Robot control method, device, apparatus, and storage medium

Abstract

The application relates to a robot control method, device, equipment and storage medium. The method comprises the following steps: acquiring current load data of each mechanical arm of a target robot; for each mechanical arm, determining the load type of the mechanical arm according to the current load data of the mechanical arm; jointly adjusting the driving parameters of each mechanical arm according to the load types of the mechanical arms; and controlling the movement of different mechanical arms according to the target driving parameters of the jointly adjusted mechanical arms. By using the above method, the load balancing adjustment of each mechanical arm is realized, which is beneficial to reducing the overload risk of the mechanical arm and improving the operation stability and reliability of the robot in a dynamic load scene.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and in particular to a robot control method, apparatus, device, and storage medium. Background Technology

[0002] With the development of robot control technology, robots can perform complex tasks such as handling, positioning, and installation during operation, improving automation levels and work efficiency. For example, a pipe rack support installation robot can achieve the handling, positioning, and installation of pipe rack supports through the coordinated control of multiple robotic arms.

[0003] However, the control methods for these robots often employ static allocation or manual parameter setting. For example, fixed drive parameters are pre-set for different work scenarios. This static control method is prone to problems such as excessive load on a single arm, affecting the operation of the overall system. Summary of the Invention

[0004] Therefore, it is necessary to provide a robot control method, device, equipment, and storage medium to address the aforementioned technical problems, thereby achieving robot load balancing.

[0005] In a first aspect, this application provides a robot control method, including:

[0006] Obtain the current load data of each robotic arm of the target robot;

[0007] For each robotic arm, determine the load type based on the current load data of the robotic arm;

[0008] Based on the load type of each robotic arm, the drive parameters of each robotic arm are jointly adjusted;

[0009] Based on the target drive parameters of each robotic arm after joint adjustment, the movement of different robotic arms is controlled.

[0010] In one embodiment, the drive parameters of each robotic arm are jointly adjusted according to the load type of each robotic arm, including: obtaining the current action type of the target robot; determining a collaborative adjustment strategy based on the current action type and the load type of each robotic arm; and jointly adjusting the drive parameters of each robotic arm according to the collaborative adjustment strategy.

[0011] In one embodiment, the load type includes a first load type and a second load type; the first load of the robotic arm of the first load type is greater than the second load of the robotic arm of the second load type; based on the current action type and the load type of each robotic arm, a collaborative adjustment strategy is determined, including at least one of the following: when the current action type is a handling action type, the collaborative adjustment strategy is determined as a first adjustment strategy, the first adjustment strategy including limiting the rate of change of acceleration of the robotic arm of the first load type and increasing the support force of the robotic arm of the second load type; when the current action type is a positioning action type, the collaborative adjustment strategy is determined as a second adjustment strategy, the second adjustment strategy including reducing the attitude adjustment speed of the robotic arm of the first load type and increasing the attitude adjustment participation of the robotic arm of the second load type; when the current action type is an installation action type, the collaborative adjustment strategy is determined as a third adjustment strategy, the third adjustment strategy including limiting the instantaneous torque change rate of the robotic arm of the first load type and increasing the bearing force and / or anti-deflection support force of the robotic arm of the second load type.

[0012] In one embodiment, the first load type includes a first subtype and / or a second subtype; the safety margin of the robotic arm of the first subtype is greater than the safety margin threshold of the robotic arm, and the safety margin of the robotic arm of the second subtype is not greater than the safety margin threshold of the robotic arm; the collaborative adjustment strategy further includes at least one of the following: increasing the driving torque and / or end-effector force of the robotic arm of the first subtype without exceeding a preset driving upper limit; reducing the rate of change of acceleration and / or rate of change of speed of the robotic arm of the first subtype; and reducing the speed limit parameter and / or acceleration limit parameter of the robotic arm of the second subtype.

[0013] In one embodiment, the driving parameters of each robotic arm are jointly adjusted according to a collaborative adjustment strategy, including: taking at least one of the following constraints as constraints: minimizing posture error, reducing load ratio difference, and ensuring that the synchronization error of each robotic arm's movement does not exceed a preset error threshold, the driving parameters of each robotic arm are jointly adjusted according to the collaborative adjustment strategy.

[0014] In one embodiment, for each robotic arm, the load type of the robotic arm is determined based on the current load data of the robotic arm, including: determining the load distribution of each robotic arm based on the current load data of each robotic arm; if the load distribution meets the preset load imbalance condition, determining the load ratio and safety margin of the robotic arm based on the current load data of the robotic arm; and determining the load type of the robotic arm based on the load ratio and safety margin.

[0015] In one embodiment, controlling the movement of different robotic arms based on the jointly adjusted driving parameters of each robotic arm includes: acquiring historical operating data of each robotic arm of the target robot; determining the predicted driving parameters of each robotic arm based on the historical operating data; determining the target control command of each robotic arm based on the jointly adjusted target driving parameters and predicted driving parameters of each robotic arm; and controlling the movement of the corresponding robotic arm using the target control command of the robotic arm.

[0016] Secondly, this application also provides a robot control device, comprising:

[0017] The acquisition module is used to acquire the current load data of each robotic arm of the target robot;

[0018] The determination module is used to determine the load type of each robotic arm based on its current load data.

[0019] The adjustment module is used to jointly adjust the drive parameters of each robotic arm according to the load type of each robotic arm;

[0020] The control module is used to control the movement of different robotic arms based on the target drive parameters of each robotic arm after joint adjustment.

[0021] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0022] Obtain the current load data of each robotic arm of the target robot;

[0023] For each robotic arm, determine the load type based on the current load data of the robotic arm;

[0024] Based on the load type of each robotic arm, the drive parameters of each robotic arm are jointly adjusted;

[0025] Based on the target drive parameters of each robotic arm after joint adjustment, the movement of different robotic arms is controlled.

[0026] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0027] Obtain the current load data of each robotic arm of the target robot;

[0028] For each robotic arm, determine the load type based on the current load data of the robotic arm;

[0029] Based on the load type of each robotic arm, the drive parameters of each robotic arm are jointly adjusted;

[0030] Based on the target drive parameters of each robotic arm after joint adjustment, the movement of different robotic arms is controlled.

[0031] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0032] Obtain the current load data of each robotic arm of the target robot;

[0033] For each robotic arm, determine the load type based on the current load data of the robotic arm;

[0034] Based on the load type of each robotic arm, the drive parameters of each robotic arm are jointly adjusted;

[0035] Based on the target drive parameters of each robotic arm after joint adjustment, the movement of different robotic arms is controlled.

[0036] The aforementioned robot control method, device, equipment, and storage medium, by acquiring the current load data of each robotic arm of the target robot, can perceive the actual load status of each robotic arm in real time, providing a data foundation for subsequent differentiated control. By determining the load type of each robotic arm based on its current load data, the load states of different robotic arms can be distinguished, providing a basis for the joint adjustment of drive parameters among multiple robotic arms. Based on the load state of each robotic arm, the load balance of each robotic arm is achieved through joint adjustment of its drive parameters. By controlling the movement of different robotic arms according to the target drive parameters of each arm after joint adjustment, the risk of overload of the robotic arms can be reduced, improving the operational stability and reliability of the robot in dynamic load scenarios. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is an application environment diagram of the robot control method in one embodiment;

[0039] Figure 2 This is a flowchart illustrating a robot control method in one embodiment;

[0040] Figure 3 This is a schematic diagram of the drive structure of the target robot in one embodiment;

[0041] Figure 4 This is a flowchart illustrating the motion control steps of a robotic arm in one embodiment;

[0042] Figure 5A This is a schematic diagram of the system structure of the robot's control system in one embodiment;

[0043] Figure 5B This is a schematic diagram of the data acquisition process of the load sensing and acquisition module in one embodiment;

[0044] Figure 5C This is a schematic diagram of the modeling and evaluation of the load state modeling module in one embodiment;

[0045] Figure 6 This is a flowchart illustrating the robot control method in another embodiment;

[0046] Figure 7 This is a structural block diagram of the robot's control device in one embodiment;

[0047] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

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

[0049] The robot control method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the input terminal of controller 101 is connected to load acquisition unit 102, and the output terminal of controller 101 is connected to execution unit 103. Load acquisition unit 102 is used to collect load data of execution unit 103 in the target robot; execution unit 103 may include multiple robotic arms. Controller 101 can control the movement of each robotic arm to collaboratively achieve actions such as handling, positioning, or installation.

[0050] For example, the target robot can be a multi-arm collaborative robot, such as a pipe rack support installation robot. The pipe rack support installation robot can achieve actions such as handling, lifting, positioning, drilling, installation, and fastening of pipe rack supports through the coordinated control of multiple robotic arms.

[0051] In recent years, with the increasing demand for automation in utility tunnel construction, support installation, and underground space engineering, utility tunnel support installation robots and related multi-arm robotic equipment have gradually entered the exploration stage of engineering applications. Among the control methods for utility tunnel support installation robots, static allocation or manually set parameters are often used. For example, fixed drive parameters are pre-set for different operating scenarios. This static control method is prone to problems such as excessive load on a single arm, affecting the operation of the overall system.

[0052] The robot control method provided in this application acquires the current load data of each robotic arm of the target robot, enabling real-time perception of the actual load-bearing status of each arm and providing a data foundation for subsequent differentiated control. By determining the load type of each robotic arm based on its current load data, the load states of different robotic arms can be distinguished, providing a basis for the joint adjustment of drive parameters among multiple robotic arms. Based on the load state of each robotic arm, the load balance of each robotic arm is achieved through joint adjustment of its drive parameters. By controlling the movement of different robotic arms according to the target drive parameters of each arm after joint adjustment, the risk of overload on the robotic arms can be reduced, improving the operational stability and reliability of the robot in dynamic load scenarios.

[0053] In one exemplary embodiment, such as Figure 2 As shown, a robot control method is provided, which can be applied to... Figure 1 Taking controller 101 as an example, the following steps are included:

[0054] S201. Obtain the current load data of each robotic arm of the target robot.

[0055] The current load data can be understood as real-time monitoring information used to characterize the load on the robotic arm at the current moment.

[0056] In some embodiments, target sensors can be set at target acquisition points of each robotic arm of the target robot to collect the current load data of each robotic arm.

[0057] For example, the target acquisition points may include at least one of the following: joint motor current acquisition points located at the joint servo motor positions of each robotic arm, joint torque acquisition points located at the output shaft positions of each joint, and end effector force acquisition points located at the end effector positions of each robotic arm. The target sensor may include at least one of the following: a current sensor located at the joint motor current acquisition point, a torque sensor located at the joint torque acquisition point, and a force sensor located at the end effector force acquisition point.

[0058] For example, current data collected by a current sensor, torque data collected by a torque sensor, and force data collected by a force sensor can be acquired. Based on the current data, torque data, and force data, the current load data of each robotic arm can be determined. In other words, current load data characterizing the current load condition of the robotic arm can be acquired through multi-source sensing. The current load data includes, but is not limited to, at least one of the following: joint motor current, joint output torque, end effector support force, and end effector clamping force. Optionally, the acquired current load data can be classified according to the robotic arm number and joint number, and preprocessed with dimension conversion and normalization to form a multi-arm load data set reflecting the current load state of each robotic arm, i.e., the current load data of each robotic arm.

[0059] Understandably, by acquiring the current load data of each robotic arm of the target robot, real-time and continuous perception of the load status of multiple robotic arms during operations such as support handling, lifting, positioning, hole alignment, installation, and fastening can be achieved, providing a data foundation for subsequent load allocation. Simultaneously, by normalizing the multi-source load perception data and combining it with the robotic arm number, joint number, and rated drive capacity to form a multi-arm load data set, the overall load distribution of the multiple robotic arms can be modeled and evaluated. This transforms dispersed force feedback data into a unified load assessment result, providing a quantitative basis for subsequent drive capacity allocation and control parameter adjustment, improving the real-time nature and targeting of load regulation.

[0060] S202. For each robotic arm, determine the load type of the robotic arm based on the current load data of the robotic arm.

[0061] The load type describes the load conditions of each robotic arm.

[0062] In some embodiments, the load type includes a first load type and a second load type; the first load of the robotic arm with the first load type is greater than the second load of the robotic arm with the second load type. The first load type can be understood as a high load type, and the second load type can be understood as a low load type.

[0063] Optionally, the first load type includes a first subtype and / or a second subtype; the safety margin of the robotic arm in the first subtype is greater than the safety margin threshold of the robotic arm, while the safety margin of the robotic arm in the second subtype is not greater than the safety margin threshold of the robotic arm. The first subtype can be understood as a high-load compensable type, meaning that although under high load, the robotic arm can bear part of the load to a certain extent or under certain conditions because its safety margin is greater than the safety margin threshold. The second subtype can be understood as a high-load sharing type, meaning that under high load and with a safety margin not greater than the safety margin threshold of the robotic arm, other robotic arms are needed to share part of the load. The low-load type referred to as the second load type can also be called a low-load assistable type. Because the load is low and the robotic arm's safety threshold is not greater than the safety threshold of the robotic arm, it can assist the robotic arm of the first load type, such as by providing auxiliary support and bearing additional load. In other words, the load type can include at least one of the following: high-load compensable type, high-load sharing type, low-load assistable type, and normal load-bearing type. The third load corresponding to the normal load-bearing type is between the first and second loads.

[0064] In some embodiments, the load distribution of each robotic arm can be determined based on the current load data of each robotic arm; if the load distribution meets the preset load imbalance condition, the load ratio and safety margin of the robotic arm can be determined based on the current load data of the robotic arm; and the load type of the robotic arm can be determined based on the load ratio and safety margin.

[0065] The load distribution can be understood as the relative relationship of load levels among the robotic arms. Load distribution can include the degree of load concentration, the magnitude of load differences, and whether there is a trend where one or more robotic arms have significantly higher loads than the others. For example, if the difference between the target load of the first robotic arm and the average load of other robotic arms is greater than a preset difference threshold, it can be determined that the load of that robotic arm is significantly higher than that of the others.

[0066] The load percentage, or load utilization rate, is used to characterize the ratio of the current load data of the robotic arm to its rated driving capacity. The load percentage can range from 0 to 1. The safety margin characterizes the remaining capacity of the robotic arm in its current operating state, away from the upper limit of its safe operating constraints. Referring to the foregoing, the load type can include at least one of the following: high load compensable type, high load requiring sharing type, low load assistable type, and normal load-bearing type. The details of load types have been explained above and will not be repeated here.

[0067] For example, the preset load imbalance condition may include at least one of the following: the load concentration degree is greater than the preset concentration degree; the load difference between any two robotic arms is greater than the first preset difference threshold; the difference between the target load of any robotic arm and the average load of other robotic arms is greater than the second preset difference threshold.

[0068] For example, the load percentage of each robotic arm can be determined based on the ratio of its current load data to its rated drive capacity.

[0069] For example, for each robotic arm, a safety margin can be determined based on the current load data of that robotic arm. The safety margin may include at least one of the following: residual power margin, residual torque margin, residual current margin, and temperature rise margin.

[0070] S203. Adjust the drive parameters of each robotic arm in combination according to the load type of each robotic arm.

[0071] The driving parameters may include at least one of the following: target driving torque, upper limit of driving power, speed limit parameter, acceleration limit parameter, end support force, clamping force, attitude adjustment speed, and installation alignment correction amount.

[0072] In some embodiments, a coordinated adjustment strategy can be determined based on the load type of each robotic arm; and the drive parameters of each robotic arm can be jointly adjusted according to the coordinated adjustment strategy. For example, the coordinated adjustment strategy may include at least one of the following: increasing the drive torque and / or end-effector force of the first subtype robotic arm, without exceeding a preset drive upper limit; reducing the rate of change of acceleration and / or rate of change of speed of the first subtype robotic arm; and reducing the speed limit parameter and / or acceleration limit parameter of the second subtype robotic arm. The preset drive upper limit may include at least one of a power upper limit, a torque upper limit, and a current upper limit.

[0073] For ease of understanding, the following description of the joint adjustment steps is provided as an example and should not be construed as limiting the specific adjustment process.

[0074] Let the load percentage of the i-th robotic arm within the current control cycle k be . Its rated driving capacity is The current actual load is ,but Let the safety margin of the i-th robotic arm be... , It is determined by the remaining power margin, remaining torque margin, remaining current margin, and temperature rise margin of the robotic arm. According to... and Determine whether the i-th robotic arm is in a high-load compensable type, a high-load need-to-share type, a low-load assistable type, or a normal load type.

[0075] For example, when the load type of a robotic arm is a high-load compensable type, it means that the current load ratio of the robotic arm is relatively high, but it still has sufficient remaining drive capacity and safety margin. At this time, limited drive compensation can be performed on the robotic arm. The compensation method can include increasing the target drive torque or end-effector support force command of the corresponding joint of the robotic arm, while reducing its rate of change of speed and rate of change of acceleration, without exceeding its power limit, torque limit, and current limit, so as to avoid the posture impact of the support components caused by simply increasing the drive force.

[0076] For example, when a robotic arm's load type is high-load requiring load sharing, it indicates that the robotic arm's load share is high and its safety margin is insufficient, and its drive burden should not be further increased. In this case, the speed limit and acceleration limit parameters of the robotic arm can be lowered, and based on the available drive capabilities of other robotic arms, some of the support force, clamping force, or attitude maintenance tasks can be distributed to robotic arms with lower load shares and higher safety margins, such as low-load assistable robotic arms. It is understood that the aforementioned load sharing is not simply reducing the load of the high-load robotic arm, but rather, under the premise that the posture error of the support components does not exceed a preset posture threshold and the synchronization error of each robotic arm's movements does not exceed a preset synchronization threshold, other robotic arms can jointly undertake the tasks of support handling, lifting, supporting, positioning, or fastening by increasing end-effector support force, adjusting clamping force, changing posture adjustment speed, or increasing alignment correction.

[0077] For example, when a robotic arm's load type is in the low-load assistable type, its assistance method can be determined based on the contact position between the robotic arm and the support component, the direction of movement, and the current action type. Optionally, if the current action type is a handling action, the low-load assistable robotic arm will prioritize assisting in lifting or lateral support; if the current action type is a positioning action, the low-load assistable robotic arm will prioritize attitude fine-tuning and alignment correction; and if the current action type is an installation action, the low-load assistable robotic arm will prioritize anti-deflection support or clamping and holding. Through this method, the low-load robotic arm can be configured to perform assistance matching the current action type, rather than indiscriminately increasing its load.

[0078] In other embodiments, the current action type of the target robot can be obtained; a collaborative adjustment strategy can be determined based on the current action type and the load type of each robotic arm; and the drive parameters of each robotic arm can be jointly adjusted according to the collaborative adjustment strategy.

[0079] For example, when the current action type is a handling action, a coordinated adjustment strategy is determined as the first adjustment strategy. The first adjustment strategy includes limiting the rate of change of acceleration of the robotic arm with the first load type and increasing the support force of the robotic arm with the second load type. For example, in the action of carrying and lifting a support, priority is given to ensuring the overall lifting force and the stability of the support posture. Load transfer is achieved by increasing the support force command of the low-load robotic arm and limiting the acceleration change of the high-load robotic arm.

[0080] For example, when the current action type is a positioning action, the collaborative adjustment strategy is determined to be the second adjustment strategy. The second adjustment strategy includes reducing the attitude adjustment speed of the robot arm with the first load type and increasing the attitude adjustment participation of the robot arm with the second load type. For example, in installation positioning and hole alignment actions, priority is given to ensuring positioning accuracy, and hole alignment deviation is reduced by reducing the attitude adjustment speed of the high-load robot arm and increasing the attitude correction participation of the low-load robot arm.

[0081] For example, when the current action type is an installation action, the coordinated adjustment strategy is determined to be a third adjustment strategy. The third adjustment strategy includes limiting the instantaneous torque change rate of the robotic arm of the first load type and increasing the clamping force and / or anti-deflection support force of the robotic arm of the second load type. For example, in a fastening action, priority is given to ensuring that the support component does not rotate or deviate. This is achieved by increasing the clamping force and anti-deflection support force of the auxiliary robotic arm and limiting the instantaneous torque change rate of the robotic arm performing the fastening action to maintain installation stability.

[0082] In some other embodiments, a collaborative adjustment strategy can be determined based on the current action type, the operational correlation data of each robotic arm, and the load type of each robotic arm. The operational correlation data may include at least one of the following: rated drive capacity, support component posture deviation, end effector force state of each robotic arm, joint drive current, joint output torque, safe operation constraints, and drive execution results from the previous control cycle.

[0083] For example, safety operation constraints may include the rated drive power constraint of the robotic arm, the maximum permissible torque constraint of the joints, the joint motor current constraint, the maximum clamping force constraint of the end effector, the speed and acceleration constraint of the robotic arm, the temperature rise constraint, and the attitude stability constraint of the support components.

[0084] For example, in determining the coordinated adjustment strategy, each robotic arm needs to meet the above-mentioned safe operation constraints to avoid overload, instability or mechanical damage caused by the adjustment of drive parameters exceeding the safety boundary of the robotic arm.

[0085] In some embodiments, the drive parameters of each robotic arm are jointly adjusted according to a collaborative adjustment strategy, based on at least one of the following constraints: minimizing attitude error, reducing the load ratio difference, and ensuring that the synchronization error of each robotic arm's movements does not exceed a preset error threshold. This ensures that the drive adjustment between multiple robotic arms is not performed independently, but rather through collaborative calculations centered on the overall attitude stability and installation positioning accuracy of the support structure. The load ratio difference can be understood as the difference in load between two robotic arms; attitude error can include at least one of the following: support attitude error and robotic arm alignment error.

[0086] S204. Control the movement of different robotic arms according to the target drive parameters of each robotic arm after joint adjustment.

[0087] In some embodiments, target control commands can be generated based on the target drive parameters of each robotic arm after joint adjustment; and the corresponding robotic arm movement can be controlled by the target control commands.

[0088] In other embodiments, a periodic closed-loop feedback method can be used to correct the drive parameters. For example, at the end of each control cycle, the actual drive parameters fed back by each robotic arm are compared with the target drive parameters of each robotic arm after joint adjustment issued in this cycle, and the drive execution deviation between the actual drive parameters and the target drive parameters is determined; based on the drive execution deviation, a target control command is generated to control the movement of the corresponding robotic arm.

[0089] For example, if the actual supporting force of a robotic arm is lower than the target supporting force, but the robotic arm still meets the safety operation constraints, the target driving torque or end-effector supporting force command of the robotic arm will be increased in the next control cycle. If the actual current, actual torque, or temperature rise of a robotic arm is close to the upper limit of the safety constraints, the driving compensation of the robotic arm will not be increased. Instead, the corresponding support, clamping, or attitude holding tasks will be transferred to a robotic arm with a higher safety margin, such as a low-load auxiliary robotic arm. If an increase in the attitude deviation of the support component is detected, the rate of change of speed and the rate of change of acceleration of the robotic arms involved in attitude adjustment will be reduced first, and the alignment correction of the auxiliary robotic arm will be increased. Thus, the adjustment of the driving parameters is based not only on the current load ratio but also on the actual execution feedback of the previous control cycle, enabling the multi-robotic arm driving capacity allocation to form a continuous closed-loop adjustment. During the driving capacity allocation process, the multi-robotic arm driving capacity allocation result is ensured to meet the requirements of the overall pipe gallery support installation operation for lifting force, positioning accuracy, and installation stability, while avoiding a single robotic arm operating under high load for a long time, and ensuring that the driving capacity allocation result of each robotic arm meets the overall system constraints and the safety operation constraints of a single robotic arm.

[0090] In some other embodiments, historical operating data of each robotic arm of the target robot can be obtained; based on the historical operating data, the predicted driving parameters of each robotic arm can be determined; based on the target driving parameters and predicted driving parameters of each robotic arm after joint adjustment, the target control command of each robotic arm can be determined; and the target control command of the robotic arm can be used to control the movement of the corresponding robotic arm.

[0091] The aforementioned robot control method, by acquiring the current load data of each robotic arm of the target robot, can perceive the actual load status of each robotic arm in real time, providing a data foundation for subsequent differentiated control. For each robotic arm, based on its current load data, the load type of the robotic arm can be determined, distinguishing the load states of different robotic arms and providing a basis for the joint adjustment of drive parameters among multiple robotic arms. Based on the load state of each robotic arm, the load balance of each robotic arm is achieved by jointly adjusting its drive parameters. By controlling the movement of different robotic arms according to the target drive parameters after joint adjustment, the drive capacity of each robotic arm can be adjusted in a timely manner during support handling, lifting, positioning, hole alignment, and fastening processes. This reduces situations such as excessive single-arm load, local overload, or uneven utilization of drive resources, improving the continuous collaborative operation capability of multiple robotic arms and the operational stability and reliability under dynamic load scenarios.

[0092] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment in which the drive interface of the robotic arm is refined.

[0093] refer to Figure 3 The diagram shows the drive structure of the target robot. The drive structure includes a drive interface integration module and the original drive interfaces for each robotic arm. The original drive interfaces are the existing distributed drive interfaces for the robotic arms. Figure 3 The example illustrates the connection between the original drive interfaces of robotic arms 1, 2, and 3 and the drive interface integration module. The drive interface integration module is used to perform interface mapping and standardization on the drive interfaces of each robotic arm, thereby enabling unified and coordinated drive control. It should be noted that this application does not impose any limitation on the number of robotic arms in the target robot.

[0094] In some embodiments, before the target robot starts its operation, the drive interfaces of each robotic arm in the system can be uniformly integrated. Addressing the differences in signal type, control cycle, and feedback method between the original drive interfaces of multiple robotic arms, the control signals of each robotic arm servo drive unit are standardized. The standardized definition includes at least one of the following: target torque command, speed limit parameters, acceleration constraint parameters, and state feedback signal format.

[0095] In some embodiments, the drive interface integration module includes an interface mapping processing unit and a standardization processing unit. The interface mapping processing unit establishes a mapping relationship between the original drive interfaces of each robotic arm and a unified drive interface. This mapping relationship can include signal field correspondences and control parameter dimension conversion relationships. The standardization processing unit performs format unification, timing alignment, and parameter normalization on the mapped drive control signals. Through the above interface mapping and standardization processes, the original dispersed drive control interfaces are converted into a unified drive interface or a unified drive control channel. This allows multiple robotic arm drive behaviors to be managed simultaneously through a single interface, thereby eliminating the impact of differences in different robotic arm drive interfaces on collaborative control at the control logic level. This step forms a unified input / output system for multi-robotic arm drive control, laying the foundation for subsequent multi-arm synchronous control and load coordination.

[0096] For example, a bidirectional data and control interaction relationship is formed between the drive interface integration module and the controller (i.e., the collaborative drive control module) to realize the issuance of drive commands and the return of status feedback, thereby forming a closed-loop information channel for the drive control layer. When the system adds, replaces, or adjusts a robotic arm, the newly added or replaced robotic arm can be incorporated into the unified drive control channel by updating the interface mapping relationship, the control parameter dimension conversion relationship, and the rated drive capability parameters of the corresponding robotic arm. There is no need to reconstruct the overall collaborative drive control process, thereby improving the system's adaptability under different numbers of robotic arms, different robotic arm models, and different drive interface conditions.

[0097] In the above steps, by unifying and standardizing the drive interfaces of each robotic arm, a unified drive signal transmission and status feedback channel is established, enabling the collaborative drive control module to synchronously control multiple robotic arms based on a unified interface. This reduces response differences caused by different drive interfaces, control protocols, and feedback cycles, improves the consistency of multi-robotic arm drive command issuance and execution, and reduces time deviations and control lags during multi-arm collaborative operations. Furthermore, when it is necessary to add, replace, or adjust robotic arms, adaptation can be achieved through interface mapping relationships and relevant drive parameter configurations, reducing the difficulty of system upgrades and maintenance, and improving the engineering applicability and scalability of the pipe gallery support installation robot under different specifications of supports, different installation locations, and different construction environments.

[0098] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment in which the motion control steps of the robotic arm are refined.

[0099] See Figure 4 The robotic arm motion control steps shown include:

[0100] S401. Obtain historical operating data of each robotic arm of the target robot.

[0101] In some embodiments, historical operating data may include at least one of the following: end-effector position, end-effector posture, motion speed, load percentage, support component posture deviation, and contact reaction force variation in multiple consecutive control cycles prior to the current control cycle k.

[0102] S402. Based on historical operating data, determine the predicted drive parameters for each robotic arm.

[0103] In some embodiments, based on historical operating data, the operational change data of each robotic arm corresponding to the next control cycle k+1, the next work stage, or the next moment can be determined; based on the operational change data, the predictive drive parameters of each robotic arm can be determined. The operational change data may include at least one of the following: load change trends, attitude adjustment requirements, installation alignment error change trends, and motion change data.

[0104] For example, when the changing data indicates that the target robotic arm will show an upward trend in load in the next control cycle, predictive drive parameters for the target robotic arm in the next control cycle can be generated. The predictive drive parameters may include at least one of the following: upper limit of drive torque, upper limit of drive power, speed change rate limit, acceleration change rate limit, support force pre-allocation value, clamping force pre-allocation value, attitude adjustment speed preset value, and command smoothing coefficient.

[0105] For example, when the operational change data indicates that the next stage will involve a support handling action, it means that the current action type is about to change to a handling action type. Accordingly, the pre-distribution value of the support force of the robotic arm for the second load type can be increased, and the acceleration change rate limit of the robotic arm for the first load type can be reduced, so that the support components can complete the force redistribution before entering the load change stage. The support handling action can include handling and lifting of the support.

[0106] For example, when the change data indicates that the next stage will enter the positioning action, it means that the current action type is about to change to the positioning action type; accordingly, the upper limit of the attitude adjustment speed of each robotic arm can be reduced, and the alignment correction weight of the robotic arm of the second load type can be increased to reduce attitude sway during the hole alignment process.

[0107] For example, when the change in operational data indicates that the next stage will enter the installation action, it means that the current action type is about to change to the installation action type; accordingly, the clamping force or anti-deflection support force of the non-fastening robotic arm can be increased, and the instantaneous torque change rate of the robotic arm performing the fastening action can be limited to prevent the support components from shifting during the fastening process.

[0108] Understandably, compared to passively adjusting only after load changes occur, pre-correcting drive parameters by considering the bracket installation position, changes in the robotic arm's posture, the stress trends of bracket components, and historical load trends, and determining predictive drive parameters, can reduce response lag caused by sudden load changes.

[0109] S403. Based on the target drive parameters and predicted drive parameters of each robotic arm after joint adjustment, determine the target control command for each robotic arm.

[0110] Among them, the target control command can be determined by the target drive parameters and predicted drive parameters of each subsequent robotic arm. The target drive parameters reflect the immediate adjustment or control requirements under the current load condition, and the predicted drive parameters represent the drive parameters predicted based on historical operating data, which are used to pre-correct the target drive parameters corresponding to the current control cycle.

[0111] In some embodiments, the target control command may include at least one of the following: target drive torque of each robotic arm, upper limit of joint drive power, speed limit parameter, acceleration limit parameter, end-effector support force command, end-effector gripping force command, attitude adjustment speed command, installation alignment action correction amount, command smoothness coefficient, control cycle number, synchronous execution timestamp, and robotic arm priority identifier.

[0112] S404. Control the movement of the corresponding robotic arm using the target control command of the robotic arm.

[0113] In some embodiments, before issuing the drive command, a consistency check can be performed on the final drive command. The check includes whether the target action of each robotic arm meets the overall posture constraints of the support structure, whether the start time of each robotic arm is consistent, whether the execution cycle of each robotic arm is consistent, and whether the speed difference and posture adjustment difference between each robotic arm are within the allowable range. Only when the above check results meet the preset conditions will the final drive command be synchronously issued to each robotic arm servo drive unit through the unified drive interface to control the movement of the corresponding robotic arm.

[0114] For example, during instruction execution, each robotic arm servo drive unit starts execution according to a unified synchronization timestamp. If any robotic arm fails to complete instruction reception confirmation within a preset confirmation time, it can be controlled to pause the execution of the collaborative action instruction for this cycle, and the system can be switched to a safe holding state to maintain the support posture. If all robotic arms complete confirmation, the target drive torque, speed limit, acceleration limit, support force, clamping force, and posture adjustment actions are executed synchronously according to the same control cycle. In this step, the unified synchronization timestamp, control cycle number, and instruction reception confirmation mechanism ensure that the start time and execution cycle of the multi-robotic arm drive instructions are consistent, so that the actions of the multi-robotic arms are highly synchronized in the time dimension, thereby realizing the stable posture maintenance and continuous installation action of the support component during the handling, lifting, positioning, hole alignment, and fastening processes.

[0115] In some embodiments, target control commands can be sent to each robotic arm servo drive unit through a unified drive interface to control the movement of the corresponding robotic arm. It is understood that by synchronously issuing drive commands through the unified drive interface, the overall system operation delay can be controlled within a preset delay threshold. The preset delay threshold can be set according to the control cycle and operation response requirements of the pipe gallery support installation robot, and this application does not impose any limitations on it.

[0116] In some embodiments, current operating data of each robotic arm and collaborative operating data between robotic arms can be acquired; based on the current operating data and collaborative operating data, a target robotic arm with a risk of collaborative instability can be identified; and the target robot can be stabilized.

[0117] Optionally, the stability assurance and adjustment module can be invoked to identify the target robotic arm with a risk of collaborative instability based on the current operating data and collaborative operating data; and to perform stability control on the target robot.

[0118] For example, the current operating data may include at least one of the following: load fluctuation amplitude, drive parameter change rate, and vibration trend; the collaborative operating data may include at least one of the following: load ratio difference between each robotic arm, relative position deviation of the end effector, posture deviation of the support component, synchronous execution time deviation, support force between adjacent robotic arms, and difference in clamping force between adjacent robotic arms.

[0119] For example, when the load fluctuation amplitude, drive torque change rate, speed change rate, or acceleration change rate of a certain robotic arm exceeds a preset stability threshold, it can be determined that the robotic arm has a single-node instability risk; when the load ratio difference, end-effector relative position deviation, synchronous execution time deviation, or support force difference between multiple robotic arms exceeds a preset coordination threshold, it can be determined that the current coordinated drive state has a multi-arm coordinated instability risk.

[0120] Optionally, for single-node instability risks, the rate of change of the driving torque, the rate of change of speed, and the rate of change of acceleration of the robotic arm can be limited, and some of the support or attitude adjustment tasks of the robotic arm can be transferred to a robotic arm with a higher safety margin. For multi-arm cooperative instability risks, the driving parameters of multiple robotic arms participating in the operation of the same support component can be adjusted simultaneously, so that the support force, clamping force, attitude adjustment speed, and synchronous execution time of each robotic arm can meet the cooperative constraints again.

[0121] In collaborative drive mode, if the difference in support force among multiple robotic arms is detected to be greater than the preset support force threshold, or if there is an increasing trend in the synchronization deviation of the movements (e.g., the rate of change of synchronization deviation is greater than the preset rate of change threshold), the overall attitude adjustment speed and acceleration change rate can be reduced first, and the command smoothness coefficient can be increased to keep the attitude changes of the support components continuous during the handling, lifting, positioning and fastening processes. If the load of a certain robotic arm is detected to rise rapidly and other robotic arms still have residual driving capacity, the auxiliary support force and clamping force can be redistributed according to the collaborative adjustment strategy to avoid the load of a single arm from continuing to concentrate.

[0122] During closed-loop control, the drive capacity allocation results and drive parameters for subsequent control cycles can be continuously updated based on the adjusted operating status, thus forming a closed-loop control link of "load perception—overall assessment—dynamic allocation—look-ahead adjustment—synchronous execution—cooperative stability feedback". Simultaneously, by forming a complete multi-arm cooperative drive control system at the drive interface layer, load allocation layer, look-ahead prediction layer, and cooperative stability control layer, it can be applied to the multi-arm cooperative operation stages of support component handling, lifting, positioning, hole alignment, and fastening during pipe gallery support installation. Furthermore, through the synergistic optimization of drive control strategies and work load characteristics, combined with stability assurance and closed-loop adjustment mechanisms, shocks, vibrations, and load fluctuations during the drive process are suppressed. This ensures rapid response from multiple robotic arms while improving the stability and reliability of the system during continuous high-load operations, reducing the risks of robotic arm instability, support component attitude deviation, installation hole position deviation, or instability in the fastening process caused by localized force concentration.

[0123] Based on the technical solutions of the above embodiments, a robot control system is provided. (See reference...) Figure 5A The control system of the robot shown includes: a multi-arm execution module, a load sensing and acquisition module, a support mounting trajectory prediction module, a stability assurance and adjustment module, a collaborative drive control module, and a drive interface integration module. Optionally, the support mounting trajectory prediction module, the stability assurance and adjustment module, the collaborative drive control module, and the drive interface integration module can constitute the controller of the target robot. The input of the controller is connected to the load sensing and acquisition module, and the output of the controller is connected to the multi-arm execution module.

[0124] For example, the load sensing and acquisition module is used to collect the execution command feedback or execution status feedback of the multi-robotic arm execution module. The execution status feedback may include the current load data of each robotic arm, which is also the real-time load data set.

[0125] For example, the bracket installation trajectory prediction module is used to generate bracket installation trajectory prediction based on the real-time load data set, and send the bracket installation trajectory prediction to the stability assurance and adjustment module.

[0126] For example, the collaborative drive control module is used to determine the load type of the robotic arm based on the current load data collected by the load sensing and acquisition module, jointly adjust the drive parameters of each robotic arm according to the load type of each robotic arm, and determine the target drive parameters of each robotic arm after joint adjustment, which are also the drive capability allocation parameters. Optionally, the collaborative drive control module can jointly adjust the drive parameters of each robotic arm based on the load type of each robotic arm and the predicted support installation trajectory.

[0127] For example, the stability assurance and adjustment module is used to adjust stability based on the predicted trajectory of the support installation and the target driving parameters, and to generate stability adjustment commands. On one hand, the stability assurance and adjustment module forms a closed-loop control link with the support installation trajectory prediction module, that is, based on the stability adjustment commands, the support installation trajectory prediction module reconstructs the trajectory. On the other hand, the stability adjustment commands can be fed back to the collaborative drive module, enabling the collaborative drive module to adjust the target driving parameters based on the stability adjustment commands. According to the adjusted target driving parameters, the module controls the multi-robotic arm execution module to drive the robotic arm to realize the handling, positioning, and installation of the support components through standardized drive signals or interface mapping data. Among them, under heavy load conditions, the stability adjustment commands can include smooth adjustment and closed-loop correction of the drive commands.

[0128] refer to Figure 5B The diagram illustrates the data acquisition process of the load sensing and acquisition module. The multi-arm execution module can be configured with joint motor current acquisition points, joint torque acquisition points, and end effector force acquisition points. The load sensing and acquisition module acquires sensor data from different acquisition points, such as current data, torque data, and force data. Based on the sensor data from different acquisition points, it determines the current load data of each arm and sends this data to the collaborative drive control module. The acquired current load data can be categorized according to arm number and joint number, and preprocessed with dimension conversion and normalization to form a multi-arm load data set reflecting the current load status of each arm, i.e., the current load data of each arm.

[0129] In some embodiments, the control system of the target robot may further include a load state modeling module. (Reference) Figure 5C The diagram illustrates the modeling and evaluation process of the load state modeling module. The input terminal of the load state modeling module is used to input the current load data of each robotic arm (e.g., robotic arm 1, robotic arm 2, and robotic arm 3); the output terminal outputs the overall load evaluation result, such as whether it meets the preset load imbalance conditions. The load state modeling module may include a load percentage calculation unit and a load distribution evaluation unit. The load percentage calculation unit determines the load percentage of each robotic arm based on its current load data; the load distribution evaluation unit determines the load distribution of each robotic arm based on its current load data. The load distribution evaluation unit can send the overall load evaluation result to the collaborative drive control module for the module to determine whether load adjustment is necessary.

[0130] For example, when the overall load assessment result indicates that the load distribution meets the preset load imbalance condition, the collaborative drive control module determines the load type of the robotic arm based on the current load data of the robotic arm; based on the load type of each robotic arm, it jointly adjusts the drive parameters of each robotic arm; and based on the target drive parameters of each robotic arm after joint adjustment, it controls the movement of different robotic arms. The joint adjustment steps and the control steps of the robotic arms have been described above and will not be repeated here.

[0131] Based on the technical solutions of the above embodiments, the robot control method is described in detail.

[0132] refer to Figure 6 The diagram illustrates a robot control method in another embodiment, comprising:

[0133] S601. Obtain the current load data and historical operation data of each robotic arm of the target robot.

[0134] S602. Determine the load distribution of each robotic arm based on its current load data.

[0135] S603. When the load distribution meets the preset load imbalance condition, determine the load ratio and safety margin of the robotic arm based on the current load data of the robotic arm.

[0136] S604. Determine the load type of the robotic arm based on the load percentage and safety margin.

[0137] S605. Obtain the current action type of the target robot.

[0138] S606. Determine the collaborative adjustment strategy based on the current motion type and the load type of each robotic arm.

[0139] S607. Based on at least one of the following constraints—minimum attitude error, reduced load ratio difference, and synchronization error of each robotic arm not exceeding a preset error threshold—the driving parameters of each robotic arm are jointly adjusted according to the collaborative adjustment strategy.

[0140] S608. Based on historical operating data, determine the predicted drive parameters for each robotic arm.

[0141] S609. Based on the target drive parameters and predicted drive parameters of each robotic arm after joint adjustment, determine the target control command for each robotic arm.

[0142] S610: Control the movement of the corresponding robotic arm using the target control command of the robotic arm.

[0143] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0144] Based on the same inventive concept, this application also provides a robot control device for implementing the robot control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more robot control device embodiments provided below can be found in the limitations of the robot control method described above, and will not be repeated here.

[0145] In one exemplary embodiment, such as Figure 7 As shown, a robot control device is provided, including: an acquisition module 701, a determination module 702, an adjustment module 703, and a control module 704, wherein:

[0146] The acquisition module 701 is used to acquire the current load data of each robotic arm of the target robot;

[0147] The determination module 702 is used to determine the load type of each robotic arm based on the current load data of the robotic arm.

[0148] The adjustment module 703 is used to jointly adjust the drive parameters of each robotic arm according to the load type of each robotic arm;

[0149] The control module 704 is used to control the movement of different robotic arms according to the target drive parameters of each robotic arm after joint adjustment.

[0150] In some embodiments, the adjustment module 703 includes: a first acquisition unit, configured to acquire the current action type of the target robot; a first determination unit, configured to determine a collaborative adjustment strategy based on the current action type and the load type of each robotic arm; and an adjustment unit, configured to jointly adjust the drive parameters of each robotic arm according to the collaborative adjustment strategy.

[0151] In some embodiments, the load type includes a first load type and a second load type; the first load of the robotic arm of the first load type is greater than the second load of the robotic arm of the second load type; the first determining unit includes at least one of the following: a first determining subunit, configured to determine a cooperative adjustment strategy as a first adjustment strategy when the current action type is a handling action type, the first adjustment strategy including limiting the rate of change of acceleration of the robotic arm of the first load type and increasing the support force of the robotic arm of the second load type; a second determining subunit, configured to determine a cooperative adjustment strategy as a second adjustment strategy when the current action type is a positioning action type, the second adjustment strategy including reducing the attitude adjustment speed of the robotic arm of the first load type and increasing the attitude adjustment participation of the robotic arm of the second load type; a third determining subunit, configured to determine a cooperative adjustment strategy as a third adjustment strategy when the current action type is an installation action type, the third adjustment strategy including limiting the instantaneous torque change rate of the robotic arm of the first load type and increasing the bearing force and / or anti-deflection support force of the robotic arm of the second load type.

[0152] In some embodiments, the first load type includes a first subtype and / or a second subtype; the safety margin of the robotic arm of the first subtype is greater than the safety margin threshold of the robotic arm, and the safety margin of the robotic arm of the second subtype is not greater than the safety margin threshold of the robotic arm; the adjustment strategy also includes at least one of the following: increasing the driving torque and / or end-effector force of the robotic arm of the first subtype without exceeding a preset driving upper limit; reducing the rate of change of acceleration and / or rate of change of speed of the robotic arm of the first subtype; reducing the speed limit parameter and / or acceleration limit parameter of the robotic arm of the second subtype.

[0153] In some embodiments, the adjustment unit includes an adjustment subunit, configured to jointly adjust the drive parameters of each robotic arm according to a collaborative adjustment strategy, based on at least one of the following constraints: minimum attitude error, reduced load ratio difference, and synchronization error of each robotic arm's movements not exceeding a preset error threshold.

[0154] In some embodiments, the determining module 702 includes: a second determining unit, configured to determine the load distribution of each robotic arm based on the current load data of each robotic arm; a third determining unit, configured to determine the load percentage and safety margin of the robotic arm based on the current load data of the robotic arm when the load distribution meets a preset load imbalance condition; and a third determining unit, configured to determine the load type of the robotic arm based on the load percentage and safety margin.

[0155] In some embodiments, the control module 704 includes: a second acquisition unit for acquiring historical operating data of each robotic arm of the target robot; a fourth determination unit for determining the predicted driving parameters of each robotic arm based on the historical operating data; a fifth determination unit for determining the target control command of each robotic arm based on the target driving parameters and predicted driving parameters of each robotic arm after joint adjustment; and a control unit for controlling the movement of the corresponding robotic arm with the target control command of the robotic arm.

[0156] The various modules in the control device of the robot described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0157] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a robot control method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0158] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0159] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0160] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0161] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0162] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0163] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0164] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling a robot, characterized in that, The method includes: Obtain the current load data of each robotic arm of the target robot; For each robotic arm, the load type of the robotic arm is determined based on its current load data; The drive parameters of each robotic arm are jointly adjusted according to the load type of each robotic arm. Based on the target drive parameters of each robotic arm after joint adjustment, the movement of each robotic arm is controlled.

2. The method according to claim 1, characterized in that, The step of jointly adjusting the drive parameters of each robotic arm according to the load type of each robotic arm includes: Obtain the current action type of the target robot; Based on the current action type and the load type of each robotic arm, a collaborative adjustment strategy is determined; According to the aforementioned collaborative adjustment strategy, the drive parameters of each robotic arm are jointly adjusted.

3. The method according to claim 2, characterized in that, The load type includes a first load type and a second load type; the first load of the robotic arm of the first load type is greater than the second load of the robotic arm of the second load type; the determination of the collaborative adjustment strategy based on the current action type and the load type of each robotic arm includes at least one of the following: When the current action type is a handling action type, the coordinated adjustment strategy is determined to be a first adjustment strategy. The first adjustment strategy includes limiting the rate of change of acceleration of the robotic arm of the first load type and increasing the support force of the robotic arm of the second load type. When the current action type is a positioning action type, the collaborative adjustment strategy is determined to be a second adjustment strategy. The second adjustment strategy includes reducing the attitude adjustment speed of the robotic arm of the first load type and increasing the attitude adjustment participation of the robotic arm of the second load type. When the current action type is the installation action type, the coordinated adjustment strategy is determined to be the third adjustment strategy. The third adjustment strategy includes limiting the instantaneous torque change rate of the robotic arm of the first load type and increasing the bearing force and / or anti-deflection support force of the robotic arm of the second load type.

4. The method according to claim 3, characterized in that, The first load type includes a first subtype and / or a second subtype; the safety margin of the robotic arm of the first subtype is greater than the safety margin threshold of the robotic arm, and the safety margin of the robotic arm of the second subtype is not greater than the safety margin threshold of the robotic arm; the collaborative adjustment strategy further includes at least one of the following: Without exceeding the preset driving limit, increase the driving torque and / or end-effector support force of the first subtype of robotic arm; Reduce the rate of change of acceleration and / or the rate of change of velocity of the first subtype of robotic arm; Reduce the speed limit parameters and / or acceleration limit parameters of the second subtype of robotic arm.

5. The method according to claim 2, characterized in that, The step of jointly adjusting the drive parameters of each robotic arm according to the collaborative adjustment strategy includes: Based on the constraints of minimizing attitude error, reducing the difference in load ratio, and ensuring that the synchronization error of each robotic arm's movement does not exceed a preset error threshold, the drive parameters of each robotic arm are jointly adjusted according to the collaborative adjustment strategy.

6. The method according to any one of claims 1-5, characterized in that, For each robotic arm, determining the load type of the robotic arm based on its current load data includes: Based on the current load data of each robotic arm, determine the load distribution of each robotic arm; If the load distribution meets the preset load imbalance condition, the load ratio and safety margin of the robotic arm are determined based on the current load data of the robotic arm. The load type of the robotic arm is determined based on the load percentage and safety margin.

7. The method according to any one of claims 1-5, characterized in that, The step of controlling the movement of different robotic arms based on the jointly adjusted drive parameters of each robotic arm includes: Obtain historical operational data for each robotic arm of the target robot; Based on the historical operating data, the predicted drive parameters of each robotic arm are determined; Based on the target drive parameters of each robotic arm after joint adjustment and the predicted drive parameters, the target control command of each robotic arm is determined; The target control command of the robotic arm is used to control the movement of the corresponding robotic arm.

8. A control device for a robot, characterized in that, The device includes: The acquisition module is used to acquire the current load data of each robotic arm of the target robot; The determination module is used to determine the load type of each robotic arm based on its current load data. An adjustment module is used to jointly adjust the drive parameters of each robotic arm according to the load type of each robotic arm; The control module is used to control the movement of different robotic arms according to the target drive parameters of each robotic arm after joint adjustment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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