393 results about "Robot motion control" patented technology

The invention discloses an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

The invention discloses a quadruped robot robust motion control method based on deep reinforcement learning, and belongs to the technical field of robot motion control, and the method comprises the steps: constructing a deep reinforcement learning model which comprises a state estimation network, a strategy network and a value network; interaction between the quadruped robot and the simulation environment is carried out, and standard observation information, historical observation information and privileged observation information of the quadruped robot at all moments are obtained; inputting the standard observation information, the historical observation information and the privilege observation information of the moment into a deep reinforcement learning model, and training based on a total loss function until convergence is carried out to obtain a trained deep reinforcement learning model; inputting standard observation information and historical observation information at corresponding moments in an actual scene into the trained deep reinforcement learning model to obtain output features of a corresponding strategy network; and the target position of each joint motor is calculated to complete the motion control of the quadruped robot. And efficient training and robust motion on various complex and unstructured terrains can be realized.

The invention discloses a quadruped robot motion control method based on adaptive deep reinforcement learning. The method comprises the following steps: S1, determining a network model, a composite reward function, a state space and a bionic action generation mechanism of a simulation training environment; the state space provides environment information input, the network model processes the input information and generates a decision, the bionic action mechanism executes a specific decision behavior, and the composite reward function evaluates a behavior effect and optimizes a decision direction; s2, constructing a simulation training environment of the quadruped robot, wherein the simulation environment comprises quadruped robot model information and simulation environment information; s3, training the network model by using a deep reinforcement learning algorithm based on robot model information and simulated environment information to obtain a trained motion control strategy; and S4, verifying the feasibility of utilizing the trained motion control strategy by controlling the motion of the quadruped robot in a real environment. According to the invention, the self-adaptive capability to a complex environment is obviously improved.

The invention discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The invention discloses a mobile robot control device giving consideration to field navigation patrol and transportation load, and relates to the field of robot control, and the device comprises a sensor data collection module which forms multi-source sensor data; the environment understanding module is used for predicting terrain risks and weather influences based on the feature vectors and outputting environment feature data and risk prediction; the navigation control module is used for navigating a path based on the environment characteristic data and generating a robot motion control instruction; the load management module adjusts parameters of the navigation control module based on the load state; the feedback adjusting module is used for dynamically adjusting control parameters according to the load state and feeding back the control parameters to the navigation control module to optimize and modify a robot motion control instruction; and the intelligent detection module is used for detecting environment abnormity and dangerous case events and outputting an alarm signal. The method has the advantages that accurate prediction of environmental risks is realized through multi-sensor layered feature extraction and attention weighted fusion, and motion parameters are dynamically adjusted in combination with a moment balance model.

The invention provides a robot motion control model training method, device and equipment based on deep reinforcement learning, a robot and a medium, and relates to the technical field of robots. The method comprises the following steps: acquiring a first potential vector obtained after a student encoder encodes robot body observation data, and a second potential vector obtained after a teacher encoder encodes privilege observation data; based on the current training step number and a preset probability function, calculating a sampling probability for controlling a fusion proportion of the first potential vector and the second potential vector; fusing the first potential vector and the second potential vector based on the sampling probability to generate a third potential vector, and inputting the third potential vector into a strategy network; and updating the parameters of the policy network based on the value estimation of the current state output by the value network and the action policy output by the policy network. According to the method, updating oscillation caused by sudden change of input distribution in the training process of the strategy network can be avoided, the training efficiency is improved, and the training cost is reduced.

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 intelligent robot with a body and a robot motion control system and method.The intelligent robot with the body is provided with an execution component, a moving module and the robot motion control system, and the robot motion control system comprises a multi-modal input encoder, a multi-modal output encoder and a multi-modal output encoder, the multi-modal feature fusion module is used for collecting and processing multi-modal data to obtain multi-modal features and fusing the multi-modal features to obtain a multi-modal feature token sequence; the action expert network is used for mapping the fused multi-modal feature token sequence into an abstract robot action sequence block; and the execution controller is used for converting the abstract robot action sequence block into a control instruction which can be executed by bottom hardware. According to the method, direct mapping from environment perception and semantic understanding to movement control and execution of specified task actions can be realized, so that the task understanding, response speed and execution completion degree of the robot in a complex environment are improved.

The invention provides a robot control system and method based on double cores, the robot control system comprises a real-time core, a non-real-time core and a memory, the memory is connected with the real-time core and the non-real-time core, and a shared storage space of the real-time core and the non-real-time core is arranged in the memory; the non-real-time core is used for executing a track information generation class task and writing a generated expected information group into a shared storage space; and the real-time core is used for calling the corresponding expected information groups from the shared storage space in sequence according to the writing sequence of the expected information groups, and executing a motion control type real-time task according to the expected information groups so as to drive each shaft motor of the robot. The real-time core and the non-real-time core communicate through the shared storage space, transmission of the expected trajectory information block is completed, the real-time requirement for obtaining the expected trajectory information block is guaranteed, interference of calculation fluctuation in the non-real-time core to a control thread in the real-time core is avoided, and the certainty and stability of robot motion control are ensured.

The invention relates to the technical field of robot control, and discloses a robot motion control method, device, equipment and medium, and the method comprises the steps: constructing a simulation environment and configuring a robot model, obtaining a depth image of an area in front of the robot model through a depth sensor, carrying out the semantic segmentation of the depth image, generating a semantic mask of a passable area, and carrying out the recognition of the passable area. And generating three-dimensional terrain point cloud data, extracting a semantic feature vector of the point cloud data, inputting the semantic feature vector and the body state information of the robot model into the motion control strategy model to generate a joint control instruction, and driving the robot model to move according to the joint control instruction. According to the method, depth image perception, semantic segmentation, three-dimensional point cloud reconstruction and feature extraction are fused, robot body state information is combined, a self-adaptive joint control instruction is generated, dynamic perception, decision making and control of robot motion in various complex terrains can be achieved, and the autonomous motion energy of the robot in an unstructured environment is effectively improved.

The invention belongs to the field of robot motion control, and particularly relates to a humanoid robot multi-terrain gait control method and system fused with visual perception. The method comprises the following steps: acquiring multi-modal sensing data of the humanoid robot, and preprocessing the multi-modal sensing data; the preprocessed multi-modal sensing data are input into a pre-trained world model, and the world model updates and outputs a potential state at the current moment based on a historical recursive state, a historical random posteriori state and an action sequence in a historical updating interval; inputting the potential state at the current moment into a pre-trained strategy network, and outputting an action at the current moment; and based on the action at the current moment, the driving torque of each joint is calculated through a PD controller, and the humanoid robot is driven to move. According to the method, a world model structure is introduced, so that the robot can realize more stable and more efficient gait control and terrain adaptation of the humanoid robot under the condition that the robot only depends on perception information which can be acquired by the robot.

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.

The invention discloses a double-foot wheel-legged robot pose control method and device based on hierarchical cooperative control, a medium and a product, and relates to the field of robot control, and the method comprises the steps that a hierarchical cooperative control framework is constructed; the layered cooperative control framework comprises a balance controller and a whole body attitude controller; the balance controller determines a reference wheel joint driving torque based on a simplified inverted pendulum dynamic model according to the pose information of the upper mass center of the biped wheel-leg robot; the whole-body attitude controller performs optimization control on all joints of the biped wheel-leg robot by adopting an incremental model prediction control algorithm combined with a reference wheel joint driving moment based on a whole-body dynamic model; state information of the biped wheel-legged robot is obtained; according to the state information, a driving torque instruction of each joint is obtained based on a hierarchical cooperative control framework; and driving the double-foot wheel-leg robot to move according to the driving torque instruction of each joint. The precision, robustness and real-time performance of robot motion control can be improved.

The invention belongs to the technical field of robot motion control, and particularly relates to a quadruped robot control method based on error symbol robust integral feedback, which combines an RISE control mechanism with a constraint-based optimization method, and realizes high-precision trajectory tracking and strong-robustness control under the condition that a system model has relatively large uncertainty. According to the method, RISE controllers are designed in a position subsystem and a posture subsystem respectively, quadratic programming (QP) optimization distribution of ground contact force is fused, control input is dynamically adjusted while the physical feasibility of foot end force is ensured, and closed-loop stable control over the whole-body movement of the quadruped robot is achieved. The control strategy has an asymptotic error convergence characteristic, and can be widely applied to quadruped robot motion tasks with severe load change and frequent interference.

The invention discloses a quadruped robot anti-disturbance motion control method based on reinforcement learning and application, and belongs to the field of robot motion control. Firstly, a self-adaptive propulsion control frame is provided, and a propeller and a leg movement system of the quadruped robot are deeply fused. And secondly, through cooperative training of a double-encoder system (a teacher encoder and a student encoder) and a predictor, hidden state extraction and environment disturbance reconstruction are realized, so that the robot adjusts a motion strategy in real time under complex scenes such as actuator faults and water flow interference. And finally, in combination with multi-stage reinforcement learning training and privilege information utilization, strategy network output is optimized, and a speed and trajectory tracking task is completed. The motion robustness of the robot under unknown interference and damage conditions is remarkably improved through cooperative control of the propellers and the legs, anti-disturbance decision of hidden state driving and dynamic reward function design. The method is suitable for amphibious environments, can adapt to diversified disturbance scenes without additional fine adjustment, and has high practicability and reliability.

The invention relates to the technical field of robot teaching, and discloses a robot infant teaching system and device based on somatosensory interaction. According to the method, a somatosensory data acquisition module acquires skeleton point motion time sequence data and an interactive behavior video stream of a target child through a depth camera and an inertial sensor; the multi-modal feature fusion module is used for extracting dynamic track features from the skeleton motion data, analyzing behavior semantic features of the video stream, and generating a fusion interaction feature set through cross-modal association fusion; the interaction sensitivity calculation module is used for calling a pre-training dynamic weighting network model to carry out attention distribution on the fusion features and generating a teaching interaction sensitivity coefficient set containing different teaching link response sensitivity distributions; the abnormal behavior tracing module is used for generating a teaching behavior abnormal tracing result for indicating an abnormal source link and a deviation type; and the interaction strategy generation module generates a self-adaptive teaching strategy according to the traceability result and sends the self-adaptive teaching strategy to the robot motion control unit.

The invention discloses a dual-drive self-switching strong-adaptability quadruped robot motion control method, which comprises the following steps of: estimating current topographic features by using sensing signals such as body state feedback, IMU (Inertial Measurement Unit) and sole force, and predicting a falling probability according to the topographic features, so that a quadruped robot is intelligently switched between two driving modes of MPC (Moving Picture Control) independent control and MPC-DRL (Moving Picture Control-Digital Regulation Language) cooperative control; dynamic behavior parameters are introduced to influence the gait and step height of robot movement, the optimization efficiency and smoothness of MPC and the disturbance adaptive capacity of DRL are efficiently fused, and the high-robustness, high-efficiency and self-adaptive movement capacity of the quadruped robot in various terrains under the condition that external sensing information is not relied on is enhanced.

The invention discloses a robot motion control optimization method, device, equipment and medium, and the method comprises the steps: inputting task description in a natural language form, robot state information, a historical execution track and environment feedback information into a preset large language model; through the large language model, selecting a target motion strategy of the robot according to the task description; when it is detected that the task of the robot fails or the action of the robot is abnormal, a reward function used for controlling a deep reinforcement learning controller of the robot is reconstructed; and when the state of the robot is abnormal, parameters of the deep reinforcement learning controller are adjusted and optimized, the motion stability, the task completion efficiency and the strategy adaptability of the robot in a high-risk, high-temperature and high-complexity scene are remarkably improved, and the self-adaptive adjusting and optimizing capability and the abnormity recovery capability of the robot in a dynamic environment are enhanced.

The invention discloses a robot motion control method and system based on cerebellum reinforcement learning, and the method comprises the steps: obtaining a current environment state vector, and inputting the current environment state vector to a main strategy channel and a cerebellum compensation channel in parallel; the main strategy channel outputs a basic action based on a long-term task target, and the cerebellum compensation channel outputs a compensation action responding to real-time dynamic through an efficient query mechanism; synthesizing the basic action vector and the compensation action vector into a synthesized action vector driving robot; feeding back latest data after the robot drives the motion action vector, and determining a sensory prediction error based on the latest data; and updating the original parameters of the cerebellum compensation channel based on the sensory prediction error, and optimizing the original strategy parameters of the main strategy channel based on the latest data. According to the invention, by constructing a parallel double-channel architecture, functional decoupling of advanced decision and rapid adaptation is realized, and unification of rapid adaptation and continuous optimization is realized through a double-loop hierarchical learning system.

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.

The invention provides a motion control method and device based on reinforcement learning, electronic equipment and a storage medium, and relates to the technical field of robot motion control. The motion control method based on reinforcement learning comprises the following steps: training and generating a teacher network model corresponding to a robot according to a robot model description file, road surface detection sample data and motion performance information, and determining student network training initial parameters corresponding to the teacher network model; training and generating a corresponding student network model according to the student network training initial parameters and the road surface detection sample data; deploying the student network model to the robot, analyzing a real-time image collected by the robot based on the road surface type classification detection network model, and determining a corresponding road surface type; and determining the target joint position offset of the robot based on the road surface type corresponding to the real-time image, the current road surface detection data and the student network model, and determining the joint torque of the robot according to the target joint position offset. The effect of controlling robot motion based on reinforcement learning is improved.

The invention discloses a hybrid robot adaptive motion control method and system, and relates to the technical field of robot motion control, and the method comprises the steps: obtaining the initial state of a robot system; establishing a nominal dynamics prediction model, and defining a safety set and a control barrier function set; statistical characteristics of model prediction residuals are obtained through online learning; correcting the dynamic model and generating a feed-forward compensation amount, and adaptively adjusting a safety set and a barrier function according to uncertainty; constructing a rolling optimization problem integrating the closed chain constraint and the barrier function hard constraint; solving the optimization problem to obtain an expected control quantity, and adjusting a safety parameter to obtain a rollback control quantity when solving fails; and synthesizing the feed-forward compensation quantity and the control quantity to generate a driving instruction. According to the method, the problem of model mismatching is solved through online learning and residual compensation, safety self-adaption is achieved through uncertainty, performance and safety are considered through a unified optimization framework, and the adaptability, precision and robustness of the system under dynamic disturbance are remarkably improved.

The present invention relates to a method for synchronizing movement control and location recognition for a micro-robot by using a bed-integrated electromagnetic field apparatus, and more specifically to a method for synchronizing movement control and location recognition for a micro-robot by using an electromagnetic field apparatus which enables precise movement control for a micro-robot and simultaneously enables location recognition for the micro-robot, and enables miniaturization of the apparatus, thereby having excellent compatibility with other medical equipment.

The invention discloses a humanoid robot joint motor control method with model prediction control, and relates to the technical field of robot motion control. The method is used for solving the problems of control instruction infeasibility and model disturbance caused by voltage saturation during high-speed motion. An error state space model of a joint motor system is established, and non-linear terms such as gravity are used as nominal feed-forward quantities for stripping; then, the rotating speed of the motor and the bus voltage of the driver are collected in real time, the back electromotive force is calculated, and the voltage margin is mapped into a dynamic torque physical limit value at the current moment; and meanwhile, estimating an unmodeled lumped disturbance value of the model by using an extended state observer. And finally, taking the dynamic limit as a real-time inequality constraint, introducing a disturbance value correction prediction equation, constructing and solving a quadratic programming problem, extracting an optimal compensation torque, superposing the optimal compensation torque with an inverse dynamic feedforward torque, and converting the superposed torque into a current signal to drive a motor, thereby realizing high-dynamic and high-precision robust control under the physical boundary constraint.

The embodiment of the invention provides a robot control method and system based on virtual reality and hand motion capture, and the method comprises the steps: collecting the hand posture data of a user, obtaining the first hand posture data of the user through a virtual reality device, the first hand posture data comprises the hand end posture information, and obtaining the first hand posture data of the user through a virtual reality device; obtaining second hand posture data of the user through wearable hand motion capture equipment, wherein the second hand posture data comprises hand joint fine motion information; standardizing the hand position and posture data, and mapping the hand position and posture data into robot target motion data based on a preset calibration matrix, the robot target motion data including first robot target motion data mapped based on the first hand position and posture data and second robot target motion data mapped based on the second hand position and posture data; robot motion control data is generated based on the robot target motion data, and robot parts are controlled to move. According to the robot control method and system based on virtual reality and hand motion capture, the operation precision of the robot can be greatly improved, the operation real-time performance and smoothness are enhanced, and the motion state of the robot can be monitored in real time.

The invention discloses a decoupling motion control method and system for a four-footed mobile operation robot considering the acting force of a mechanical arm, and belongs to the field of motion control of foot type mobile operation robots. An expected motion track input by a user can be tracked by performing planning through linear model prediction control MPC; on the basis of the tracked motion trail, mechanical arm joint control torque is calculated through a mechanical arm dynamic model and PD feedback; through nonlinear model predictive control NMPC planning, a quadruped robot whole-body motion trail of mechanical arm acting force obtained through calculation according to a mechanical arm dynamic model is considered, and an expected speed trail and an expected force trail input by a tracking user are obtained; and according to an expected speed trajectory and an expected force trajectory input by a tracking user, a whole body controller WBC based on hierarchical quadratic programming calculates a joint driving torque for tracking the expected trajectory according to task priorities. According to the method, the acting force / torque of the mechanical arm to the robot body is considered during motion control of the quadruped robot, and decoupling control over the mechanical arm and the quadruped robot in the quadruped mobile operation robot is achieved.

The invention discloses a robot application secondary development method supporting user behavior pattern self-learning, and relates to the field of robot application development, and the method specifically comprises the following steps: S1, data collection and treatment; s2, performing behavior abstraction; s3, mode learning; s4, performing double-domain scheduling during operation; s5, performing mode capitalization; and S6, low code reuse. According to the robot application secondary development method supporting user behavior mode self-learning, during operation, a double-domain isolation framework physically isolates a safety domain from a learning domain, and the situation that key operation of a robot is disturbed in the learning process is avoided; the scheduling algorithm combining the SMP and the EDF and a real-time monitoring mechanism can accurately control core indexes such as thread scheduling delay and interrupt response time, ensure that key tasks such as robot motion control and emergency fault processing are executed preferentially, meanwhile, parallel operation of behavior learning and execution is achieved, the strict requirement for real-time performance of an industrial scene is met, and the real-time performance of the industrial scene is improved. And the operation safety of the robot is ensured.

The invention relates to the technical field of robot motion control and trajectory planning, in particular to a robot motion trajectory planning method based on AI reinforcement learning. The method comprises the following steps: acquiring robot workspace layout information and an environment sensing probe candidate list, performing probe configuration planning, calibration and planning session index generation, and generating a planning session primary key recording structure; executing multi-source observation acquisition scheduling, data alignment and three-dimensional grid modeling, and generating a reinforcement learning state basic structure; state vector construction and continuous action strategy inference are carried out, and a candidate motion track set structure is generated; and track rolling simulation, grid collision risk assessment, multi-target reward calculation, target track selection and strategy parameter updating are carried out to generate a strategy network parameter set structure. Through combination of multi-source perception and reinforcement learning, intelligent planning of the motion trail of the robot is realized, the safety and efficiency of the trail are improved, and the adaptability and robustness in a dynamic environment are enhanced.

The invention belongs to the technical field related to robot motion control, and provides a robot motion control method and system in a microgravity environment in order to solve the problems of stability and sand blowing of robot motion under existing microgravity. A robot swing stage is divided into three sub-stage motions, and a robot foot end expected track is generated for each sub-stage motion; the robot state information, the historical track and the environment information are input into a pre-constructed joint control model, and a joint control instruction of the robot in the microgravity environment is obtained through prediction; the joint control model is a strategy network model trained in a reinforcement learning mode; the trajectory tracking reward is determined by the actual position of the robot and the expected position of the foot end of the robot. Stable, low-disturbance and high-efficiency movement of the robot in the microgravity environment is achieved, and meanwhile the sand blowing phenomenon is effectively restrained.

The invention discloses a motion control method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining the current motion state parameters of a robot in response to a motion control instruction for the robot, and enabling the motion state parameters to comprise the body state parameters and the foot end state parameters of the robot; determining a target centroid trajectory of the robot according to the motion control instruction and the motion state parameters; and controlling the robot to run along the target centroid trajectory. The robot motion control precision can be effectively improved.