1392 results about "Robot locomotion" patented technology

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention discloses a visual prediction method for space-time manifold and implicit state deduction of a robot, and relates to the technical field of robot space visual perception. The motion state and the visual image of the robot are collected; calculating carrier pose offset and generating a reverse compensation matrix; modeling an image target into a visual cone probability manifold, and extracting an explicit manifold observation vector; monitoring the confidence coefficient in real time through an observation quality evaluation network, and activating an implicit deduction mode during observation degradation; reading historical time sequence characteristics by using an improved Transform model, and performing prediction and deduction on a future nonlinear motion state of the target; performing coordinate correction and probability field decoding on a prediction result in combination with the reverse compensation matrix, and outputting a three-dimensional prediction trajectory and a spatial covariance ellipsoid; according to the method, the problem that traditional visual tracking fails under the conditions of violent shaking of a carrier and target shielding is solved, and continuous and foresight physical scale positioning and safety decision making of a robot on a dynamic target are achieved.

The invention relates to the technical field of industrial robot path planning, in particular to an industrial mobile robot path planning method based on an artificial potential field method and dynamic prediction. The method comprises the following steps: firstly, acquiring environment point cloud data, and distinguishing static and dynamic obstacles based on time sequence difference and Euclidean distance; kalman filtering is used to predict the future position of a dynamic obstacle, and a density clustering algorithm is used to simplify a static obstacle; combining a target point, a static obstacle set and a dynamic obstacle prediction result to construct an improved artificial potential field function to calculate gravitational force and repulsive force; the potential collision risk is judged based on the motion state of the robot, the dynamic obstacle repulsive force with the risk is brought into potential synthesis in advance, and the final resultant force for guiding motion is generated; and when it is detected that the system is caught in a local optimal or oscillation state, executing a virtual gravity point escape strategy until the system reaches a target point. According to the method, the fusion of static clustering, dynamic prediction and potential field improvement is realized, and the obstacle avoidance and path planning efficiency in a complex dynamic environment is improved.

The invention provides a spatial heterogeneous vision positioning system and positioning method of a pineapple picking robot, relates to the technical field of picking robot positioning, and realizes high-precision spatial positioning and attitude estimation of the robot in a complex terrain environment by comprehensively utilizing multi-source information such as vision, inertial measurement, multispectrum and structured light. The positioning stability and the real-time performance are obviously improved; by embedding a multi-modal deep learning model, efficient non-destructive detection and intelligent classification of internal defects of pineapple fruits are realized, a mechanical arm is supported to dynamically adjust a picking strategy, fruits with better quality are preferentially selected for picking, and the picking quality and efficiency are improved; and meanwhile, the preset three-dimensional terrain model and the heuristic search algorithm are combined, the motion path of the robot is effectively planned, it is guaranteed that the robot safely and stably moves in the complex environment, and the mechanical damage risk is reduced.

The invention relates to the field of mobile robot motion planning, and discloses a quadruped robot motion planning method and system based on height self-adaption. The method comprises the following steps: constructing 3D and 2D local obstacle maps according to coordinate transformation, and calculating a grid vertical distance through ray casting to generate a 2.5 D local obstacle map; determining an initial control sampling space by combining speed, acceleration and height constraints; discretizing a sampling space and simulation time, deducing a control value sequence and recursively calculating a local trajectory; then calculating a shaft alignment bounding box in a directed bounding box of the robot, screening grid units in the shaft alignment bounding box, comparing the height of a machine body with the allowable height of grids, and rejecting collision trajectories to obtain a feasible trajectory set; and finally selecting an optimal track by the evaluation function, extracting a control value and sending the control value to hardware for execution. According to the method, the 2.5 D local obstacle map containing height information is constructed, the four-dimensional control sampling space is designed, and the robot height self-adaptive adjustment is realized in combination with the collision detection and evaluation function.

The invention discloses a ship plane segmented welding multi-robot task distribution system based on digital twinning, and relates to the technical field of ship intelligent manufacturing, and the system comprises a four-dimensional digital twinning body module which is used for collecting workpiece assembly errors, welding seam positions and robot state data in real time; and constructing a twin model comprising a physical engine layer, a process knowledge base and a dynamic topological graph. According to the ship plane segmented welding multi-robot task allocation system based on digital twinning, workpiece assembly errors, robot motion states and welding process rules are fused in real time through the digital twinning body, a weld joint task topological graph is dynamically constructed, and multi-robot task accurate allocation is achieved in combination with an anti-conflict auction algorithm. By means of a space-time gridding cooperation mechanism, motion conflicts between a large gantry mechanism and a small gantry mechanism are effectively solved, and zero interference of a welding path is guaranteed; the local path optimization module adaptively adjusts the posture and the dry extension length of a welding gun for a narrow space, and eliminates the physical collision risk.

The invention provides a whole-body control method and device of a robot, a storage medium and the robot, and belongs to the technical field of humanoid robot control. The method comprises the following steps: training a basic whole body control model by using an AMASS data set; training a sub-control model corresponding to each motion type on the basis of the basic whole body control model by using the plurality of clustering sub-data sets; after the trained sub-control model is deployed to a control module of the robot, an execution track sequence of the robot is collected by controlling the robot to move; utilizing the collected execution track sequence to train an action increment model corresponding to the sub-control model so as to adjust the corresponding sub-control model; and fusing the adjusted sub-control models by using a knowledge distillation technology to obtain an optimized whole-body control model. According to the embodiment of the invention, a'general-expert-general 'training framework is provided and is used for learning a general whole-body control strategy, and the problem of interference among different motion types is effectively relieved.

The invention relates to the technical field of industrial robot control, and discloses an industrial robot trajectory optimization control method based on an intelligent algorithm. The method comprises the steps that joint position information, tool center point coordinates and a motion time sequence when a robot executes multiple tasks are collected and stored in a track database; after cleaning and screening data, extracting a feature set containing a path point sequence, speed distribution and an acceleration contour; constructing an intelligent optimization algorithm model of a neural network structure, and training by using the feature set to learn a trajectory optimization strategy; analyzing a target position coordinate and a motion constraint condition of the current task to obtain an initial track parameter; and inputting the initial parameters into the trained model, outputting an optimized track sequence containing a path point list and a speed curve, and generating a control instruction to drive the robot to move. The method can adapt to different tasks, improves track rationality and motion stability, and fits industrial production practice.

The embodiment of the invention provides a robot control method and related equipment. The robot control method specifically comprises the steps that target action information used for controlling robot movement is generated based on a robot control task; performing security verification on the target action information based on the motion security constraint information of the robot; wherein the motion safety constraint information is used for constraining the motion range of the robot in the space from at least one spatial dimension; under the condition that the security verification is not passed, optimizing the target action information until the optimized target action information passes the security verification; and driving the robot to move by using the target action information passing the safety verification. According to the invention, unsafe robot behaviors can be reduced.

The invention discloses a decoupling control method for motion coupling between a track and a propeller, relates to the technical field of robot control, and realizes accurate and intelligent regulation and control of motion of a sea-land amphibious robot by constructing a complete closed-loop strategy from sensing, recognition, prediction to control. And the motion stability of the robot in a complex mixed environment is obviously improved. According to the method, the undesirable coupling effect between the crawler belt and the propeller can be anticipated and quantified in a prospective mode, suppression is conducted actively through self-adaptive control, impact, vibration and attitude instability caused by power conflicts in land and water transition or rugged terrain are effectively avoided, and stability and safety of a robot platform and loads of the robot platform are guaranteed. The energy utilization efficiency of the robot is greatly improved. And it is ensured that the two power systems work in the optimal cooperative state at any moment, and invalid energy internal consumption and redundant output are avoided.

The invention provides a robot cable fault classification method and system based on deep tree learning, and relates to the technical field of robots, and the method comprises the steps: collecting cable signals and joint position information, and calculating bending degree features; voltage, current, power and impedance features are extracted according to the motion state of the robot, and a state sensitive feature set is formed; constructing a deep tree learning classifier, and performing feature transfer through an attention connection mechanism; and carrying out local optimization and global optimization training by adopting a multi-scale splitting criterion. According to the method, cable faults in different motion states can be effectively identified, and the classification accuracy and robustness are improved.

The invention provides a touch and visual perception integrated micro-nano robot magnetic field control system and method, and the system comprises a camera which collects the image of a micro-nano robot in a pipeline in real time; the magnetic field generating device generates a gradient magnetic field for driving the micro-nano robot to move through a permanent magnet; the tail end of the mechanical arm is connected with the permanent magnet and adjusts the three-dimensional pose to change the magnetic field distribution; the force feedback equipment is used for providing a touch signal for the micro-nano robot to be in contact with the pipeline boundary; and the computer is configured to identify the aggregation state of the micro-nano robot according to the camera image, increase the resistance of the force feedback equipment and reduce the rotation speed of the permanent magnet when the dispersion state is detected, and reduce the resistance and recover the conventional magnetic field control after the aggregation state is recovered. According to the system, accurate control and abnormal state self-adaptive response of the micro-nano robot in a complex experiment scene are realized through touch-vision collaborative perception and dynamic magnetic field regulation and control.

The invention relates to the technical field of robots, and discloses a robot dynamic inspection control system based on anti-shake performance. The visual detection module is used for collecting image data of an inspection target; the motion compensation and anti-jitter module is used for monitoring the motion state of the robot in real time and compensating jitter generated by the robot; the environment sensing module is used for detecting and acquiring change data of external environment factors in real time; the data fusion and optimization control module is used for fusing data from the visual detection module, the motion compensation and anti-jitter module and the environment perception module; and the intelligent control module is used for fusing and optimizing the control instruction generated by the control module according to the data. By introducing the motion compensation and anti-jitter technology, the motion state of the robot is monitored and optimized in real time, interference caused by vibration is eliminated, the precision and stability of images and sensor data in the dynamic inspection process of the robot can be greatly improved, and the influence of external interference on a measurement result is reduced.

The invention discloses a robot automatic navigation method and system based on depth vision fusion, and relates to the technical field of intelligent navigation, and the method comprises the steps: carrying out the registration of a point cloud subset of continuous frames through employing an ICP algorithm, distributing weights for a static obstacle and a dynamic obstacle, and carrying out the fusion, and obtaining a fused environment perception data set. The method comprises the following steps: updating a dynamic environment in real time, generating a dynamic environment map, carrying out trajectory tracking on moving targets by using binocular vision equipment, calculating a real-time motion trajectory of each moving target, obtaining an environment hotspot map, generating a global navigation path for a robot by using an A * algorithm, planning a detouring path at the periphery of a hotspot area, and outputting a navigation path sequence. According to the method, intelligent optimization of the motion path of the robot is realized through path planning and an obstacle avoidance strategy, the safety is ensured, the path efficiency is maximized, and unnecessary detour and pause time is reduced.

The invention provides a robot production line article grabbing method based on visual localization, which comprises the following steps: preprocessing a multi-modal image to obtain an original image; performing grid mapping on the original image to obtain a multi-level feature descriptor; based on the multi-level feature descriptors, a dynamic mapping relation among the three coordinate systems is established through a non-rigid coordinate system alignment algorithm, and coordinate offset of movement of the conveyor belt is compensated in real time; performing hierarchical feature matching on the multi-level feature descriptors and an article template library, identifying article categories and extracting contour geometric features; based on the contour geometric features and the surface curvature distribution, the three-dimensional pose and candidate grabbing points of the object are obtained through a geometric constraint optimization algorithm; and generating a robot obstacle avoidance track according to the candidate grabbing points and the robot motion model. According to the method, accurate coordinate compensation is realized through multi-modal data fusion and dynamic adaptive grid mapping, and the article positioning accuracy is improved in combination with hierarchical feature matching.

Collision detection useful in motion planning for robotics advantageously represents planned motions of each of a plurality of robots as obstacles when performing motion planning for any given robot in the plurality of robots that operate in a shared workspace, including taking into account the planned motions during collision assessment. Edges of a motion planning graph are assigned cost values, based at least in part on the collision assessment. Obstacles may be pruned as corresponding motions are completed. Motion planning requests may be queued, and some robots skipped, for example in response to an error or blocked condition.

The invention discloses a medical double-arm robot task and motion planning method and system based on a large model, and the method comprises the steps: obtaining the pose information of a to-be-operated object of a double-arm robot through an RGB-D camera and an AprilTag marking system according to a given medical preparation experiment scene; based on the pre-trained vision-language large model GPT-4V and the designed environment understanding prompt project, the relative position relation of the two-arm to-be-operated object is obtained; environment understanding information, user task requirements and designed task planning prompt projects are input into the large model to generate a specific action sequence of the double-arm robot; analyzing information of a motion starting point and a target point corresponding to the motion sequence on the basis of a large model and a designed motion planning analysis prompt project; and calling a robot open source motion library to plan a feasible path from a motion starting point to a target point, obtaining a motion track of the double-arm robot, and performing safe execution. And the flexibility and the operation efficiency of the medical double-arm robot in a complex dispensing environment are improved.

The invention discloses a bionic walking feature fused quadruped robot gait reinforcement learning training method, which comprises the steps of S1, bionic gait feature modeling for extracting key features from a motion mode of a natural quadruped animal and constructing a bionic template capable of directly guiding robot gait control; and S2, constructing a reinforcement learning training environment, and simulating diversified actual scenes by constructing a high-fidelity simulation platform. According to the method, key features are extracted from natural four-footed animal gaits, a bionic template library is constructed, and natural features such as nonlinear rhythm and dynamic symmetry of animal movement are fused into robot gait control, so that the problem of action mechanical stiffness caused by dependence on manual design of a track in a traditional method is effectively solved; movement of the robot is closer to natural biological gaits, impact generated when the robot interacts with the environment is reduced while movement energy consumption is reduced, and movement smoothness is improved.

The invention discloses a biped robot gait generation method based on enhanced confrontation motion prior, and belongs to the technical field of robot control. Comprising the following steps: constructing an enhanced PPO-AMP network; establishing an action network model, an evaluation network model and an AMP module; establishing a biped robot motion model, and selecting a simulation experiment environment; performing reinforcement learning on the enhanced PPO-AMP network by using a reward function combining task rewards and style rewards and combining an improved PPO-Clip mechanism with KL divergence constraint on the basis of a simulation experiment environment to obtain optimized network parameters; the optimized enhanced PPO-AMP network is used for generating the biped robot gait meeting the actual walking task. According to the control method, the environment perception capability is optimized, the network structure is deepened, and the reward mechanism and the training strategy are improved, so that the biped robot has high robustness and high natural gaits on various complex terrains at the same time.

The invention relates to a robot self-adaptive control method and system based on visual radiation perception, and relates to the field of intelligent control, environmental data are obtained by integrating a laser radar, a plantar pressure sensor, a joint torque sensor, a binocular camera, an IMU and other sensors, and the environmental data are preprocessed and then input into an environment recognition model to obtain initial environment and attitude information; and constructing a local area map and planning an initial advancing path, evaluating the contact surface type by using a flexible capacitor array and a plantar pressure sensor, and finally matching a motion mode according to an advancing sequence and the surface type and controlling the robot to move. The technical problems that the robot is limited in environment sensing capacity, insufficient in autonomous decision-making capacity and poor in motion control flexibility in the complex, changeable and unknown environment are solved, and efficient environment sensing, autonomous path planning and flexible motion control of the robot in the complex environment are achieved.

The invention relates to the technical field of large-scale structure measurement, and discloses a robot laser vision three-dimensional scanning measurement system for a large-scale structural member. The system comprises a robot motion platform, a laser vision sensing module, an ambient light sensing module, a point cloud processing module and a motion control module, wherein the laser vision sensing module is arranged at the tail end of the robot motion platform. The ambient light sensing module collects ambient light intensity distribution data in real time, and the motion control module combines the data with three-dimensional model data of the large-scale structural member to be measured to generate a multi-view dynamic scanning path instruction. The robot motion platform executes an instruction to drive the laser vision sensing module to move, the module projects laser stripes to the surface of a to-be-measured structural member and synchronously collects an image, the point cloud processing module performs adaptive filtering processing on the image and then generates three-dimensional point cloud data, and the requirement for efficient and accurate measurement of a large structural member in a complex industrial environment can be met.

The invention discloses an improved robot motion planning method based on Jump Point Search (JPS), and relates to a robot motion planning method based on JPS. According to the method, the search direction and the path planning process of the JPS algorithm are optimized, the path direction guidance of BFS (breadth-First Search) is combined, and the search direction priorities are sorted, so that the overall search efficiency and the path security are remarkably improved. Specifically, according to the method, a cosine value is used for measuring the similarity degree of each search direction and a BFS path, and the directions are divided into high priorities and secondary priorities. In the hop point searching process, the heuristic function value only evaluates the extended hop points in the high priority direction, and selects the hop point corresponding to the minimum value, so that the extension in the redundant direction is reduced, and the searching efficiency is improved. In addition, the invention also designs a set of path optimization strategy which mainly comprises two parts, namely a'security node updating strategy 'and a'redundant node eliminating strategy'. Aiming at the dangerous boundary path points, a security node updating strategy is utilized, and adjacent nodes are replaced by common security neighbor points, so that paths with potential safety hazards are prevented from being formed; according to the redundant node elimination strategy, the number of control nodes is reduced and the overall path length is shortened by detecting the direct connectivity between continuous nodes.

The invention provides a robot motion path planning method and device based on machine learning, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining and storing the position information of an obstacle and a target point in an environment; constructing an artificial potential field according to the current position of the robot, the speed of the robot, the target point position, the target speed, the obstacle information and the motion state of the robot; the artificial potential field is optimized through a variable neighborhood search algorithm, and a reinforcement learning strategy is introduced into the variable neighborhood search algorithm; nodes on the basic path generated by the artificial potential field method serve as initial starting points of ants in the ant colony algorithm, and a final path is obtained through multi-round iterative optimization through an ant release pheromone mechanism, a transition probability selection mechanism and a pheromone volatilization updating mechanism; and evaluating the optimized path and dynamically adjusting the path. The robot motion path planning method can cope with complex environment changes, and effectively improves the safety and efficiency of path planning.

The invention relates to the field of intelligent robot control, and discloses an intelligent security robot control method comprising the following steps: building a robot motion and task model: building a dynamic relationship among the position, speed and acceleration of an intelligent security robot, and defining a multi-task target executed by the robot, wherein the execution time, the energy consumption and the priority of each task are expressed through a target function; the invention also discloses a control system of the intelligent security robot, and the system comprises a control decision module which is used for operating the multi-objective optimization function and carrying out the scheduling and path planning of multiple tasks executed by the robot, and an environment perception module which is used for obtaining the state information of the environment where the robot is located. According to the method, a dynamic optimization control strategy of the HJB equation is introduced, so that the robot can adjust paths and task scheduling in real time, dynamic environment changes are effectively coped with, and efficient and flexible task execution and real-time optimization in a complex environment are achieved.

The invention discloses a humanoid robot walking and running unified control method and system based on a biological rhythm, and belongs to the technical field of humanoid robots. Capturing human walking and running action data, and extracting rhythm features from frequency and phase time domains; constructing a rhythm generator based on the rhythm features extracted in the step 1; according to a strategy gradient method based on a constraint reinforcement learning algorithm, a walking and running unified control strategy is constructed, rhythm information generated based on a rhythm generator is adopted in the walking and running unified control strategy, then action-critic is used for training, a bionic motion control system Walk2Run under speed driving is jointly constructed, natural gait transition and frequency adjustment are achieved, and the walking and running unified control strategy is obtained. And meanwhile, the physical capacity limitation of the robot is met, and motion fluency and energy efficiency are improved. The problems that an existing humanoid robot does not achieve energy optimization of human walking during walking, and mobility of an existing data set has high requirements for the motion ability of the robot and is poor in generalization ability are solved.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The invention discloses a robot automatic mapping method and related equipment in an unknown open environment, and the method comprises the steps: obtaining sensor data, and constructing a local map according to the sensor data; topologizing the local map to obtain a collision-free topological graph of the local map; performing sparse processing on the collision-free topological graph to obtain a sparse topological graph; and inputting the obtained sparse topological graph into a strategy network model based on deep reinforcement learning, and outputting a strategy for controlling the robot to move so as to move the robot. According to the method, the environment mapping problem is modeled into a graph structure, spatial dependency in the environment is modeled through the relation between the nodes, and effective learning of the robot on the relation between different areas in the complex environment is achieved. In addition, the attention mechanism helps the robot to preferentially select the area with the high potential return in the autonomous exploration process by dynamically adjusting the attention weight of each area, and therefore the exploration efficiency is improved. The robot can be widely applied to the technical field of robots.

The invention relates to a wall-climbing laser cleaning robot weld joint identification tracking control method and device, and belongs to the technical field of robot tracking control. The maximum curvature of the arc length between the position point of the robot at the current moment and a preview point is determined, the maximum longitudinal speed corresponding to the maximum curvature is determined, and the preview point is any point on the weld joint track in the advancing direction of the robot; and determining a predicted position of the robot at the next moment according to the maximum curvature and the maximum longitudinal speed, and controlling the robot to move to the predicted position. The method solves the problem that the precision of a dynamical model and an operation control strategy of the wall-climbing robot is low because the wall surface motion control of the wall-climbing robot does not consider the influence of adsorption force and gravity.

The invention discloses an industrial robot motion state intelligent control method based on a twinborn model, and the method comprises the steps: collecting the motion state parameters, such as the joint angular velocity, the joint angular displacement, the position and posture of an end effector, of a robot through a multi-domain coupling twinborn modeling engine; transmitting to an industrial internet twinborn intelligent control center for classified storage, and establishing a parameter association mapping table; constructing a multi-domain coupling twinborn model based on the parameters and the mapping table, inputting motion state representation data output by the model into a deep enhanced evolutionary control algorithm with spatio-temporal attention enhancement, and generating multiple groups of control instruction candidate sets after spatio-temporal dimension features are extracted; and the center evaluates and screens the candidate set, selects an instruction meeting the requirement and transmits the instruction to a robot execution mechanism. The method depends on a space-time attention enhancement algorithm to improve the control precision so as to complete a control link to avoid a process fault, the intelligence and reliability of robot motion control are effectively improved, and the method is suitable for robot motion regulation and control under complex industrial working conditions.