructing three-dimensional scenes. Furthermore, a six-dimensional force / torque sensor installed on the wrist of the robotic arm can detect in real time the force and torque data generated when the end effector contacts the workpiece or environment in three directions. Finally, the point cloud data from the LiDAR, the image sequence and depth data from the depth camera, and the wrist force / torque data are synchronized using a unified timestamp and used as the environmental perception data within the working area of ​​the composite robot. Other methods can also be used for data collection in other embodiments, and no specific limitations are specified here.

[0046] It should be noted that the environmental perception data in this application refers to a multi-source information set that characterizes the static obstacles, dynamic objects, and physical contact interactions within the working space of the composite robot, which affect its motion control accuracy and stability.

[0047] In some embodiments, determining the motion stability index of the composite robot under environmental fluctuations based on the interaction between environmental disturbances and the motion accuracy of the composite robot in the environmental perception data can be achieved through the following steps: Extract environmental interference from the environmental perception data; The potential impact on the motion accuracy of the composite robot is determined based on the interaction between the environmental disturbances and the motion accuracy of the composite robot. The motion stability index of the composite robot under environmental fluctuations is determined based on the potential impact level.

[0048] In specific implementation, the extraction of environmental interference from the environmental perception data can be achieved in the following way: the distance and approach speed of the nearest obstacle to the robot body can be calculated based on the point cloud data of the lidar in the environmental perception data. At the same time, the motion vector of the dynamic object can be identified based on the image sequence of the depth camera in the environmental perception data by optical flow method or background subtraction technology. In addition, the output of the six-dimensional force / torque sensor in the environmental perception data is read in real time, and the steady-state contact force and transient impact force caused by environmental contact are separated by low-pass filtering. Finally, the physical quantities that characterize the dynamic changes of the external environment obtained above are used as environmental interference.

[0049] In specific implementation, the potential impact on the motion accuracy of the composite robot based on the interaction between the environmental disturbance and the motion accuracy of the composite robot can be determined in the following way: an interaction model based on Lyapunov stability theory or impedance control framework can be established, with the environmental disturbance as the model input; the norm of the expected trajectory tracking error of the composite robot end effector or the rate of change of system kinetic energy under the current environmental disturbance can be calculated through the interaction model, and this calculated value can be quantified as the potential impact on the motion accuracy of the composite robot. This potential impact reflects the degree of threat that environmental disturbance poses to the stability and tracking accuracy of the composite robot system.

[0050] In specific implementation, the motion stability index of the composite robot under environmental fluctuations can be determined based on the potential influence degree in the following way: the potential influence degree can be input into a preset fuzzy inference system or piecewise linear mapping function, and a corresponding virtual damping coefficient can be dynamically generated based on the magnitude of the potential influence degree, or a compensation torque for offsetting the main interference direction can be directly calculated; this damping coefficient or compensation torque can be used as the motion stability index of the composite robot under environmental fluctuations. This motion stability index can be used as a real-time control parameter to enhance the dynamic robustness and anti-interference ability of the composite robot when facing environmental fluctuations. Other methods can also be used to determine this in other embodiments, which are not limited here.

[0051] It should be noted that, in this application, environmental disturbance refers to dynamic physical factors outside the workspace of the composite robot that may adversely affect its planned motion; the interactive influence relationship in this application refers to the dynamic causal relationship between environmental disturbance and the motion control accuracy of the composite robot that can be described by a mathematical model; the potential influence degree in this application refers to the degree of threat posed by environmental disturbance to the motion accuracy of the composite robot; and the motion stability index in this application refers to the index used by the composite robot to adjust controller parameters in real time to maintain system robustness when facing environmental fluctuations.

[0052] In step 105, the control trajectory of the composite robot is corrected in real time based on the hysteresis control quantity and the motion stability index.

[0053] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart of real-time correction of the control trajectory in some embodiments of this application. The real-time correction of the control trajectory of the composite robot based on the hysteresis control quantity and the motion stability index can be achieved by the following steps: Preliminary control commands are generated from the reference trajectory of the composite robot during collaborative control. The hysteresis control quantity is superimposed on the initial control command as a feedforward compensation term. The motion stability index is converted into feedback control parameters, and the initial control command is dynamically adjusted. The actuators of the composite robot are driven according to the final control commands obtained from the adjustment.

[0054] In practice, the generation of preliminary control commands from the reference trajectory during collaborative control of the composite robot can be achieved in the following way: the reference trajectory during collaborative control of the composite robot can be input into a proportional-integral-derivative controller or a computational torque controller; then, based on the error between the current actual joint position and the expected position of the reference trajectory, the controller generates preliminary current or torque commands for driving the motors of each joint of the composite robot through proportional, integral, and derivative operations or model-based feedforward calculations, and these commands serve as preliminary control commands.

[0055] In specific implementation, the hysteresis control quantity can be superimposed on the preliminary control command as a feedforward compensation term in the following way: In each control cycle of the composite robot, the calculated hysteresis control quantity, i.e. the feedforward torque compensation value, can be algebraically added to the preliminary control command as a feedforward compensation term; wherein, this addition operation is completed at the output stage of the controller, so that the final drive command contains both feedback components for trajectory tracking and deterministic feedforward components for compensating for dynamic load changes.

[0056] In specific implementation, the motion stability index can be converted into feedback control parameters, and the preliminary control command can be dynamically adjusted in the following way: the motion stability index is converted into feedback control parameters. If the motion stability index is a virtual damping coefficient, it is used to adjust the derivative gain parameter in the proportional-integral-derivative controller in real time, thereby changing the damping characteristics of the system to suppress oscillations. If the motion stability index is a compensation torque, it is directly vector-superimposed onto the preliminary control command that has already undergone feedforward compensation. This dynamic adjustment process enables the control command to adaptively change according to the strength of environmental disturbances.

[0057] In specific implementation, the actuator of the composite robot driven by the final control command obtained from the adjustment can be implemented in the following way: the final control command synthesized after feedforward compensation and feedback adjustment, that is, the total torque or current command value of each joint of the composite robot arm, is sent to the servo driver of each joint of the composite robot arm and the motion controller of the chassis through a real-time communication bus; then the driver and controller convert the electrical signal into physical action to drive the motor and actuator, so that the composite robot can accurately execute the cooperative control trajectory after real-time correction; other methods can also be used in other embodiments, which are not limited here.

[0058] It should be noted that the preliminary control command in this application refers to the basic control signal calculated based on the error between the reference trajectory and the current state, which does not yet include dynamic compensation; the feedforward compensation term in this application refers to the additional command component pre-calculated and injected into the control loop to actively offset the known dynamic lag of the system; the feedback control parameter in this application refers to the adjustable coefficient or additional command used to dynamically adjust the behavior of the controller according to the real-time state error and environmental disturbances; the final control command in this application refers to the complete control signal synthesized after feedforward compensation and feedback adjustment, which can directly drive the actuator of the composite robot; and the actuator in this application refers to the set of drive devices that convert the electrical signal carried by the final control command into the specific physical motion of the composite robot.

[0059] Furthermore, in another aspect of this application, in some embodiments, this application provides a composite robot, which includes a control unit, referenced... Figure 4 The figure is a schematic diagram of the structure of a control unit according to some embodiments of this application. The control unit 400 includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire the multimodal operating status data of the composite robot; Processing module 402 in this application is mainly used to extract the motion coupling characteristics of the composite robot chassis and the robotic arm from the multimodal operating state data, and to determine the reference trajectory of the composite robot when performing collaborative control based on the motion coupling characteristics and the task requirements of the composite robot. The processing module 402 described in this application is further used to extract dynamic system parameters of the composite robot under load change scenarios from the multimodal operating state data, and then determine the hysteresis control quantity in the collaborative control process through the obtained dynamic system parameters; The processing module 402 described in this application is also used to collect environmental perception data within the working area of ​​the composite robot, and to determine the motion stability index of the composite robot under environmental fluctuations based on the interaction between environmental interference and motion accuracy of the composite robot in the environmental perception data. The execution module 403 in this application is mainly used to correct the control trajectory of the composite robot in real time based on the hysteresis control quantity and the motion stability index.

[0060] The foregoing has detailed examples of the composite robot and control method provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0061] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device executes the above-described control method for the composite robot.

[0062] In some embodiments, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device implementing the control method of the composite robot of this application. The control method of the composite robot in the above embodiments can be achieved through… Figure 5 The computer device 500 shown is used to implement this, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 may be a terminal device, a server or a chip.

[0063] The processor 501 can be a general-purpose processor or a special-purpose processor. For example, the processor 501 can be a central processing unit (CPU). The CPU can be used to control the computer device 500, execute software programs, and process data from the software programs. The computer device 500 may also include a communication unit 505 for inputting (receiving) and outputting (transmitting) signals.

[0064] For example, computer device 500 may be a chip, communication unit 505 may be the input and / or output circuit of the chip, or communication unit 505 may be the communication interface of the chip, and the chip may be a component of terminal device, network device or other device.

[0065] For example, computer device 500 may be a terminal device or a server, and communication unit 505 may be a transceiver of the terminal device or the server, or communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0066] The computer device 500 may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0067] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0068] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0069] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described control method for the composite robot.

[0071] 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.

[0072] 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 control method for a composite robot, characterized in that, Includes the following steps: Acquire multimodal operational status data of the composite robot; The motion coupling features between the composite robot chassis and the robotic arm are extracted from the multimodal operating state data. Based on the motion coupling features and the task requirements of the composite robot, the reference trajectory of the composite robot when performing cooperative control is determined. The dynamic system parameters of the composite robot under load change scenarios are extracted from the multimodal operation status data, and then the hysteresis control quantity in the cooperative control process is determined by the obtained dynamic system parameters. Collect environmental perception data within the operating area of ​​the composite robot, and determine the motion stability index of the composite robot under environmental fluctuations based on the interaction between environmental interference and the motion accuracy of the composite robot in the environmental perception data. The control trajectory of the composite robot is corrected in real time based on the hysteresis control quantity and the motion stability index.

2. The method as described in claim 1, characterized in that, Extracting the motion coupling features between the composite robot chassis and the robotic arm from the multimodal operating state data specifically includes: The chassis inertial measurement unit data and the robotic arm joint angle data are extracted from the multimodal operating status data; The dynamic coupling effect of the composite robot arm's motion on the chassis is determined based on the data from the chassis inertial measurement unit and the joint angle data of the robotic arm. The motion coupling characteristics of the composite robot chassis and the robotic arm are extracted from the aforementioned dynamic coupling effect.

3. The method as described in claim 1, characterized in that, Based on the aforementioned motion coupling characteristics and the task requirements of the composite robot, the specific criteria for determining the reference trajectory of the composite robot during cooperative control include: Generate the initial task trajectory of the robotic arm end effector based on the task requirements of the composite robot; Using the aforementioned motion coupling characteristics as dynamic constraints, the initial task trajectory is optimized. The baseline trajectory for collaborative control of the composite robot is determined based on the trajectory optimization results.

4. The method as described in claim 1, characterized in that, Extracting the dynamic system parameters of the composite robot under load variation scenarios from the multimodal operating state data specifically includes: The joint current and angle data of the composite robot arm are obtained from the multimodal operating status data; Based on the joint current and angle data, dynamic system parameters of the composite robot under load variation scenarios are extracted.

5. The method as described in claim 1, characterized in that, Determining the lag control quantity in the coordinated control process using the obtained dynamic system parameters specifically includes: The dynamic model of the composite robot is updated based on the obtained dynamic system parameters; Based on the updated dynamic model, the theoretical driving torque required for the robotic arm to track the reference trajectory of the composite robot for cooperative control is determined; The hysteresis control quantity in the cooperative control process is determined by the theoretical driving torque.

6. The method as described in claim 1, characterized in that, The motion stability index of the composite robot under environmental fluctuations is determined based on the interaction between environmental disturbances and the motion accuracy of the composite robot in the environmental perception data. Specifically, this includes: Extract environmental interference from the environmental perception data; The potential impact on the motion accuracy of the composite robot is determined based on the interaction between the environmental disturbances and the motion accuracy of the composite robot. The motion stability index of the composite robot under environmental fluctuations is determined based on the potential impact level.

7. The method as described in claim 1, characterized in that, The real-time correction of the composite robot's control trajectory based on the hysteresis control quantity and the motion stability index specifically includes: Preliminary control commands are generated from the reference trajectory of the composite robot during collaborative control. The hysteresis control quantity is superimposed on the initial control command as a feedforward compensation term. The motion stability index is converted into feedback control parameters, and the initial control command is dynamically adjusted. The actuators of the composite robot are driven according to the final control commands obtained from the adjustment.

8. A composite robot, comprising a control unit, characterized in that, The control unit includes: The acquisition module is used to acquire multimodal operational status data of the composite robot; The processing module is used to extract the motion coupling characteristics of the composite robot chassis and the robotic arm from the multimodal operating state data, and determine the reference trajectory of the composite robot when performing cooperative control based on the motion coupling characteristics and the task requirements of the composite robot. The processing module is also used to extract dynamic system parameters of the composite robot under load change scenarios from the multimodal operating state data, and then determine the hysteresis control quantity in the cooperative control process through the obtained dynamic system parameters. The processing module is also used to collect environmental perception data within the working area of ​​the composite robot, and to determine the motion stability index of the composite robot under environmental fluctuations based on the interaction between environmental interference and motion accuracy of the composite robot in the environmental perception data. The execution module is used to correct the control trajectory of the composite robot in real time based on the hysteresis control quantity and the motion stability index.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory for storing computer programs, and the processor for calling and running the computer programs from the memory, causing the computer device to execute the control method of the composite robot according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the control method for the composite robot as described in any one of claims 1 to 7.

Images