144 results about "Joint angle" patented technology

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 relates to the technical field of mechanical arm control, and discloses a bionic self-adaptive variable-stiffness mechanical arm joint control method. The method comprises the steps that multi-source biological signals such as electromyographic signals, tendon tension signals and joint angle signals during joint operation are collected; constructing a stiffness feature vector based on the myoelectricity and tendon tension signals, and generating a motion trail sequence according to the joint angle signals; extracting a corresponding stiffness feature vector for each track point, and calculating stiffness sensitivity; constructing a bionic stiffness reference model containing a mapping relation between the stiffness feature vector and a joint angle signal, and dynamically weighting and adjusting the mapping relation by using stiffness sensitivity; predicting a joint stiffness change sequence based on the adjusted model and the real-time signal, and fusing the sequence with the current joint angle signal to generate a dynamic stiffness parameter; and calculating a driving torque correction amount according to the dynamic stiffness parameter and a preset safety threshold range, iteratively updating the model weighting parameter until the correction amount meets a convergence condition, and outputting a driving torque instruction to an execution mechanism.

The invention discloses a control method and system of a mechanical arm, electronic equipment, a medium and a program product. The control method comprises the steps that the target pose of an end effector under world coordinates and forward kinematics information of the mechanical arm are obtained; according to the first joint angle of the mechanical arm, the forward kinematics information and the first current pose of the end effector, the second current pose of the end effector under the world coordinates is determined; determining a loss function, wherein the loss function comprises a first loss function corresponding to the difference between the target pose and the second current pose; and based on the loss function, the first joint angle is updated through a gradient descent algorithm to control the mechanical arm to move. The mechanical arm is controlled to move without collecting training data in advance, and the calculation cost is reduced.

The invention relates to a mechanical arm control system and method, and belongs to the field of mechanical arm control, and the control system comprises a microkernel controller which calculates the joint angle of each joint of a mechanical arm according to the difference value between the current position and the target position of the tail end of the mechanical arm and sends the joint angle to a servo driver; the servo driver is responsible for closed-loop control of a current loop and a speed loop, accurately executes the position instruction sent by the microkernel controller and feeds back the actual state of each joint to the microkernel controller in real time; the multi-degree-of-freedom mechanical arm serves as an executing mechanism, receives the joint movement instruction from the servo driver, and drives an end effector to achieve any specified pose and complex track in a three-dimensional space through cooperative movement of a plurality of joints; and the instruction unit is used for communicating with the microkernel controller and issuing the target position information of the end effector of the multi-degree-of-freedom mechanical arm to the microkernel controller. By adopting the control method of the control system, the control precision and reliability of the mechanical arm can be improved.

The invention provides a training method and device for a VLA model, and relates to the technical field of intelligent robots with bodies, and the method comprises the steps that the VLA model outputs a joint angle sequence of a mechanical arm of a robot with a body based on visual input and language input; the differentiable forward kinematics module outputs a real-time pose of an end effector of the mechanical arm in a task space; based on joint space loss formed by the joint angle sequence and the joint angle in the teaching data, task space loss formed by the real-time pose and the tail end pose in the teaching data, and task space constraint loss, a multi-objective loss function is constructed, and total loss is calculated; calculating the gradient of the total loss to the joint angle sequence and the real-time pose through back propagation, and optimizing preset parameters of the VLA model; the above steps are repeatedly executed until the total loss meets the expectation, and a trained target VLA model is obtained; the technical problems that when a VLA model is trained in a joint space, the hardware coupling performance is high, and the generalization ability is insufficient are solved.

The invention discloses a robot dexterous arm teleoperation sharing control method and a related device, and relates to the technical field of robotics.The method comprises the steps that smooth expected pose information and expected angle state information are obtained, and pose information of a mechanical arm of a slave end at the current stage and angle state information and contact force information of a dexterous hand are obtained; based on the expected pose information and the joint angle information of the mechanical arm, the inverse kinematics of the mechanical arm is solved through a nonlinear optimization method, and a joint angle instruction of the mechanical arm is obtained; based on the expected angle state information, the angle state information of the dexterous hand and the contact force information, utilizing a fuzzy logic theory and a dynamic compliance primitive model to carry out adaptive variable impedance control on the dexterous hand to obtain a joint torque instruction of the dexterous hand; and feeding back the contact force information of the dexterous hand in the next stage to a main end operator. The success rate and the working efficiency of the teleoperation robot can be improved.

The invention discloses an intelligent planning method for a motion track of a multi-axis mechanical arm, and relates to the technical field of automatic control, and the intelligent planning method comprises the following steps: setting a task target and initial parameters of the mechanical arm, collecting environment data to generate an environment map, and constructing a dynamic environment model, the initial parameters of the mechanical arm comprise the initial position and posture, the joint angle, the speed and the acceleration; a mechanical arm track generation model is constructed, a mechanical arm preliminary track is generated based on the mechanical arm track generation model, the dynamic constraint of the mechanical arm preliminary track is checked, and the mechanical arm preliminary track is optimized; and predicting a future track of the obstacle according to the dynamic environment model, and combining the optimized preliminary track of the mechanical arm and the future track of the obstacle to generate a motion track of the mechanical arm. According to the method, through dynamic environment modeling, accurate pre-judgment of the motion trail of the dynamic obstacle is achieved, and the real-time adaptability of the mechanical arm in a complex scene is remarkably improved.

The invention discloses a VLA task space control method and system for a multi-degree-of-freedom mechanical arm, and relates to the technical field of intelligent robot control, visual information and language instructions when the mechanical arm executes a task are obtained, and a trained VLA model is adopted to obtain a target pose of an end effector in a task space; an IK solver corresponding to the mechanical arm configuration is adopted, and according to the target pose, multiple sets of joint angle solutions of all joints of the mechanical arm are obtained; selecting an optimal joint angle of each joint from the multiple groups of joint angle solutions by adopting a preset optimization criterion; and after the optimal joint angle sequence of the mechanical arm is subjected to track smoothing processing, the mechanical arm is driven to move. Mechanical arm motion control is carried out in combination with a VLA model and an IK solver, the problems that the generalization ability between different mechanical arm configurations is poor, the data collection cost is high and the model interpretability is insufficient are solved, and the generalization ability and data efficiency of mechanical arm control are improved.

The invention relates to the technical field of mechanical arm control, and discloses a mechanical arm force control method, system and device based on a current loop, and the technical scheme is characterized in that the mechanical arm force control method comprises the following steps: S1, obtaining the current value of each mechanical arm joint, and obtaining the joint torque according to the current-torque mapping relation; s2, according to the kinetic model, calculating a gravity item and a friction item in the joint torque to obtain the acting torque of the external force on each joint; s3, according to the acting moment of the external force on each joint, the tail end contact force is calculated in real time, the pose increment is obtained through processing, matrix transformation is conducted on the pose increment and the actual tail end pose, and the final pose is obtained; s4, performing inverse kinematics real-time solution on the final pose to obtain expected joint angle data, and performing torque feedforward control according to the expected joint angle data; the technical scheme of the invention has the advantages of low cost, fast response speed and strong anti-interference capability.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as mechanical arm grabbing, financial science and technology and medical health, and discloses a target control method, device and equipment based on multi-modal information and a medium, and the method comprises the steps that a language instruction expressing an operation intention is obtained, and visual information of the environment where a target object is located is collected; semantic representation is generated through the language processing module, and visual features are generated through the visual processing module; inputting the visual features and the semantic representations into an action generation module to generate an action sequence; and a joint angle sequence and a joint angle value are determined based on the action sequence, and the mechanical arm is controlled to execute grabbing operation. According to the method, the accurate grabbing action sequence is generated by fusing language and visual information, the movement of the mechanical arm is controlled by combining the joint angle sequence and the accurate joint angle value, and smooth track movement and accurate grabbing are achieved. The adaptive capacity and the industrial-grade grabbing precision in the complex environment are improved, and the production efficiency and the grabbing success rate are effectively improved.

The invention belongs to the technical field of mechanical arm joint control, and particularly discloses a preset performance self-adaptive joint control method for a rope-driven super-redundant snakelike mechanical arm, and the method comprises the following steps: carrying out kinematics modeling on a rope joint link of the rope-driven super-redundant snakelike mechanical arm, determining a relation between a rope speed and a joint angle, and determining a preset performance of the rope-driven super-redundant snakelike mechanical arm; calculating a joint jacobian matrix; designing a joint angle controller, defining an angle error and constraining the angle error in a preset performance range, and obtaining control input through an error transformation and return-to-zero dynamics principle so as to realize joint angle control; according to the method, a joint Jacobian matrix is estimated on line, and the joint Jacobian matrix is dynamically updated according to a rope speed error and a return-to-zero dynamic theory so as to adapt to the flexibility, time-varying characteristics and external disturbance of a rope, and the control precision is ensured. According to the method, it can be guaranteed that the joint angle of the rope-driven super-redundant mechanical arm can be accurately controlled under different working conditions, and it is guaranteed that the mechanical arm completes high-precision operation under the complex working conditions.

The invention discloses a singular avoidance control method and device for a six-axis mechanical arm based on virtual seven degrees of freedom. The tail end of the mechanical arm is provided with a virtual rotating shaft which coincides with the original point of a tail end coordinate system and is perpendicular to a rotating shaft of the tail end coordinate system. The control method comprises the steps that position information and angle information of all joints of the mechanical arm and the actual pose and expected pose of the tail end in the current detection period are obtained; based on the position information and the angle information of all the joints, the weight value of the virtual rotating shaft is correspondingly adjusted by combining the state that the mechanical arm approaches the shoulder singular point and / or the wrist singular point, a quadratic programming problem used for avoiding the singular points is reconstructed, and a to-be-executed joint angle increment vector is solved; and on the basis of the to-be-executed joint angle increment vector, the angle value of each joint of the mechanical arm in the next detection period is calculated, and the mechanical arm is controlled to move on the basis of the angle value of each joint so as to avoid shoulder singular points and / or wrist singular points of the mechanical arm.

The invention provides a mechanical arm control method and device, computer equipment and a storage medium, and the method comprises the steps that the target tail end pose and the initial joint angle of a mechanical arm are obtained; based on the target tail end pose, self-motion variables are introduced to parameterize the joint space of the mechanical arm, and multiple sets of candidate joint angles are obtained; constructing a joint motion cost function, and determining the total cost of moving from the initial joint angle to each group of candidate joint angles based on the joint motion cost function; traversing the value range of the self-motion variable, and screening out a solution with the minimum total cost from the multiple groups of candidate joint angles according to the joint motion cost function to serve as an optimal inverse kinematics solution; and a control signal is generated based on the optimal inverse kinematics solution, all joints of the mechanical arm are driven to move, and therefore the tail end of the mechanical arm reaches the target tail end pose. By the adoption of the method, the accuracy, economical efficiency and reliability of mechanical arm control can be improved.

The invention discloses a mechanical arm joint angular position self-adaptive specified time control method which comprises the following steps: step 1, establishing a dynamic model of a mechanical arm system, and initializing the state and control parameters of the mechanical arm system; step 2, designing an expected error trajectory of specified time convergence, and defining an error variable; and step 3, designing a specified time controller, and realizing rapid tracking of an expected angular position trajectory within specified time. According to the method, specified time tracking control over the angular position of the joint of the mechanical arm can be achieved, and meanwhile the controller does not need to be switched to guarantee continuity and boundaries of the controller; compared with a minimum upper bound of convergence time provided by limited / fixed time control, the method can realize convergence of the angular position error at an accurate specified time.

The invention relates to the technical field of robot motion, in particular to a variable load working condition heavy-load mechanical arm kinematics parameter calibration method and system. The method comprises the steps that joint angle parameters and theoretical Standard D-H model parameters are determined; the theoretical position of the end effector in the ideal state is determined; the actual position is determined, and the position error of the end effector is determined; the deflection under the preset load condition is determined, and corrected D-H model parameters are obtained; determining a load effect based on the parameter structure characteristics; introducing the load effect as a correction item into a parameter updating formula of a preset LM algorithm, and designing an adaptive compensation coefficient to adjust the contribution degree of the load effect; and dynamically adjusting the adaptive compensation coefficient through a convergence index in an iteration process of the LM algorithm. According to the method, the calibration precision of the kinematics parameters of the mechanical arm can be improved under the variable load working condition.

The invention relates to a joint angle prediction method and device, wearable lower limb exoskeleton equipment and electronic equipment, and relates to the technical field of exoskeleton equipment control. The method comprises the following steps: acquiring an electromyographic signal when a user walks by wearing the wearable lower limb exoskeleton equipment; classifying the electromyographic signals according to an electromyographic signal classification model, and determining a first electromyographic signal associated with the knee joint and a second electromyographic signal associated with the ankle joint; inputting the first electromyographic signal and the second electromyographic signal into a joint angle prediction model, and determining a joint angle of a knee joint and a joint angle of an ankle joint; wherein the wearable lower limb exoskeleton equipment performs joint control according to the joint angle of the knee joint and the joint angle of the ankle joint. Therefore, the electromyographic signals are disassembled into the signals related to all the lower limb joints, then the joint angles of all the lower limb joints are predicted through the signals related to all the lower limb joints, and therefore the joint angle prediction accuracy is improved.

The invention discloses a mechanical arm hierarchical motion planning method and device in a dynamic environment, and belongs to the technical field of mechanical arm motion planning. A target position of the mechanical arm is set, and a collision-free initial global path from the current position of each joint of the mechanical arm to the target position is planned; a continuous and smooth planned mechanical arm movement track is generated; generating the angular velocity control quantity of each joint of the mechanical arm; on the basis of avoiding the static obstacles, when it is detected that the moving range of the mechanical arm contains the dynamic obstacles, a dynamic obstacle avoiding frame is constructed in combination with a dynamic control obstacle function (DCBF); and a signal sequential logic STL is introduced into the dynamic obstacle avoidance framework to carry out task constraint, and the mechanical arm is controlled to carry out dynamic obstacle avoidance. The method is simple in step, high in execution efficiency and capable of guiding the mechanical arm to avoid obstacles and complete tasks in real time.

The invention discloses a task-driven active and passive fusion mechanical arm motion evaluation method and system, and the method comprises the steps: guiding a mechanical arm task, and collecting a joint angle data sequence; performing time alignment and smoothing processing to obtain an ideal demonstration track; inputting into a dynamic motion primitive model for learning to obtain model parameters; generalizing a new joint angle sequence conforming to the trajectory shape; a real-time control instruction is generated by taking the real-time control instruction as a reference track and adopting a reinforcement learning controller, and the mechanical arm is driven to track the reference track; and a multi-dimensional evaluation index system is constructed, and quantitative evaluation is conducted on the motion performance, the energy consumption efficiency and the intelligent level of task execution of the mechanical arm. According to the method, the robustness and the self-adaptive capability of the mechanical arm in different task scenes are remarkably improved, so that the efficiency and the safety of automatic operation are improved while stable operation is ensured.

The embodiment of the invention provides a trajectory optimization method and device, electronic equipment and a storage medium, and relates to the technical field of robots. The method comprises the steps that a preset trajectory of the tail end of a mechanical arm is obtained; interpolation is carried out based on points in a predetermined track, and a plurality of interpolation track points are obtained; aiming at a first track point in the starting point, the target point and the interpolation track point, determining a posture reachable interval which can be reached by the tail end of the mechanical arm at the position of the first track point; determining a target attitude for the first track point based on the attitude reachable interval of the first track point; and the posture of each first track point is updated to be the corresponding target posture, so that an optimized mechanical arm track is generated. Therefore, the task execution efficiency of the mechanical arm can be improved under the condition that the track point at the tail end of the mechanical arm has no inverse solution of the corresponding joint angle.

This application discloses a training method, apparatus, device, and storage medium for a joint angle prediction model, relating to the field of computer technology. The method includes: acquiring training sample data, which includes skeletal parameters, sample wrist joint pose sequences, and arm angle labels for the sample wrist joint pose sequences; obtaining arm angle feature information based on the sample wrist joint pose sequences using a selective state-space network of the joint angle prediction model to be trained; obtaining elbow position prediction information based on the arm angle feature information and skeletal parameters using a differentiable kinematic layer of the joint angle prediction model to be trained; and iteratively updating and training the joint angle prediction model to be trained based on a preset objective function, the training sample data, the arm angle feature information, and the elbow position prediction information to obtain a trained joint angle prediction model.

The invention provides a human body joint angle measuring method and device, and the method comprises the steps: obtaining the sensing data of a multi-mode sensor for synchronously monitoring a joint angle to be measured, obtaining the joint angle measured by various sensors based on the sensing data collected by the various sensors, and calculating the angle of the joint. Joint angles measured by various sensors are subjected to data fusion through a fuzzy neural network, and a final accurate joint angle measurement result is obtained. According to the method, the inherent limitation of the technology of measuring the joint angle through a single sensor is overcome, the joint angle is measured through multiple sensors used for measuring the joint angle, fusion is carried out, and advantage complementation is achieved. The invention further provides a multi-mode sensor which comprises an inertial measurement unit, a flexible strain sensor and an optical distance measurement sensor, and in the joint angle measurement process, the defect of a single sensor is eliminated through a data complementation and fusion algorithm.

The application relates to a multi-channel fused exoskeleton joint gait synchronous assistance control method, a system and a storage medium, wherein the method comprises the following steps: acquiring joint angle data and joint angular velocity data of a target user; inputting the joint angular velocity data into an adaptive oscillator to obtain an oscillation phase and an oscillation frequency; performing convergence determination on the adaptive oscillator according to the oscillation frequency to obtain a convergence determination result; fusing a first channel torque and a second channel torque according to the convergence determination result to obtain a target output torque; and driving an exoskeleton joint to generate assistance based on the target output torque. Through the application, the problem that an adaptive oscillator causes an assistance blank period due to a convergence process in the related art is solved, and assistance can be provided in each stage of user walking.

A motor torque control method for direct teaching of an articulated robot, in which, during direct teaching, joint angles and joint angular velocities of joints of the articulated robot when an end effector of a robot arm is moved to a teaching target position are acquired by a sensor; calculating a deviation of an end effector variable of the robot arm and a speed deviation of the end effector variable on the basis of the joint angle and the joint angular velocity of each joint acquired by the sensor; the control target value of the joint torque of each joint is calculated by a mathematical expression in which an inertia compensation term is omitted on the basis of the deviation of the end effector variable of the robot arm, the speed deviation of the end effector variable, and the joint angle of the robot arm, and the control target value of the joint torque of each joint is calculated by a mathematical expression in which the inertia compensation term is omitted. The motor torque of the motor built in each joint is controlled to the calculated control target value of the joint torque.

The invention provides a dexterous hand control method and system. The method is applied to the electronic equipment, the electronic equipment displays a virtual model corresponding to dexterous hand equipment, and the method comprises the following steps: detecting a dragging operation for a target joint of the virtual model; determining a first joint angle of each joint in the dexterous hand equipment according to the dragging operation; and sending a control instruction to the dexterous hand equipment, wherein the control instruction comprises the first joint angle of each joint. By means of the method, the entity dexterous hand device can be directly controlled through the virtual model, the control efficiency is improved, and the operation threshold is lowered.

The invention discloses a kinematics semi-analytical modeling method and terminal for a human wrist-simulated parallel mechanism, and the method comprises the steps: constructing a global coordinate system, a first coordinate system and a second coordinate system when the wrist parallel mechanism is subjected to kinematics positive solution, and then obtaining a target D-H expression corresponding to the tail end pose of the human wrist-simulated parallel mechanism, a target D-H expression is obtained, the target D-H expression shows the relation between the tail end pose and a first joint angle and the relation between the tail end pose and a second joint angle, then a target modular length expression is obtained, the target modular length expression shows the relation between a first modular length and the first joint angle and the relation between a second modular length and the second joint angle, and finally, the target D-H expression shows the relation between the tail end pose and the second joint angle. And obtaining numerical values of a first modulus length and a second modulus length, and substituting the numerical values of the first modulus length and the second modulus length into the target D-H expression and the target modulus length expression to obtain a tail end pose. The invention provides a solution capable of completing operation on a low-computing-power platform for a parallel mechanism simulating a human wrist.

To shorten time until a solution converges in iteration, in a method for controlling a robot's operation.SOLUTION: A method for controlling an articulated robot's operation includes: a first step of outputting a first joint angle that is a joint angle at the first time, regarding at least one joint; and a second step of outputting a second joint angle that is the joint angle at the second time after the first time by iteration, through the use of an initial value set by using the first joint angle output in the first step, so as to output the second joint angle, regarding the at least one joint.SELECTED DRAWING: Figure 4

The present application relates to the field of hydraulic mechanical arm, especially to a five-axis hydraulic mechanical arm control method based on inequality constraint, comprising the following steps: defining the generalized coordinates of the mechanical arm according to the improved D-H parameter method, establishing a dynamics model satisfying Newton mechanics or Lagrange mechanics, optimizing the dynamics model according to the inequality constraint condition to obtain the dynamics model of the mechanical constraint system, and adopting the space mapping mode to convert the master arm remote lever Cartesian space to the slave hydraulic mechanical arm Cartesian space to expand the operation space of the master robot on the slave robot. The present application adopts the DH parameter method to establish the base coordinate system with the base center point of the mechanical arm as the origin, and positions the position of the whole mechanical arm according to the joint angle, and then combines the inequality constraint mode to establish the mechanical arm controller, so as to reduce the error between the control parameter output by the controller and the actual operation state and the expected operation state.

The application provides a singular point avoidance method and device in a constant force control process of a mechanical arm, and the method comprises the following steps: a kinematic model of a target mechanical arm is established, and a calculation mode of a Jacobian matrix and an operation index is determined based on the kinematic model; a Cartesian space virtual force is calculated based on joint angles of the target mechanical arm and by using the calculation mode of the Jacobian matrix and the operation index; the Cartesian space virtual force is input into a constant force controller of the target mechanical arm to obtain a Cartesian space pose control amount; and the target mechanical arm is controlled to move by using the Cartesian space pose control amount until an operation index corresponding to the target mechanical arm is greater than a virtual force enable threshold. Therefore, the Cartesian space virtual force is introduced into the calculation of an input force signal of the constant force controller, so that the mechanical arm is subjected to repulsion from a singular region in the constant force control process, online singular point avoidance is realized, and the requirement of the constant force control on end pose keeping is met.

A method for optimizing the design of balancing force parameters of a manipulator follower suspension system comprises the following steps: designing a ground test manipulator configuration and path, and generating a joint angle data file; establishing a parameterized manipulator dynamics model in ADAMS, and importing the joint angle data file; further establishing a parameterized follower suspension system dynamics model; saving the model as a standard dynamics model for calculating the joint torque during the manipulator test under the effects of gravity and the follower suspension system; and designing an objective function, calling the standard dynamics model, and employing an intelligent optimization algorithm to determine the optimal balancing force of the follower suspension system.

The application discloses a mechanical arm disturbance observation method and device and a nonvolatile storage medium. The method comprises the following steps: determining a dynamic model of a mechanical arm according to system parameters of the mechanical arm, wherein the system parameters comprise the mass of the mechanical arm, the length of the mechanical arm and the joint angle position of the mechanical arm; converting the dynamic model into a task space model, wherein the parameters of the task space model comprise a known part and an unknown part, the known part comprises parameters determined by model calculation, and the unknown part comprises disturbance values to be observed; and determining the disturbance values of the mechanical arm according to the task space model and a disturbance observer model, wherein the observation error of the disturbance observer model converges to zero within a preset time length. The application solves the technical problem of affecting the operation stability of the mechanical arm system due to the fact that the existing disturbance observation cannot obtain results within a predetermined time.