540 results about "Impedance control" patented technology

The invention provides an igniter robot production line control method and system based on deep reinforcement learning, and relates to the technical field of robot production line control, and the method comprises the steps: collecting the real-time state data of a production line station, extracting features through a multi-scale residual neural network and a spatial pyramid pooling module, and carrying out the feature extraction; channel attention and space attention modules are combined to perform feature enhancement, a cross-modal encoder is used to perform time sequence modeling, task decomposition is performed based on hierarchical reinforcement learning, soft constraint trajectory optimization and adaptive impedance control are used to generate a station control instruction, and a multi-target Pareto evaluation and priority dual experience pool is used to perform sample screening. Network parameters are optimized and updated through a near-end strategy, and a production line optimization decision scheme is output, so that the production line control precision can be improved, the system robustness can be enhanced, and the optimization of the production efficiency is realized.

The invention relates to the technical field of grinding, in particular to a self-adaptive force control grinding system and method for a complex curved surface machining robot. Three-dimensional point cloud data of a workpiece are obtained through machine vision, gridding processing is carried out, and normal vector and curvature characteristic parameters are calculated; performing operation area division and operation path planning based on curvature characteristics; calculating operation parameters according to the normal vector and curvature characteristics; a self-adaptive impedance control algorithm is adopted, the polishing force is monitored in real time through a force sensor, and the rigidity and damping parameters of the robot are dynamically adjusted; and performing defect identification on the surface image of the processed workpiece by using the deep learning model, and when defects are identified, re-planning the operation path. According to the method, self-adaptive grinding of the complex curved surface workpiece is achieved, and the grinding efficiency and quality of the complex curved surface can be improved.

The invention discloses an upper limb exoskeleton rehabilitation robot control system and method, and relates to the technical field of rehabilitation robot upper limb control, and the method comprises the steps: collecting multi-modal data such as multi-channel myoelectricity, joint angles, angular velocity and interactive torque; constructing a sliding window feature vector, predicting a next joint angle increment through TCN and Bi-LSTM deep networks, and outputting intention confidence; and based on the confidence coefficient and the muscle fatigue factor, dynamically adjusting an impedance parameter, and combining an impedance control model to generate a power-assisted torque in real time so as to realize power-assisted torque output. By introducing an impedance adjustment mechanism of multi-modal perception, deep time sequence learning and myoelectricity driving, the bottleneck of a traditional upper limb rehabilitation robot in the aspects of intention prediction, parameter adjustment and training individuation is broken through, and the accuracy, safety and active participation degree in the rehabilitation training process are remarkably improved; wide clinical application prospects and industrial transformation values are realized.

The invention discloses a control method and device for a mounting and dismounting integrated robot of a sound barrier, and relates to the field of robot control. According to the method, a digital twinborn model and a dynamic environment model are constructed according to original sensor data, and a current environment risk value is determined; a mounting or dismounting sequence is generated based on a sound barrier topology rule, and a motion path and a mechanical arm motion track are generated according to the digital twin model, the dynamic environment model and the mounting or dismounting sequence; rehearsing the motion path and the motion track of the mechanical arm in the digital twin model, and when a rehearsing result meets a preset requirement, generating an initial instruction based on the motion path and the motion track of the mechanical arm; the current clamping force is determined through an impedance control algorithm, the current hoisting parameter is determined according to the load weight, the initial instruction is adjusted according to the current clamping force, the current hoisting parameter and the current environment risk value to obtain a control instruction, and the control instruction is executed. By implementing the technical scheme provided by the invention, the mounting and dismounting efficiency is improved, and the operation safety is enhanced.

The invention provides a humanoid robot action coordination method and system based on joint torque, and the method comprises the steps: obtaining the original data of the joint torque of a humanoid robot through a sensor, carrying out the preprocessing of the original data of the torque, outputting stable torque data, constructing an impedance control model through the combination of the angle data of the mechanical arm joint of the humanoid robot, and carrying out the control of the mechanical arm joint. Predicting a moment change trend to obtain a predicted moment sequence; adaptive threshold detection is carried out on the predicted moment sequence, and moment peak values of different amplitudes are identified; analyzing the propagation velocity of the torque peak value, and calculating a velocity vector of peak value propagation by combining the propagation direction of the torque peak value; whether the velocity vector of peak propagation exceeds a preset threshold value or not is judged, and if yes, the response trend of a joint torque controller is predicted to judge whether joint control parameters of the humanoid robot need to be adjusted or not; and if the joint control parameters of the humanoid robot need to be adjusted, optimizing the rigidity and damping parameters of the impedance control model to output a stable control signal.

The invention discloses a robot hierarchical reinforcement learning variable impedance control method based on vision and touch, and belongs to the field of robot control. According to the method, an upper-layer planning system and a lower-layer variable impedance control system are included, the upper-layer planning system fuses information from a visual sensor and a tactile sensor, feature coding is conducted on a visual image through a variational self-coding model, tactile feedback and mechanical arm state information are combined, and an action strategy adapting to the current environment and corresponding impedance parameters are formulated. And the lower-layer variable impedance control system realizes variable impedance control of the robot based on a depth kinematics and dynamics model, and allows the robot to dynamically adjust the motion and contact force according to the sensed information when interacting with the environment, thereby improving the adaptability to the environment change and the task execution efficiency. According to the method, the success rate and execution efficiency of the robot for executing the complex assembly task in the unstructured environment are remarkably improved, and high robustness is shown.

The invention belongs to the technical field of mechanical arm control, and discloses a mobile mechanical arm control method based on depth element imitation learning, which introduces a depth element imitation learning (DMIL) algorithm, and the algorithm combines depth element learning and imitation learning (IL) technologies to improve the adaptability and efficiency of a mobile mechanical arm in such tasks. The expert trajectory is elaborately made and carefully checked in a AppeliaSim simulation environment, and the interaction of the real world is evaluated at the same time. The position and the direction of the button lock are determined by adopting a deep learning 6D attitude estimation method, and key operation reference points are visually identified. In the whole IL stage, through integration of resistance inverse reinforcement learning (AIRL) and variable impedance control (VIC) technologies, a strategy for controlling operation of the mechanical arm is enhanced. The expert-guided trajectory and tangible force feedback data from real-world settings enhance this.

The invention discloses a force / position hybrid control method for a dexterous arm-hand robot, and aims to improve the tracking precision and flexibility of a high-degree-of-freedom robot in a complex interaction task. The method comprises the following steps: constructing a complete kinematics and dynamics model; designing a self-adaptive sliding mode impedance controller, converting an impedance equation into a system taking interaction force as a state, and introducing a sliding mode variable to realize stable tracking of expected force; a preset performance boundary function and a synchronization error are introduced to realize cooperative control and performance constraint; and finally, a force / position hybrid control structure is constructed, the outer ring adjusts the expected trajectory, the inner ring achieves accurate tracking, and therefore smooth interaction between the robot and the complex environment is achieved. The method has good robustness and adaptability, and is suitable for tasks such as grabbing and operation.

The invention discloses a robot control system and method based on vision and force sense fusion and a robot, and relates to the technical field of robots. The robot control system based on vision and force sense fusion and the control method thereof analyze visual features in real time through an artificial intelligence processing unit to predict mechanical parameters; and the self-adaptive impedance control unit is combined to dynamically fuse a position instruction and force sense feedback, so that precise adaptation of target object characteristics and compliant regulation and control of an interaction process are realized, real-time sensing and dynamic adjustment capabilities are achieved, assembly damage caused by mechanical mismatching is effectively avoided, and the reliability and efficiency of precise assembly are improved.

The invention relates to the technical field of automatic equipment control, in particular to an automatic equipment control system and method and a storage medium. The method comprises the following steps that a high-frequency laser scanner is used for scanning the surface of a target polishing part, B-spline surface fitting is carried out, and a surface fitting surface model is generated; controlling the polishing operation of the polishing industrial robot based on the surface fitting curved surface model, and performing polishing contact state detection to generate contact state point cloud data; based on the contact state point cloud data, grinding expected pose error analysis is carried out, reverse impedance error compensation is carried out, and a reverse impedance compensation amount is obtained; and the joint angle of the polishing industrial robot is corrected through the reverse impedance compensation amount, so that reverse impedance control over the automatic polishing equipment is achieved. By compensating the reverse impedance error of the automatic polishing equipment, accurate control over the automatic polishing process is achieved.

The invention provides a robot skill learning and control method and device oriented to rich contact operation tasks. The method provided by the invention comprises the following steps: collecting human demonstration data; the human demonstration data at least comprises end effector pose data and stiffness matrix data; on the basis of the end effector pose data and the stiffness matrix data, a generalization motion track and a generalization stiffness track are obtained through time alignment, probability distribution modeling and new state constraint adaptation; constructing an obstacle model through environmental perception, taking the generalized motion trajectory and the generalized rigidity trajectory as reference sampling candidate trajectories, screening a collision-free trajectory in combination with motion consistency, rigidity consistency and smoothness cost, and performing iterative optimization to obtain an optimized motion trajectory and an optimized rigidity trajectory; and converting the optimized motion track and the optimized rigidity track into a robot joint torque instruction based on variable impedance control, and controlling the robot to execute a rich contact operation task based on the robot joint torque instruction.

The invention relates to a robot dynamic grabbing control method based on visual sense and tactile sense depth fusion. The method comprises the following steps that firstly, based on visual information, surface geometric features of a target object are extracted, grabbing adaptability scores are calculated, an optimal grabbing point is selected, and a collision-free approaching track is planned; secondly, in a contact establishment stage, detecting a contact event through a touch sensor, fusing vision and touch data to unify a coordinate system, calculating a vision-touch consistency score, and applying an initial grabbing force; and thirdly, in the stable holding stage, slip detection is conducted through wavelet packet energy entropy and pressure gradient, and the grabbing force and impedance parameters are dynamically adjusted in combination with self-adaptive impedance control. According to the method, intelligent control over the whole process from approaching to contact to stable holding is achieved, and the adaptability, stability and safety of robot grabbing in the dynamic environment are improved.

The invention belongs to the technical field of signal processing, and particularly relates to a lower limb motion intention recognition method and system based on electromyographic signals. The method comprises the following steps: S1, arranging electromyographic sensors on a hip key muscle group, a knee joint key muscle group and an ankle joint key muscle group in sequence, and collecting electromyographic signals; s2, performing time-frequency-space domain feature fusion and dimension reduction processing on the electromyographic signals acquired at the hip key muscle group and the knee joint key muscle group, and performing corresponding motion intention prediction by using a dynamic weight distribution method and a decoder based on an LSTM-attention mechanism; electromyographic signals collected at key muscle groups of ankle joints are processed through a muscle force distribution matrix, and meanwhile a time-varying weight adjustment strategy and impedance control are adopted for corresponding motion intention prediction; s3, according to the predicted motion intention, the system controls a direct-current brushless motor on rehabilitation training equipment, a fuzzy self-adaptive PID control method is adopted to regulate and control joint motion, and a walking posture is simulated.

The invention discloses a traditional Chinese medicine internal medicine deficiency syndrome conditioning effect evaluation method and system based on a wearable device, and relates to the technical field of traditional Chinese medicine clinical informationization, and the method comprises the following steps: S1, building an electrode skin complex impedance time-frequency fingerprint, executing full-band sweep frequency perturbation, collecting complex impedance data in a continuous monitoring period, and carrying out the detection of the complex impedance time-frequency fingerprint; extracting resonance peak group and sideband coupling path information, and generating a self-excitation risk spectrum as a response reconstruction reference; and S2, performing anti-fact playback based on the self-excitation risk spectrum, replaying the conductance response in a non-resonance interval, comparing a stripping environment drift signal with contact pressure noise through the risk spectrum, and extracting purified anchor point data as an interface feature calibration reference. Abnormal signal recognition and positioning are achieved through complex impedance time-frequency fingerprints, self-excitation risk spectrums, anti-fact playback and micro-capacitance imaging, interference field regulation and control and self-adaptive impedance control are combined, the accuracy and stability of traditional Chinese medicine deficiency syndrome conditioning evaluation are improved, and the method has the high anti-interference capacity and the intelligent characteristic.

The invention discloses a constant force control method and system for a multi-joint flexible mechanical arm, and belongs to the technical field of robots. The method specifically comprises the steps that through a force sensor installed at the tail end of the flexible mechanical arm, the difference value of contact force between the tail end of the flexible mechanical arm and the environment and expected force serves as a force error, and the difference value of the change rates of the contact force and the expected force serves as the force error change rate; calculating a damping coefficient and a corresponding stiffness coefficient variable quantity; an impedance control model is constructed and solved, and an expected trajectory of the flexible mechanical arm is obtained; obtaining a track deviation and a speed deviation; a fast terminal sliding mode controller is constructed, and a control law is solved in combination with an extended state observer; and carrying out driving control on the flexible mechanical arm by utilizing the obtained control law to realize constant force control on the tail end of the flexible mechanical arm. According to the invention, the response speed of the system and the stability of the transition process are ensured, the problem of impedance coefficient selection is solved, automatic adjustment can be carried out according to real-time information, and the control precision, the response speed and the anti-interference capability are improved.

The invention discloses a space manipulator impedance control method based on joint hysteresis dynamic position compensation, and belongs to the field of space robot nonlinear dynamics control. The control method comprises the following steps: constructing a flexible joint space manipulator model based on a standard dynamic model of the space manipulator; according to the flexible joint space manipulator model, a virtual torque sensing strategy is designed based on motor side sensor information, and a joint torsion torque estimated value is obtained; building a joint hysteresis model based on a Bi-LSTM mixed hysteresis modeling method fusing multi-scale memory and an attention mechanism, and training the joint hysteresis model; inputting a joint torsion moment estimation value into the trained joint hysteresis model, and estimating a joint torsion angle; based on the estimated joint torsion angle, the joint angle measured by the motor side encoder is subjected to superposition correction, and track error compensation caused by joint hysteresis is achieved.

The invention discloses a self-adaptive variable impedance algorithm for hydraulic four-foot support unloading, which comprises the following specific steps: step 1, single-cycle variable impedance control: introducing a variable impedance control strategy in a single-step cycle, judging that a robot is subjected to large impact when a large force change rate is detected by a force sensor, and judging that the robot is subjected to large impact when the force change rate is larger; at the moment, expected force is rapidly adjusted to achieve force unloading, and therefore impact damage to structural parts is reduced; step 2, dynamic impedance coefficient adjustment: when it is detected that the impact force change rate exceeds a preset threshold value or the speed is increased, the impedance coefficient is dynamically reduced to reduce rigidity and improve flexibility, and rapid force unloading is assisted; and step 3, performing impact detection and real-time response. The hydraulic quadruped robot can dynamically adapt to complex tracks and stress changes, compliant control and whole-course stable operation are achieved, the unloading efficiency and the structural reliability are improved, the compliance and the movement capacity are also considered, and reliable support is provided for efficient walking of the hydraulic quadruped robot in the complex environment.

The invention discloses a glass bottle grabbing method and system based on visual inspection, and the method comprises the steps: carrying out the real-time recognition of a bottle opening, a bottle body contour and defect features through an image recognition algorithm, and outputting a detection data set containing the precise three-dimensional pose and feature identification of a glass bottle; based on the detection data set, generating an action parameter set containing the expansion and contraction amount, the rotation angle and the grabbing time sequence; according to the action parameter set, the movable guide rail is controlled to slide to a target area along the fixed guide rail, and self-adaptive adjustment of grabbing force is achieved through an impedance control algorithm; and after grabbing is completed, the conveying path is dynamically corrected through a model prediction control algorithm in combination with the real-time movement speed of the conveying belt, the conveying belt is accurately placed on a preset detection station, and a closed-loop grabbing-conveying process is formed. By utilizing the embodiment of the invention, high-precision identification, dynamic task allocation and self-adaptive flexible grabbing of the glass bottle can be realized, and the grabbing success rate and the production efficiency are improved.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The invention discloses an industrial part carrying control method and system based on dynamic path planning, and relates to the technical field of industrial equipment control, and the technical scheme is characterized in that an initial path satisfying suction constraint and joint torque limitation is generated by using a path planning algorithm and quadratic programming; the method comprises the following steps: acquiring three-dimensional coordinates of an obstacle, a part inclination angle and joint torque data in real time through a fusion sensor to perform local deformation adjustment on a path, constructing a multi-objective optimization function, and dynamically balancing an obstacle avoidance distance to obtain an optimal carrying path on the premise of ensuring a certain coincidence degree with an initial path; feedforward torque compensation is carried out according to the optimal carrying path, and compliant adjustment of the contact force is achieved in combination with impedance control. According to the technical system, the path re-planning efficiency of the system in a dynamic environment is improved, the joint torque fluctuation is reduced, and the part damage rate is reduced.

The invention belongs to the technical field of force / position hybrid control of mechanical arm systems, and particularly discloses a finite-time self-adaptive impedance control method and system for a flexible joint mechanical arm system. In order to solve the problems that the control effect of a mechanical arm system is affected by nonlinear and unknown system model parameters at present and derivation is complex in backstepping design, the fuzzy self-adaptive finite time impedance controller is designed based on the impedance control technology so as to achieve hybrid control over the track and force of the tail end of the mechanical arm. In the design process of the controller, a real control law is constructed to realize finite time stability of the system; and meanwhile, command filtering and fuzzy self-adaptive technologies are introduced to solve the problems of calculation explosion in backstepping design and unmodeled dynamics of a mechanical arm system. By means of the control method, the influence caused by system uncertainty can be effectively overcome, the position tracking error of the mechanical arm system is reduced, and meanwhile it can be guaranteed that all signals in a closed-loop system are bounded within finite time.

The invention discloses a robot variable stiffness surface path planning method based on three-dimensional perception and application. The method comprises the following steps: processing workpiece surface point cloud data; a surface defect detection model is established, and deformation deviation generated by workpiece surface defects in the point cloud data is compensated; and performing path planning analysis on the point cloud model to generate a self-adaptive tool path. In the process that the robot executes the planned path, passive analysis is conducted through a variable stiffness impedance control law under energy constraint, force control surface treatment is achieved by collecting a force feedback track as a reference and combining a quadratic programming optimization controller, and task execution of the robot planned path is completed. According to the method, a planned path model is combined, an AI auxiliary programming framework is designed, and a user parameterization task and tool are guided through a natural language interface and a knowledge representation engine. According to the method, the adaptive capacity of the robot to the complex variable stiffness surface is improved, the machining consistency and efficiency can be remarkably improved, and the wide application prospect is achieved.

The invention relates to the technical field of industrial control, in particular to an industrial stacker intelligent control system based on machine vision, and the system comprises a state resolving module which is used for carrying out the feature region decoupling of original observation data, and resolving a position deviation matrix and a deformation state variable for a rigid subspace and a flexible subspace respectively; the dynamic modeling module is used for identifying the first-order inherent frequency and the damping ratio of the mechanical structure on line and establishing a second-order impedance contact model; the impedance control module is used for generating an expected speed based on the position deviation and adaptively adjusting an impedance parameter according to a deformation state so as to carry out compliant modulation on a speed vector; and the estimation shaping module is used for correcting system time lag by using a Kalman observer and inhibiting residual vibration in combination with a zero-vibration input shaper. Through multi-dimensional physical field fusion and dynamic modeling, the problem that a rigid control model is mismatched with a flexible operation object is solved, and self-adaptive compliant control and vibration-free accurate operation of the stacking machine in the unstructured environment are achieved.

The invention discloses a dynamic environment robot adaptive control method and system, and relates to the technical field of robot adaptive control. According to the invention, dynamic compensation is realized based on multi-sensor fusion and real-time anomaly detection in combination with impedance compliance control; through a lightweight error model and a trajectory interpolation technology, the calculation efficiency is improved compared with traditional model prediction control, and high-frequency real-time correction is supported; the system adopts a modular design, and adapts to a heterogeneous robot platform by adjusting equivalent mass-damping-spring parameters; for sudden disturbance, trajectory smoothing and impedance control technologies are fused, the oscillation amplitude of the system is reduced, and execution jitter is effectively inhibited; according to the method, high-precision force tracking and stable control are achieved in scenes such as complex terrain walking and dynamic grabbing, the problems that traditional fixed parameter control response is lagged, model dependence is high and an actuator is overheated are solved, and the adaptive capacity of the robot to the dynamic environment and the task completion rate are remarkably improved.

The invention discloses a hand-eye-force integration-based catenary cantilever robot assembly method, which comprises the following steps of: synthesizing image sequences of different exposures into a high-quality image based on a self-adaptive multi-exposure fusion technology, identifying a workpiece contour in the image based on a deep learning framework, and calculating a pose of an identified part in a camera coordinate system; the two-dimensional camera and the three-dimensional camera are matched to carry out accurate scanning, an accurate pose deviation matrix of an actual workpiece relative to a theoretical model is obtained through a nearest point cloud registration algorithm, and a corrected moving target pose is calculated; and the robot moves towards the position and posture of the moving target, when the robot is about to make contact with the assembly workpiece, the impedance control mode is switched to conduct cantilever installation, and moment data are continuously monitored in the whole process till installation is completed. According to the method, the robot can intelligently cope with uncertainty in the cantilever assembly process, and the assembly success rate and the assembly efficiency are remarkably improved.

The invention relates to a rope-driven snakelike arm self-adaptive control method and system based on rigidity pre-distribution, and the method comprises the steps: obtaining the expected tail end rigidity and the actual tail end rigidity of a rope-driven snakelike arm, and building a total fitness function according to the rigidity of each joint of the rope-driven snakelike arm; taking the escape gradient as guidance, calculating the rope tension of each joint of the rope-driven snakelike arm by utilizing a total fitness function, and controlling the rigidity of each joint and the rigidity of the tail end by controlling the rope tension; a self-adaptive damping change rate is designed based on the contact force error to establish a self-adaptive admittance control model, and parameters of the self-adaptive admittance control model are adjusted through the contact force error monitored in real time; and a Lyapunov energy function is used for calculating the position correction amount to correct the reference trajectory, the corrected reference trajectory is converted into a control instruction, and the rope-driven snake-shaped arm is driven to move. According to the method disclosed by the invention, the self-adaptive impedance control of the rope-driven snake-shaped arm can be realized, the method is suitable for different environments, and the operation efficiency is improved.

The invention discloses a network construction type converter control method and system based on a virtual impedance feedforward controller, and the converter comprises a DC voltage source, a three-phase half-bridge converter, a low-pass output LC filter, a virtual synchronous generator control module, a virtual impedance control module, and a voltage and current double-closed-loop control module. The method specifically comprises the following steps: establishing a small signal model considering coupling on a network construction type converter based on virtual synchronous generator control; a small signal model for coupling is analyzed and considered, an active power loop, a reactive power loop and a coupling function all contain the same conjugate pole, synchronous frequency resonance can be caused, and coupling can intensify the synchronous frequency resonance; a feedforward controller containing virtual impedance is designed to increase system damping, and unstable factors introduced by the controller are inhibited by simplifying the controller; the damping and bandwidth of the system are comprehensively considered, and the setting condition of the virtual impedance is calculated. The device can restrain synchronous frequency resonance and has the advantages of being high in response speed and good in damping effect.

The invention relates to a self-adaptive interference pin assembling device and method, and belongs to the technical field of intelligent manufacturing and robotics.The self-adaptive interference pin assembling device designs a three-jaw centering clamp, a rotating disc is connected with three connecting rods, the three connecting rods synchronously move around the center point of the rotating disc, and the connecting rods transmit the rotating action of the rotating disc to sliding blocks; the sliding block is pushed to move towards the center point of the top plate or away from the center point of the top plate; the sliding blocks move to drive the clamping fingers to be opened and closed, opening and closing of the clamping fingers always keep the center of the top plate aligned with the pin, the clamping force can be adjusted, it is ensured that the pin is located at the center position, and high repeated positioning precision is provided. The six-dimensional force sensor is installed at the tail end of the mechanical arm, and force tracking in the assembling process is achieved; in addition, an impedance control law is used in the assembling process, and flexible pin assembling is achieved.

The invention relates to a flexible operation control system based on a mechanical arm control algorithm, and aims at solving the problems that an existing mechanical arm is insufficient in sensing capacity, poor in operation flexibility, low in multi-task scheduling efficiency and the like in a complex environment. The system comprises a multi-mode sensor module, a control algorithm module, a flexible execution module, a dynamic task allocation and scheduling module and a cloud collaboration and learning module. The multi-mode sensor module obtains comprehensive information of the operation environment of the mechanical arm in real time through a force and torque sensor, a visual sensor, a touch sensor and the like. And the control algorithm module adopts path planning, adaptive impedance control and model prediction control to realize dynamic obstacle avoidance, flexible grabbing and high-precision operation of the mechanical arm in a complex environment. And the flexible execution module is combined with a multi-degree-of-freedom mechanical arm and a flexible clamping device. And the dynamic task allocation and scheduling module optimizes the task sequence according to the task priority and the delay time. And the cloud collaboration and learning module continuously optimizes a control algorithm and a scheduling model through reinforcement learning.

The invention relates to the technical field of AMR robots, and provides an AMR robot-oriented anti-slip movement control method and an AMR robot, and the method comprises the steps: determining a target wheel with a slip trend based on the real-time driving torque, collected by a driving motor of the AMR robot, of each wheel; based on the four-quadrant self-adaptive impedance, the current driving torque of the target wheel and the wheel torque difference value between the adjacent wheels are combined, and self-adaptive impedance adjusting parameters of the target wheel are determined; corresponding impedance control is applied to a driving motor of the target wheel based on the self-adaptive impedance adjustment parameter of the target wheel, and the adjusted output torque of the target wheel is obtained; and the moving posture and the moving speed of the AMR robot are adjusted based on the adjusted output torque till the AMR robot does not have the slipping trend. The operation stability of the AMR robot in the operation process is improved.