300 results about "Impedance control" patented technology

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 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 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 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 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 discloses a robot Cartesian force damping control method and system based on a process energy mechanism, and the method comprises the steps: building a robot kinetic model, constructing an impedance control law and a force control law of a Cartesian space, and achieving the dynamic adjustment of control energy through combining a process energy container mechanism, global passivity and stability of the system in contact and non-contact states are maintained; the invention relates to the technical field of robot intelligent control and man-machine interaction. According to the robot Cartesian force damping control method and system based on the process energy mechanism, high-precision, safe and stable force control and damping adjustment can be achieved in the complex processes of polishing, assembling, wiping and the like, and a universal scheme with the energy self-management capacity is provided for safe man-machine cooperation.

The invention relates to a hexapod robot leg impedance control method and device, equipment and a medium, and relates to the technical field of automation control, and the method comprises the steps: training a primary function neural network through domain adversarial invariant learning based on a force-displacement compensation data set; a robot contact force deviation value is calculated, the contact force deviation value is input into the primary function neural network to extract environment invariant features, a Kalman filter is utilized to estimate an adaptive coefficient on line according to the environment invariant features, and a displacement compensation amount is calculated in combination with the environment invariant features and the adaptive coefficient; and the displacement compensation amount is injected into a robot impedance control loop, and leg movement of the hexapod robot is adjusted. According to the method, the environment invariant features are extracted, so that the method can better adapt to various complex terrains, the sensitivity to environment parameters is reduced, and the urgent requirements of the hexapod robot on high robustness, high precision and rapid self-adaptive contact control in actual operation are met.

The invention relates to the technical field of automobile electronics and intelligent control, and discloses an intelligent cabin mechanical arm system which comprises a folding mechanical arm body, a tail end sensing interaction module, a driver state monitoring subsystem and a central control unit which are installed in a cabin. The central control unit controls the storage or working mode of the mechanical arm according to the comprehensive fatigue index of the driver; in the working mode, visual gazing information is used as a gating signal to activate electroencephalogram analysis, the operation intention is confirmed in combination with the visual prior probability and electroencephalogram signals, and the mechanical arm impedance parameters are adjusted in real time based on the fatigue state. According to the method, through Bayesian fusion of vision and electroencephalogram signals, the problem that single-mode control is unstable is solved; by means of variable impedance control and collision detection of the momentum observer, active safety protection based on the physiological state of the driver is achieved, and non-sensitive and high-safety intelligent auxiliary experience is provided in a limited cabin space.

The invention relates to the technical field of industrial automation and intelligent manufacturing, and discloses a flexible packaging forming system and method based on a modular station robot, and the system comprises a central control module, a station robot module, a flexible material conveying module and a distributed intelligent detection module. The station robot module integrates functional units such as gluing, positioning and attaching, edge folding and frame erecting, foam scraping and flattening, precise gluing and assembling and cleaning. The method comprises the following steps: the central control module analyzes an order into a control instruction; the flexible material transmission module establishes a dynamic logistics network according to the instruction; the station robot module executes a full-process automatic forming process based on force control, visual compensation and impedance control algorithms; and the intelligent detection module feeds back quality data in real time to realize closed-loop correction. The problems that a traditional rigid production line is difficult to adapt to multi-variety small-batch production, the key process precision is insufficient, and accessory assembly depends on manual work are solved, and the flexibility level and the yield of packaging production are improved.

The invention discloses a space robot compliance control method and device based on multi-dimensional visual constraints, and belongs to the technical field of space robots and autonomous control. The method comprises the following steps: collecting and fusing data through a heterogeneous visual sensor group, and carrying out uncertainty quantification to generate a perception confidence index; constructing and dynamically adjusting a visual constraint function set based on the index, solving a motion feasible region according to the visual constraint function set, and generating an expected motion track; inputting the track into an impedance controller, and adjusting the rigidity and damping parameters in real time according to the sensing confidence so as to output an instruction; the instruction is verified through digital twin simulation, and if the instruction does not pass the verification, a fault-tolerant mechanism is triggered for optimization; and finally, updating the system parameters on line according to the deviation between the actual execution data and the simulation data. According to the method, the technical problems of poor operation safety, low control precision and insufficient autonomy of the space robot in a complex environment caused by single sensing information dimension, lack of uncertainty quantization and fixed control strategy are solved.

The invention relates to a force control-based weld joint ultrasonic detection method for a pressure container, and solves the technical problem of how to improve the precision, stability and reliability of defect detection of a weld joint of the pressure container by a mechanical arm driving an ultrasonic probe. A zero drift value of a six-dimensional force sensor and gravity components of an ultrasonic probe in six dimensions are calculated, then actual contact force and actual contact torque are calculated, the actual contact force is input into an impedance control module, force position control of a mechanical arm is achieved, cooperative compliant control over the contact force and the pose of the probe is achieved, and the mechanical arm is controlled. And the detection stability and reliability of the surface and internal defects of the welding seam are obviously improved.

The invention belongs to the technical field of intelligent assembly, and particularly relates to an active flexible assembly method for an electronic module, a mechanical arm controls the electronic module to move along a Lissaja curve under the current contact force, and meanwhile, the descending depth of the electronic module is monitored; when the descending depth of the electronic module changes, judging that the connector on the electronic module is aligned with the radio frequency interface; inserting the KK connector until the first-stage assembly is successful under the curvilinear motion along the Lissaja; and continuing to keep the pose for downward smooth insertion until the electronic module under the pose can reach the set depth of the second stage. Through combination of force-position hybrid control and impedance control, smooth control of the whole insertion process is realized, attitude compliance is improved, and initial impact is reduced. The Lissajous curve with adjustable amplitude, frequency and phase is adopted for high-density search, the jack position is actively captured, the problem that insertion cannot be achieved due to visual alignment errors is solved, and the jack center capturing flexibility is enhanced.

The invention relates to the technical field of intelligent robot control and artificial intelligence, and discloses a robot dynamic trajectory control method based on reinforcement learning and multi-sensor fusion, and the method comprises the steps: firstly fusing multi-modal sensor data through extended Kalman filtering, and predicting the time sequence distribution of dynamic obstacles through a long-short-term memory network; meanwhile, the dynamic operability and singularity risk factors of the robot are calculated, on this basis, a reinforcement learning network based on an attention mechanism is constructed to generate a high-level motion strategy, the high-level motion strategy is converted into a bottom-layer execution instruction through a variable impedance controller and a convex optimization torque distribution module, and singular points are avoided by using null-space characteristics. In addition, according to the method, model parameters are finely adjusted in real time by monitoring and predicting errors and the safety intervention degree on line. According to the method, organic fusion of logic decision and physical execution is realized through a layered architecture, and the operation safety, robustness and control precision of the robot in a dynamic environment are remarkably improved.

The invention relates to the technical field of circuit fault processing, and discloses an automatic identification and remote isolation method for a fault section of a low-voltage distribution line, and the method comprises the steps: building a reference fingerprint database based on channel state information through a central coordination node; monitoring real-time channel frequency response and executing threshold comparison to judge suspected fault points; triggering an active impedance control unit of the terminal agent node to execute controlled perturbation; a transmission line network model is used for calculating a theoretical transmission function variable quantity, fitting analysis is carried out on the theoretical transmission function variable quantity and a measured value, and a physical fault is judged and load interference is stripped through a normalized residual coefficient; calculating a residual drift distance between adjacent nodes based on the topological mapping table to execute differential positioning so as to lock a fault physical interval; and issuing a tripping message, and driving the circuit breaker to execute physical isolation at the power frequency zero crossing point. According to the scheme, on-line fault identification and isolation are realized through active impedance perturbation and distributed residual analysis, and the robustness of fault judgment in a complex load environment is improved.

The invention discloses a mechanical arm self-adaptive variable impedance constant force control system which comprises an inverse kinematics unit, a fractional order active disturbance rejection controller unit, a mechanical arm and a self-adaptive variable impedance controller unit, and the input of the system is a tail end expected position and expected contact force. According to the method, the force overshoot is remarkably reduced, the steady-state error approaches zero, and the force tracking precision and robustness are improved.

The invention discloses a dexterous hand fruit and vegetable grabbing control method based on fuzzy variable impedance control. The method comprises the following steps: firstly, establishing a multi-finger collaborative kinematics model based on a spinor theory, and determining fingertip poses, grabbing force of each finger and multi-finger resultant force distribution; on the basis of force sealing, an optimization model with the minimum contact force as a target is constructed, an NSGA-II algorithm is adopted for solving, and the minimum grabbing force needed by stable and lossless grabbing is obtained; establishing an impedance control model in a Cartesian space, enabling a grabbing system to be equivalent to a mass-spring-damping system, analyzing the influence of an impedance parameter on grabbing performance, and determining an inertial parameter and a damping coefficient as key adjustment objects; fuzzy control is further introduced, the grabbing force error and the change rate of the grabbing force error serve as input and output inertia and damping adjustment amount, a fuzzy self-adaptive variable impedance controller is constructed, and dynamic optimization of impedance parameters and accurate tracking of the grabbing force are achieved. The fruit and vegetable grabbing device can be effectively applied to rapid, stable and lossless grabbing of various fruits and vegetables in an unstructured agricultural environment.

The embodiment of the invention provides a virtual impedance compensation control method and a related device, and the method comprises the steps: obtaining a plurality of current values of other bidirectional energy interaction modules through a communication module if a first bidirectional energy interaction module is judged to be a host; determining a current mean value of the plurality of bidirectional energy interaction modules; the current mean value is sent to multiple other bidirectional energy interaction modules through the communication module; and executing a virtual impedance control strategy according to the current mean value. CAN bus communication is introduced, a plurality of bidirectional energy interaction modules are divided into a host computer and a slave computer, the host computer obtains current sampling values of the slave computer through the CAN bus, the host computer feeds back a current mean value to the slave computer, and the bidirectional energy interaction modules execute a virtual impedance control strategy according to the current mean value, so that reactive ring current can be suppressed, and the reactive ring current can be suppressed. And the problem of output voltage drop is eliminated through distributed current equalization, and the stability of the off-grid parallel system is improved.

The invention discloses a robot screw turning method and system based on a dynamic spiral field and compliance control. The system comprises a sensing module, a dynamic spiral field generator and a compliance coupling controller. The sensing module calculates parameters such as screwing-in progress, radial deviation and meshing confidence coefficient in real time through multi-sensor fusion and a state observer, and estimates the friction coefficient and the contact state. The dynamic spiral field generator adaptively adjusts the screw pitch, the spiral radius, the expected force and the expected speed according to the sensing state, and an intelligent screwing-in track is generated. And the compliant coupling controller adopts parameter self-adaptive axial admittance control and rotation impedance control, so that high-precision and high-compliant force-position cooperative operation is realized. The system can dynamically adapt to different screwing-in stages and abnormal working conditions, and the reliability, the self-adaptability and the operation precision of the screw turning process are improved.

The invention discloses a flexible material automatic feeding and accurate positioning control system based on machine vision, and belongs to the technical field of machine vision and intelligent decision making. Comprising a sensing and identifying module, a decision-making and planning module and a control and execution module. The perception and recognition module recognizes a material contour and quantifies a deformation parameter of the material by using a deep learning model through an image acquisition module and a flexible material recognition and state analysis module; the decision-making and planning module selects an optimal grabbing strategy from a feeding strategy library according to the deformation state and plans a collision-free path; the control and execution module realizes smooth and stable grabbing through the self-adaptive grabbing control module in combination with a visual feedback and impedance control algorithm, and performs real-time tracking and dynamic pose compensation on materials through the accurate positioning and placing module by utilizing a visual servo technology; the industrial problems of grabbing damage, placement drifting and the like caused by the fact that flexible materials are prone to deformation are solved, and high-precision and high-stability automatic feeding is achieved.

The invention discloses a reinforcement learning-based assembly force control method for a climbing assembly robot, and belongs to the technical field of space robot control. Aiming at the climbing and assembling tasks frequently performed by the space robot in the on-orbit assembly, the problems of low assembling precision and overlarge contact force in the docking process are solved by considering the particularity of an interface in the space assembling docking task, so that the robot can realize the effective effect of smooth docking, and the working efficiency is improved. The method comprises the following steps: establishing a kinematic model of the climbing assembly robot; constructing an impedance controller, setting an impedance parameter, and taking the impedance parameter as a learning environment of the intelligent agent; creating a reinforcement learning agent, and setting an action space, a state space and a reward function; constructing an overall control framework, setting agent hyper-parameters, training parameters, maximum training times and the like, starting training and storing the agents; and realizing compliant control of the docking process by using the trained intelligent agent.

The invention discloses a body-equipped robot control system and method based on multi-modal perception and deep learning, and the system comprises a multi-modal perception module which is used for obtaining and fusing visual information, tactile information and proprioceptive information, and generating a unified state representation; the deep reinforcement learning control module adopts a reinforcement learning algorithm based on an actor-commentator framework and is used for representing an output control strategy according to the unified state, and the reinforcement learning algorithm adopts an experience playback mechanism and a target network technology to realize stable learning; the hierarchical motion planning module is connected with the deep reinforcement learning control module and is used for decomposing a complex task corresponding to the control strategy into high-level strategy planning and bottom-level motion execution; the self-adaptive impedance control module is connected with the hierarchical motion planning module and is used for dynamically adjusting impedance parameters of the robot according to interaction information of the robot and the environment; and the safety guarantee module is used for applying constraint optimization to the control strategy or the motion execution so as to ensure the operation safety of the robot.

The present disclosure relates to ultra-high density, low profile edge card connectors. The present disclosure increases interconnect density by using a different technology approach than currently used in the industry (stamping and molding). By using a microelectromechanical system (MEMS) technology approach, better geometry and impedance control is achieved to reduce impedance discontinuities and feature sizes. In addition, the present disclosure also includes low connector insertion force, no contact wiping, and precise alignment mechanisms between the connector contacts and the connector contacts on the mating substrate.

The invention relates to the technical field of robot micro-operation, and discloses a micro-operation tail end system integrating multi-dimensional micro-force sensing and visual servo, which comprises a data acquisition module used for synchronously acquiring binocular visual images and six-dimensional force data; the calibration registration module is used for establishing a multi-sensor unified coordinate system; the visual pose estimation module is used for realizing three-dimensional reconstruction and feature point extraction based on a deep neural network and structured light; the state fusion estimation module fuses vision and force information through an extended Kalman filter and judges a contact state; the cooperative control module is used for generating a motion instruction in combination with task priority and impedance control; the parameter self-adaptive module is used for identifying materials on line and self-adaptively adjusting force control parameters; and the motion control module adopts active damping control to suppress vibration. According to the invention, high-precision and multi-degree-of-freedom micro-operation can be realized in a narrow and limited space.

The present application belongs to the technical field of robot control, and particularly relates to a stability judgment method of a robot system variable impedance model using a variable augmented matrix, which enables the robot to verify its stability offline when the stiffness changes. Impedance control is a general force control scheme suitable for various manipulator tasks, one of which is human-robot collaboration: the manipulator performs force-position hybrid control on the object according to the impedance control law, while the operator applies control force to the object or the robot to realize human-robot collaboration and complete work such as assembly, carrying, etc. According to different manipulator tasks, variable stiffness parameters can be used to reflect flexibility and compliance. Not only can the dynamic relationship between external force and robot motion be controlled, but also these dynamic behaviors can be flexibly changed in a continuous manner during the task. Impedance control can be implemented in joint coordinates, Cartesian coordinates or other task-specific coordinates. Impedance targets can be achieved in different ways.

The invention discloses a grinding robot rigidity self-adaption constant force impedance control method and system and a storage medium, and relates to the technical field of robot control, and the method comprises the following steps: firstly, obtaining original point cloud data of a to-be-ground workpiece through a camera, carrying out filtering noise reduction and down-sampling processing on the original point cloud data, and fitting a workpiece curved surface; secondly, establishing an impedance control model between the tail end of the robot and the workpiece, and constructing a complete control law; secondly, a three-dimensional vision system is introduced to obtain workpiece surface information, and a rigidity and position self-adaptive estimation mechanism integrating vision and a force sensor is constructed; and finally, designing an adaptive law based on a Lyapunov function to guarantee global asymptotic stability of the system. The robot can stably apply constant grinding force under the conditions of a complex curved surface and rigidity change, and the grinding precision, the force control stability and the system self-adaptive capacity are effectively improved; the method is suitable for free-form surface high-precision grinding scenes in the fields of aviation, military industry and the like.

The application relates to the technical field of intelligent manufacturing, and discloses a dynamic assembly impedance control method and system based on deep reinforcement learning, which are used to improve the overall performance in the engineering implementation process. The method comprises the following steps: an FPGA acquires state information collected in real time in an assembly environment, and inputs each state information into an action network of a DRL intelligent agent after preprocessing; the action network calculates impedance parameter increments through forward propagation according to the input information, outputs a compliant instruction of a virtual impedance controller according to the generated impedance parameter increments, and then sends the compliant instruction to a CPU to schedule an assembly execution mechanism to execute; the impedance parameter increments include increments corresponding to stiffness, damping and feedforward force respectively, which are jointly constrained based on safety projection and change rate limitation; a database management server integrates the impedance parameter increments, state information before and after the corresponding execution actions and obtained reward values into samples and stores the samples in an experience replay buffer for DRL intelligent agent updating.

This application relates to the field of robot motion control and safety protection, and discloses a method for preventing hand pinching in a robot servo system. This method monitors the motor status in real time. When suspected interference is detected, a composite detection signal consisting of a base holding voltage, a DC preload component, and an AC detection component is injected into the drive motor. Through open-loop feedforward control, the feedback current is collected and the same-frequency component is extracted. The phase lag difference relative to the injected signal is calculated. Based on the magnitude of the phase lag difference, the system identifies whether the obstructing object is made of rigid or flexible material, and then executes a graded protection strategy of constant torque limiting or variable stiffness impedance control, respectively. This invention utilizes the principle of motional impedance to establish a mapping relationship between electrical phase and mechanical impedance, solving the problem of difficulty in distinguishing contact materials under low-speed conditions, achieving active compliance and safe escape, and improving the safety of human-machine interaction.

This invention discloses an adaptive impedance control method and device for a teleoperated robot system. The method is based on a fuzzy logic-based intelligent control mechanism. It dynamically generates a correction update rate for online compensation adaptive terms through fuzzy inference, based on real-time acquired master-slave state information and preset control parameters. This correction update rate, real-time state information, and preset parameters are integrated, and the core dynamic equations of the preset impedance control model are solved to calculate the robot's target position within the current control cycle. This method, based on the robot following the operator's hand movements, superimposes a position compensation amount optimized by the adaptive impedance control model. When problems such as operator hand tremor or network transmission delay cause a sudden increase in contact force, making it impossible to effectively track the desired contact force, the adaptive impedance control device adjusts the contact force, achieving refined teleoperation control.