Quadruped robot motion control and operation system for industrial environment

Abstract

The invention relates to the technical field of hybrid intelligence, in particular to a quadruped robot motion control and operation system for an industrial environment, which realizes full-link intelligent management from environment perception to motion control, and remarkably improves the operation precision of a robot and the system efficiency. A three-dimensional map is constructed through a multi-sensor fused environment sensing module, and precise positioning is realized by combining state estimation based on deep learning; a motion control module with a hierarchical control architecture is adopted, and stable motion under a complex terrain is ensured through model prediction and gait optimization; the digital twinning and cloud side end collaboratively constructs a virtual-real mapping system to realize predictive maintenance and remote monitoring; and the multi-agent coordination module realizes task coordination and resource optimization of the multi-robot system through a distributed scheduling algorithm.

Description

Technical Field

[0001] This invention relates to the field of hybrid intelligent technology, and more particularly to a motion control and operating system for quadruped robots used in industrial environments. Background Technology

[0002] With the increasing application of robotics in fields such as industrial automation and emergency rescue, traditional robot systems face numerous technical bottlenecks. Existing systems have limited perception capabilities in complex environments, and insufficient fusion of multi-sensor data leads to inadequate environmental modeling accuracy. Autonomous navigation algorithms are slow to respond to dynamic obstacle avoidance and have poor real-time path planning. Multi-robot collaboration lacks intelligent scheduling mechanisms, resulting in inefficient task allocation. Data synchronization between digital twin systems and physical devices is delayed, leading to insufficient accuracy in predictive maintenance. Furthermore, the motion control modules of existing systems have poor adaptability, insufficient stability in unstructured terrain, and their operational precision cannot meet the demands of high-precision operations. Therefore, there is an urgent need to develop a novel robot system integrating intelligent perception, precise navigation, collaborative scheduling, and digital twin technology to enhance the robot's operational capabilities in complex scenarios.

[0003] Chinese Patent Publication No. CN112947428 discloses a method and apparatus for controlling the movement of a quadruped robot. The method includes: responding to the quadruped robot's four-legged ground-touching operation; acquiring the quaternion of the quadruped robot and the current coordinate positions of the four ends of the four legs of the robot; inputting the current coordinate positions and the current quaternion into a pre-trained neural network model to obtain a terrain prediction result; determining a target swing gait based on the terrain prediction result; determining the state trajectory of the quadruped robot based on the terrain prediction result and the target swing gait; and controlling the movement of the quadruped robot according to the state trajectory. By using a pre-trained neural network model to obtain the terrain prediction result of the quadruped robot in real time, the state trajectory of the quadruped robot can be determined based on the terrain prediction result, simplifying the complexity of quadruped robot control, reducing the time invested in the development of the control system, and improving the stability of the movement process. However, this solution still suffers from problems such as inaccurate perception, delayed response, difficulty in coordination, and insufficient control precision in complex application scenarios of existing robot systems. Summary of the Invention

[0004] Therefore, the present invention provides a motion control and operating system for quadruped robots in industrial environments, in order to overcome the problems of inaccurate perception, delayed response, difficulty in coordination, and insufficient control precision of existing robot systems in complex application scenarios.

[0005] To achieve the above objectives, the present invention provides a motion control and operating system for a quadruped robot in an industrial environment, comprising: The environmental perception module is used to collect environmental data through sensors and output environmental perception results; An autonomous navigation module is used to generate a navigation path plan based on the environmental perception results; The state estimation module is used to estimate the robot's motion state data in real time based on the navigation path planning; An operation execution module is used to generate operation execution instructions based on the motion state data; Human-computer interaction module, used to generate interactive control data according to the operation execution instructions; A multi-agent collaboration module is used to generate collaborative scheduling data based on the interactive control data; The digital twin and cloud-edge-device collaboration module is used to generate optimized control data based on the collaborative scheduling data; The motion control module is used to generate gait control commands for the robot based on the optimized control data, and also to perform motion control on the quadruped robot based on the gait control commands.

[0006] Furthermore, the environmental perception module collects environmental data through sensors and outputs environmental perception results, including: Multi-sensor data is collected from the inspection area of ​​a coal-fired power plant to obtain raw environmental perception data; point cloud preprocessing and image registration are performed on the raw environmental perception data to obtain registered multimodal environmental data; defect identification and obstacle detection are performed on the registered multimodal environmental data to obtain environmental semantic understanding results; data fusion and structuring processing are performed on the environmental semantic understanding results to obtain the final environmental perception results.

[0007] Furthermore, the autonomous navigation module generates a navigation path plan based on the environmental perception results, including: A 3D map is constructed from the environmental point cloud data in the environmental perception results to obtain a global environmental map; path planning calculations are performed on the global environmental map to obtain an initial navigation path; and real-time obstacle avoidance optimization is performed on the initial navigation path to obtain the final navigation path plan.

[0008] Furthermore, the state estimation module estimates the robot's motion state data in real time based on the navigation path planning, including: Robot sensor data is collected and preprocessed to obtain multi-source state observation data; feature extraction and state prediction are performed on the multi-source state observation data to obtain preliminary state estimates; the preliminary state estimates are fused and optimized to obtain accurate motion state data.

[0009] Furthermore, the operation execution module generates operation execution instructions based on the motion state data, including: The target operation task is analyzed to obtain the operation requirement description; inverse kinematics calculations are performed on the motion state data and operation requirements to obtain joint trajectory planning; force-position hybrid control optimization is performed on the joint trajectory planning to obtain operation execution instructions.

[0010] Furthermore, the human-computer interaction module generates interactive control data based on the operation execution command, including: The user input commands are parsed and identified to obtain an operation intent description; the operation execution commands and operation intents are coordinated and mapped to obtain preliminary control commands; the preliminary control commands are then subjected to security verification and optimization to obtain interactive control data.

[0011] Furthermore, the multi-agent collaborative module generates collaborative scheduling data based on the interactive control data, including: The system task requirements and robot status are evaluated to obtain a task allocation scheme; the task allocation scheme is optimized by path coordination to obtain a collaborative path plan; the collaborative path plan is encapsulated by a communication protocol to obtain collaborative scheduling data.

[0012] Furthermore, the digital twin and cloud-edge-device collaboration module generates optimized control data based on the collaborative scheduling data, including: Multi-source operational data is collected and aggregated to obtain a panoramic view of the system status; the panoramic view of the system status is then analyzed and predicted in depth to obtain optimization strategy suggestions; the optimization strategy suggestions are then verified and optimized through simulation to obtain optimized control data.

[0013] Furthermore, the motion control module generates gait control commands for the robot based on the optimized control data, including: The robot's motion requirements are analyzed and gait is selected to obtain gait parameter settings; the gait parameters are then used for trajectory planning and optimization to obtain foot trajectory planning; and the foot trajectory is then used for inverse dynamics calculation and joint control to obtain gait control commands.

[0014] Furthermore, the motion control module performs motion control on the quadruped robot according to the robot's gait control commands, including: The received gait control commands are parsed and verified to obtain joint control parameters; kinematic calculations are performed on the joint control parameters to obtain motor drive signals; and closed-loop control is performed on the motor drive signals to achieve precise movement of the quadruped robot.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: the environmental perception module achieves accurate environmental data acquisition through multi-sensor fusion technology, providing reliable environmental perception results for subsequent modules; the autonomous navigation module generates accurate navigation path planning based on the environmental perception results, ensuring optimal robot trajectory; the state estimation module estimates the robot's motion state data in real time, providing accurate input for operation execution; the operation execution module generates precise operation execution commands based on motion state data, enabling precise robot operation; the human-machine interaction module converts operation execution commands into interactive control data, providing an intuitive human-machine interaction experience; the multi-agent collaboration module achieves multi-robot collaborative scheduling based on interactive control data, improving overall system efficiency; the digital twin and cloud-edge-device collaboration module optimizes the system through collaborative scheduling data, generating optimized control data; and the motion control module generates final gait control commands based on optimized control data, achieving stable robot movement. This system achieves end-to-end intelligent management from environmental perception to motion control, significantly improving robot operation accuracy, system efficiency, and intelligence level. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the motion control and operating system for a quadruped robot used in an industrial environment, as described in this embodiment. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Please see Figure 1 The diagram shown is a structural schematic of the motion control and operating system for a quadruped robot used in an industrial environment according to this embodiment. The system includes: The environmental perception module is used to collect environmental data through sensors and output environmental perception results; An autonomous navigation module is used to generate a navigation path plan based on the environmental perception results; the autonomous navigation module is connected to the environmental perception module. A state estimation module is used to estimate the robot's motion state data in real time based on the navigation path planning. The state estimation module is connected to the autonomous navigation module. An operation execution module is used to generate operation execution instructions based on the motion state data. The operation execution module is connected to the state estimation module. A human-computer interaction module is used to generate interactive control data according to the operation execution command, and the human-computer interaction module is connected to the operation execution module; A multi-agent collaboration module is used to generate collaborative scheduling data based on the interactive control data. The multi-agent collaboration module is connected to the human-computer interaction module. A digital twin and cloud-edge-device collaboration module is used to generate optimized control data based on the collaborative scheduling data. The digital twin and cloud-edge-device collaboration module is connected to the multi-agent collaboration module. The motion control module is used to generate gait control commands for the robot based on the optimized control data, and also to perform motion control on the quadruped robot based on the gait control commands. The motion control module is connected to the digital twin and cloud-edge-device collaboration module.

[0022] Specifically, the system is applied to industrial automation and emergency rescue. In industrial automation scenarios, the system monitors the status of production equipment in real time through an environmental perception module and completes precision assembly tasks through an operation execution module. In emergency rescue scenarios, the system coordinates multiple robots to conduct search and rescue operations through a multi-agent collaboration module and constructs a 3D model of the disaster site through a digital twin module. The environmental perception module achieves accurate environmental data collection through multi-sensor fusion technology, providing reliable environmental perception results for subsequent modules. The autonomous navigation module generates accurate navigation path planning based on the environmental perception results, ensuring optimal robot motion trajectory. The state estimation module estimates the robot's motion state data in real time, providing accurate data for operation execution. The system provides accurate input; the operation execution module generates precise operation execution instructions based on motion state data to achieve precise robot operation; the human-machine interaction module converts operation execution instructions into interactive control data, providing an intuitive human-machine interaction experience; the multi-agent collaboration module realizes multi-robot collaborative scheduling based on interactive control data, improving the overall system efficiency; the digital twin and cloud-edge-device collaboration module optimizes the system through collaborative scheduling data and generates optimized control data; the motion control module generates the final gait control instructions based on the optimized control data to achieve stable robot movement. This system realizes intelligent management of the entire chain from environmental perception to motion control, significantly improving the robot's operation accuracy, system efficiency, and intelligence level.

[0023] Specifically, the environmental perception module collects environmental data through sensors and outputs environmental perception results, including: Multi-sensor data is collected from the inspection area of ​​a coal-fired power plant to obtain raw environmental perception data; point cloud preprocessing and image registration are performed on the raw environmental perception data to obtain registered multimodal environmental data; defect identification and obstacle detection are performed on the registered multimodal environmental data to obtain environmental semantic understanding results; data fusion and structuring processing are performed on the environmental semantic understanding results to obtain the final environmental perception results.

[0024] Specifically, the raw environmental perception data is obtained by collecting data from multiple sensors in the inspection area of ​​the coal-fired power plant. Specifically, the raw environmental perception data is obtained by scanning the environment with 3D lidar to obtain point cloud data, by collecting visible light and infrared image data with a dual-spectrum gimbal camera, and by acquiring equipment attitude data with an IMU. The process of performing point cloud preprocessing and image registration on the original environmental perception data to obtain registered multimodal environmental data is as follows: downsampling and denoising the lidar point cloud data, spatiotemporally registering the bispectral image with the point cloud data, and extracting environmental plane features through a point cloud plane segmentation algorithm to obtain registered multimodal environmental data. The registered multimodal environmental data is used to perform defect identification and obstacle detection to obtain environmental semantic understanding results. Specifically, the registered multimodal environmental data is input into a deep convolutional neural network to identify equipment and instrument readings and the types of defects such as leaks and spills. At the same time, static and dynamic obstacles in the environment are detected, and environmental semantic understanding results containing defect locations and obstacle information are output. The environmental semantic understanding results are fused and structured to obtain the final environmental perception results. Specifically, the environmental semantic understanding results are fused with the device topology map, and feature compression is performed through a variational autoencoder to generate a structured environmental perception result that includes an environmental point cloud map, obstacle distribution, and device status.

[0025] Specifically, the autonomous navigation module generates a navigation path plan based on the environmental perception results, including: A 3D map is constructed from the environmental point cloud data in the environmental perception results to obtain a global environmental map; path planning calculations are performed on the global environmental map to obtain an initial navigation path; and real-time obstacle avoidance optimization is performed on the initial navigation path to obtain the final navigation path plan.

[0026] Specifically, constructing a 3D map from the environmental point cloud data in the environmental perception results to obtain a global environmental map involves: constructing a 3D topology map containing obstacle locations and equipment layout based on the point cloud data in the environmental perception results using the SLAM algorithm to form a global environmental map; The path planning calculation for the global environment map to obtain the initial navigation path is specifically as follows: set the starting point and the target point on the global environment map, use the A* algorithm to calculate the optimal path, and generate an initial navigation path containing a sequence of path points; The initial navigation path is optimized for real-time obstacle avoidance to obtain the final navigation path plan. Specifically, the algorithm for planning the landing point of a legged robot is used to dynamically adjust the path points and output the final navigation path plan that includes the obstacle avoidance trajectory, based on the real-time obstacle information in the environmental perception results.

[0027] Specifically, the state estimation module estimates the robot's motion state data in real time based on the navigation path planning, including: Robot sensor data is collected and preprocessed to obtain multi-source state observation data; feature extraction and state prediction are performed on the multi-source state observation data to obtain preliminary state estimates; the preliminary state estimates are fused and optimized to obtain accurate motion state data.

[0028] Specifically, the robot sensor data is collected and preprocessed to obtain multi-source state observation data. Specifically, the acceleration and angular velocity data of the IMU, the position data of the joint encoder, and the foot force sensor data are collected, and after filtering and calibration, multi-source state observation data is formed. The process of extracting features and predicting the state of the multi-source state observation data to obtain a preliminary state estimate is as follows: the multi-source state observation data is input into a state estimation model based on a variational autoencoder, the encoder extracts latent features, and the decoder predicts the robot's position, velocity, and attitude to form a preliminary state estimate. The preliminary state estimate is fused and optimized to obtain accurate motion state data. Specifically, the preliminary state estimate is fused and optimized using a Kalman filter algorithm, which combines the path information in the navigation path planning, and outputs motion state data containing accurate pose, velocity and contact state.

[0029] Specifically, the operation execution module generates operation execution instructions based on the motion state data, including: The target operation task is analyzed to obtain the operation requirement description; inverse kinematics calculations are performed on the motion state data and operation requirements to obtain joint trajectory planning; force-position hybrid control optimization is performed on the joint trajectory planning to obtain operation execution instructions.

[0030] Specifically, the target operation task is analyzed to obtain the operation requirement description. Specifically, based on the preset task instructions, the operation type, target location and operation force to be performed are analyzed to form the operation requirement description. The inverse kinematics calculation of the motion state data and operation requirements yields the joint trajectory planning as follows: based on the robot's current pose and the operation target position, the angle sequence of each joint of the robotic arm is calculated using the inverse kinematics algorithm to generate the joint trajectory plan. The joint trajectory planning is optimized by force-position hybrid control to obtain the operation execution command. Specifically, the joint trajectory is adjusted in real time by combining force sensor feedback and using a force-position hybrid control algorithm, and the operation execution command containing joint control command and force control parameters is output.

[0031] Specifically, the human-computer interaction module generates interactive control data based on the operation execution command, including: The user input commands are parsed and identified to obtain an operation intent description; the operation execution commands and operation intents are coordinated and mapped to obtain preliminary control commands; the preliminary control commands are then subjected to security verification and optimization to obtain interactive control data.

[0032] Specifically, parsing and recognizing user input commands to obtain an operation intent description involves: parsing user commands through voice recognition, gesture recognition, or interface operation, identifying the operation type and target, and forming an operation intent description; The coordination and mapping of the operation execution instructions and operation intentions to obtain preliminary control instructions specifically involves: coordinating the operation intentions with the currently executed task, and generating preliminary control instructions that the robot can understand through an action mapping algorithm; The preliminary control commands are subjected to safety verification and optimization to obtain interactive control data. Specifically, the control commands are simulated and verified in the digital twin system to ensure safety and feasibility, and then interactive control data containing action commands and safety parameters is output.

[0033] Specifically, the multi-agent collaborative module generates collaborative scheduling data based on the interactive control data, including: The system task requirements and robot status are evaluated to obtain a task allocation scheme; the task allocation scheme is optimized by path coordination to obtain a collaborative path plan; the collaborative path plan is encapsulated by a communication protocol to obtain collaborative scheduling data.

[0034] Specifically, the task allocation scheme is obtained by evaluating the system task requirements and robot status: based on the task instructions in the interactive control data and the status of each robot, the inspection area and operation tasks are dynamically allocated using an auction algorithm to form a task allocation scheme. The task allocation scheme is optimized by path coordination to obtain the collaborative path planning. Specifically, the collaborative path planning is generated by considering the position and task priority of each robot and generating a collaborative path plan that avoids conflicts through a negotiation algorithm. The collaborative path planning is encapsulated using a communication protocol to obtain collaborative scheduling data. Specifically, the path planning and task instructions are encapsulated according to the communication protocol, and collaborative scheduling data that can be sent over the network is output.

[0035] Specifically, the digital twin and cloud-edge-device collaboration module generates optimized control data based on the collaborative scheduling data, including: Multi-source operational data is collected and aggregated to obtain a panoramic view of the system status; the panoramic view of the system status is then analyzed and predicted in depth to obtain optimization strategy suggestions; the optimization strategy suggestions are then verified and optimized through simulation to obtain optimized control data.

[0036] Specifically, the collection and aggregation of multi-source operational data to obtain panoramic system status data involves: collecting robot status data, environmental perception data, and task execution data, and aggregating and processing them in the cloud to form panoramic system status data. The optimization strategy suggestions obtained by performing in-depth analysis and prediction on the panoramic data of the system status are as follows: Based on big data analysis and machine learning algorithms, predictive maintenance analysis of the equipment status is performed to generate optimization strategy suggestions that include parameter adjustment and maintenance recommendations. The proposed optimization strategy is simulated and optimized to obtain optimized control data. Specifically, the optimization strategy is simulated and tested in a digital twin environment. After verifying its effectiveness, optimized control data containing motion parameter adjustments and control strategy optimization is output.

[0037] Specifically, the motion control module generates gait control commands for the robot based on the optimized control data, including: The robot's motion requirements are analyzed and gait is selected to obtain gait parameter settings; the gait parameters are then used for trajectory planning and optimization to obtain foot trajectory planning; and the foot trajectory is then used for inverse dynamics calculation and joint control to obtain gait control commands.

[0038] Specifically, the robot's motion requirements are analyzed and gait is selected to obtain gait parameter settings. Specifically, based on the motion requirements in the optimized control data, an appropriate gait mode is selected, and the corresponding gait frequency, stride length, and height parameters are generated. Trajectory planning and optimization of the gait parameters are performed to obtain foot trajectory planning. Specifically, the model predictive control algorithm is used to calculate the movement trajectory of the body and foot based on the gait parameters to generate a smooth foot trajectory plan. The foot trajectory is subjected to inverse dynamics calculation and joint control to obtain gait control commands; specifically, based on foot trajectory planning, the torque required by each joint is calculated through inverse dynamics, and motor control commands are generated in combination with a PID controller.

[0039] Specifically, the motion control module controls the motion of the quadruped robot according to the robot's gait control commands, including: The received gait control commands are parsed and verified to obtain joint control parameters; kinematic calculations are performed on the joint control parameters to obtain motor drive signals; and closed-loop control is performed on the motor drive signals to achieve precise movement of the quadruped robot.

[0040] Specifically, the received gait control commands are parsed and verified to obtain joint control parameters. Specifically, the joint angle, angular velocity, and torque parameters contained in the gait control commands are parsed, the parameters are verified to be within the safe range, and the joint control parameters containing the target position, velocity, and torque of each joint are output. The kinematic calculation of the joint control parameters to obtain the motor drive signals is specifically as follows: based on the kinematic model of the quadruped robot, the joint control parameters are converted into drive signals for each permanent magnet synchronous motor, including motor rotation angle, speed and current commands; To achieve precise movement of the quadruped robot by performing closed-loop control on the motor drive signal, the following steps are taken: PID closed-loop control is executed through a DSP multi-axis controller to adjust the output of each motor in real time, while encoder feedback data is collected to correct control errors, ensuring that the robot completes the specified movement according to the gait control command.

[0041] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A motion control and operating system for quadruped robots in industrial environments, characterized in that, include: The environmental perception module is used to collect environmental data through sensors and output environmental perception results; An autonomous navigation module is used to generate a navigation path plan based on the environmental perception results; The state estimation module is used to estimate the robot's motion state data in real time based on the navigation path planning; An operation execution module is used to generate operation execution instructions based on the motion state data; Human-computer interaction module, used to generate interactive control data according to the operation execution instructions; A multi-agent collaboration module is used to generate collaborative scheduling data based on the interactive control data; The digital twin and cloud-edge-device collaboration module is used to generate optimized control data based on the collaborative scheduling data; The motion control module is used to generate gait control commands for the robot based on the optimized control data, and also to perform motion control on the quadruped robot based on the gait control commands.

2. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The environmental perception module collects environmental data through sensors and outputs environmental perception results, including: Multi-sensor data is collected from the inspection area of ​​a coal-fired power plant to obtain raw environmental perception data; point cloud preprocessing and image registration are performed on the raw environmental perception data to obtain registered multimodal environmental data; defect identification and obstacle detection are performed on the registered multimodal environmental data to obtain environmental semantic understanding results; data fusion and structuring processing are performed on the environmental semantic understanding results to obtain the final environmental perception results.

3. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The autonomous navigation module generates a navigation path plan based on the environmental perception results, including: A 3D map is constructed from the environmental point cloud data in the environmental perception results to obtain a global environmental map; path planning calculations are performed on the global environmental map to obtain an initial navigation path; and real-time obstacle avoidance optimization is performed on the initial navigation path to obtain the final navigation path plan.

4. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The state estimation module estimates the robot's motion state data in real time based on the navigation path planning, including: Robot sensor data is collected and preprocessed to obtain multi-source state observation data; feature extraction and state prediction are performed on the multi-source state observation data to obtain preliminary state estimates; the preliminary state estimates are fused and optimized to obtain accurate motion state data.

5. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The operation execution module generates operation execution instructions based on the motion state data, including: The target operation task is analyzed to obtain the operation requirement description; inverse kinematics calculations are performed on the motion state data and operation requirements to obtain joint trajectory planning; force-position hybrid control optimization is performed on the joint trajectory planning to obtain operation execution instructions.

6. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The human-computer interaction module generates interactive control data based on the operation execution command, including: The user input commands are parsed and identified to obtain an operation intent description; the operation execution commands and operation intents are coordinated and mapped to obtain preliminary control commands; the preliminary control commands are then subjected to security verification and optimization to obtain interactive control data.

7. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The multi-agent collaborative module generates collaborative scheduling data based on the interactive control data, including: The system task requirements and robot status are evaluated to obtain a task allocation scheme; the task allocation scheme is optimized by path coordination to obtain a collaborative path plan; the collaborative path plan is encapsulated by a communication protocol to obtain collaborative scheduling data.

8. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The digital twin and cloud-edge-device collaboration module generates optimized control data based on the collaborative scheduling data, including: Multi-source operational data is collected and aggregated to obtain a panoramic view of the system status; the panoramic view of the system status is then analyzed and predicted in depth to obtain optimization strategy suggestions; the optimization strategy suggestions are then verified and optimized through simulation to obtain optimized control data.

9. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The motion control module generates gait control commands for the robot based on the optimized control data, including: The robot's motion requirements are analyzed and gait is selected to obtain gait parameter settings; the gait parameters are then used for trajectory planning and optimization to obtain foot trajectory planning; and the foot trajectory is then used for inverse dynamics calculation and joint control to obtain gait control commands.

10. The quadruped robot motion control and operating system for industrial environments according to claim 1, characterized in that, The motion control module controls the movement of the quadruped robot according to the robot's gait control commands, including: The received gait control commands are parsed and verified to obtain joint control parameters; kinematic calculations are performed on the joint control parameters to obtain motor drive signals; and closed-loop control is performed on the motor drive signals to achieve precise movement of the quadruped robot.

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