70 results about "Inverse dynamics" patented technology

The invention discloses a robot dynamics physical information prediction neural network construction method based on motor and external force coupling, and relates to the technical field of robots. Comprising the following steps: embedding a motor kinetic equation into a DeLaN network, and establishing an explicit dynamic mapping relation between a system motion state and a motor current through mapping between the motor current and a joint torque; introducing an external force item by fitting a Jacobian matrix, and constructing approximate mapping of the Jacobian matrix by adopting a data driving method; and an initial model is constructed, and after motor dynamics and external force modeling are incorporated into the initial model, an inverse dynamics model of the mechanical arm is obtained. Compared with an existing most advanced method, the method has higher modeling precision and generalization ability and better control performance. The innovative scheme provides solid technical support for realizing real-time accurate control in the field of robots.

The invention discloses a wheel-legged robot whole-body motion control system and method considering slippage steering, and relates to the technical field of wheel-legged machine control. The system comprises an input module used for receiving an expected motion track or a motion instruction from an upper layer planning system; the trajectory optimization module takes the motion trajectory output by the input module as a tracking target, so that the platform can be optimized in real time along an offline trajectory; the tracking control module is used for solving a joint torque through inverse dynamics by adopting a total rigid body dynamic model, realizing motion tracking through a whole body control WBC method, and solving a motion and contact force priority task by using a hierarchical optimization method; and the state estimation module is used for estimating all current state quantities of model predictive control MPC and information required by whole body control WBC through sensor information. By means of the system, high-dynamic stable motion control over the wheel-legged robot under the complex terrain can be achieved.

The invention relates to a robot standing posture recovery method and device, equipment, a storage medium and a product, and the method comprises the following steps: after the falling state of a robot is detected, collecting joint angles in real time to obtain falling posture data, and calculating contact points with the ground to determine a support part set; in combination with a preset zero moment point reference trajectory, performing inverse dynamics solution based on the set to generate a joint motion sequence, and configuring the joint motion sequence as joint position instructions to form a getting-up planning trajectory; the current output torque of each joint is compensated in real time according to the track, a driving torque instruction is obtained, each joint motor is driven to execute actions, so that the robot is recovered from the current posture to the standing posture, and the problem that the preset rigid action is often not matched with the current actual supporting state when the robot falls to the ground, and consequently the robot cannot stand up when executing the getting-up action is solved. And center-of-gravity unbalance is easy to occur.

The invention provides an unmarked action acquisition and analysis method and system, and the method comprises the steps: recognizing static and dynamic 2D key points according to the static and dynamic image data of a tester, and reconstructing the static and dynamic 2D key points into static and dynamic 3D key points; based on the static 3D key points, calculating and optimizing the skeleton length, and performing scaling adjustment on the standard skeleton and muscle model to obtain a static skeleton and muscle model of the testee; estimating skeleton motion attitude information according to the original and optimized dynamic 3D key points, and matching the skeleton motion attitude information to a static skeleton and muscle model of a tester to perform inverse kinematics analysis; estimating ground support force GRF of left and right feet by using an inverse kinematics analysis result based on a dynamic balance relationship of human motion; on the basis of an inverse kinematics analysis result and ground support force GRF estimated values of the left and right feet, counter-acting force and torque required by each joint of the human body are calculated frame by frame by adopting an inverse kinematics method of a rigid body system, and then muscle strength and activation degree are calculated. According to the invention, unmarked action acquisition and analysis can be carried out.

The invention belongs to the technical field of mixing equipment, and particularly relates to a non-intrusive homogenizer operation process abnormity monitoring method and system, and the method comprises the steps: calculating the mechanical angular acceleration according to the real-time rotating speed of a motor shaft end; on the basis of a pre-constructed inverse dynamic model, according to the motor orthogonal axis current, the motor shaft end real-time rotating speed and the mechanical angular acceleration, a rheological resistance characteristic index is calculated; processing the rheological resistance characteristic index by using a singular spectrum analysis algorithm, and extracting a trend line of the rheological resistance characteristic index through construction of a trajectory matrix, singular value decomposition and diagonal averaging operation; the information entropy of the first-order difference sequence of the trend line is calculated to serve as the mixing stability entropy, and the mixing end point of the operation process of the homogenizer is judged according to the convergence condition of the mixing stability entropy relative to the reference entropy, so that anomaly monitoring of the operation process of the non-intrusive homogenizer is achieved. According to the invention, abnormity monitoring and end point determination of the mixing process of the homogenizer are realized.

The invention relates to a balance control algorithm for gravity center trajectory planning and real-time tracking of a humanoid robot, in particular to the technical field of robot control, and realizes dynamic balance control through a closed-loop process of state sensing, online gravity center trajectory re-planning, hierarchical control law calculation and joint torque output and execution. A layered dynamic modeling and control framework is constructed, a variable-height inverted pendulum model is combined with model prediction control, and a gravity center and zero moment point track feasible in dynamics is generated online; virtual restoring force is generated through a gravity center impedance controller, a task priority inverse dynamics solver is used, multi-task execution is coordinated on the premise that balance tasks are guaranteed to be absolutely preferential through a null-space projection technology, theoretical torque is converted into current instructions to drive joints to move, and through efficient collaboration and real-time updating of dynamic models of all levels, the balance tasks are optimized. According to the invention, deep collaboration of various technical features is realized, and the problems of poor adaptability, slow response and poor balance capability of a traditional method in a dynamic environment are effectively solved.

The invention discloses a vehicle transverse and longitudinal trajectory tracking collaborative optimization control method and system. The method comprises the following steps: establishing a vehicle monorail model and an error calculation module; respectively designing a transverse MPC controller, a longitudinal MPC upper controller and a lower controller based on an acceleration-driving force inverse dynamics model according to the vehicle monorail model; carrying out normalization processing on the transverse error and the yaw angle error which are calculated by the error calculation module, and taking a normalization processing result as a fitness function of a pelican algorithm; establishing a Latin-pelican optimization algorithm fusing population initialization of sine-Latin hypercube sampling, prey position updating of a grade system and a dynamic random search strategy; a fitness function and a Latin-pelican algorithm are utilized to optimize a transverse MPC controller and a longitudinal MPC upper controller respectively, physical mapping of control quantity is achieved in combination with a lower controller based on an acceleration-driving force inverse dynamics model, optimal weight matrix parameters are obtained, and transverse and longitudinal joint trajectory tracking control of the vehicle is completed.

A robot control method includes a collision detection method for detecting an external force by subtracting a kinetic torque and a friction torque calculated value obtained by an inverse kinetic calculation of the robot from a torque output from a motor to a decelerator in a robot driven by the motor via the decelerator. When the detected value of the external force is higher than a predetermined threshold value, it is determined that the robot has collided. In the robot control method, when it is determined that there is no collision, a friction estimated filter value obtained by smoothing a friction torque estimated value used for setting a friction torque calculation value is updated on the basis of a friction estimated filter value prior to a predetermined time. The friction estimation filter value before and after the update is stored as a friction estimation stored value. When it is determined that there is no collision, the friction estimation filter value is returned to the friction estimation saved value before update.

The invention provides an unmarked kinetic analysis method and system for real-time tracking and shooting of an unmanned aerial vehicle, and the method comprises the steps: carrying out the flight control of the unmanned aerial vehicle, and obtaining the dynamic image data, tracked and shot by the unmanned aerial vehicle, of a sporter running on a playground curve or a curve path; synchronizing and preprocessing the dynamic image data recorded by different cameras; extracting 2D key points of a human body from the preprocessed image, and reconstructing a three-dimensional motion track of the human body in a local coordinate system of the camera; carrying out posture compensation on the three-dimensional motion trail, ensuring the consistency of human body joint point trails under a ground world reference system, carrying out human body model scaling and muscle model scaling calculation, and carrying out inverse kinematics analysis; according to the inverse kinematics data, the centripetal acceleration of the sporter in the curve motion is calculated; and performing inverse dynamics calculation based on the centripetal acceleration, and calculating the ground support force. According to the invention, unmarked dynamic analysis of real-time tracking shooting of the unmanned aerial vehicle can be carried out.

The invention discloses an athletic injury risk screening system based on motion capture and biomechanical analysis, and relates to the field of athletic injury risk screening, and the system comprises a standardization module which is used for establishing a grouping reference value database and a to-be-detected person exclusive file; the data acquisition module is used for acquiring three-dimensional action data of a person to be detected and preprocessing the data; the feature vector module is used for calculating joint feature parameter values of the moving joints in three moving planes based on an inverse dynamics method and constructing biomechanical feature vectors; the model construction module is used for constructing an athletic injury risk assessment model and outputting a comprehensive risk level and a specific risk mode label and probability; and the model training and verification module is used for performing staged training on the model and verifying the feasibility of the model. The method has the advantages that multi-modal biomechanical and kinematic data are fused, and accurate assessment of the sport injury risk and comprehensive identification of a specific risk mode are realized.

The invention relates to the technical field of robot control, in particular to a robot joint module control method and system based on a self-learning strategy, and the system comprises a parameter configuration module, a data collection module, a data processing module, a scheme screening module, a hybrid self-learning model module, a drive control module and a circulation self-adaption module. By constructing a hybrid self-learning system fusing an inverse dynamics model and a reinforcement learning model and combining a multi-dimensional data acquisition and intelligent optimization strategy, cooperative improvement of operation precision and low loss is achieved, an optimal joint angle combination and control parameters are iteratively optimized, and in scenes of fixed-height lifting and the like, the optimal joint angle combination and control parameters are optimized. According to the method, the deviation between the actual pose and the target pose can be controlled within the preset threshold value, the operation precision is guaranteed, the total loss of the joints can be remarkably reduced by balancing the stress of the joints, optimizing the motion trail and reducing inertial impact and mechanical wear, and the problem that the precision and the loss are difficult to consider in a traditional control method is solved.

The invention discloses a magnetic performance dynamic compensation method of a magnetic latching relay suitable for a wide temperature range environment, and relates to the field of magnetic performance dynamic compensation, and the method comprises the steps: obtaining the real-time temperature data and real-time load voltage data of the relay, outputting a basic driving energy parameter, and obtaining the real-time temperature data of the relay based on the real-time temperature data; constructing a three-dimensional rigidity potential energy curved surface dimension reduction solution equivalent rigidity coefficient, predicting mechanical collision kinetic energy at the moment that a movable contact is closed, generating a critical rebound energy threshold value, constructing an ideal convergence track meeting physical deceleration constraint, executing inverse dynamics solution, generating a composite step driving waveform instruction, and performing dynamic deformation on the composite step driving waveform instruction. A relay coil is driven by executing the composite step driving waveform instruction, the motion state vector of a movable iron core is calculated in real time through a flux linkage observer, mechanical rebound is recognized, active magnetic damping adjustment is triggered, the movable iron core is forcibly clamped in a magnetic maintaining balance area, a movable contact can be forcibly locked at a conduction position, and the magnetic field is controlled. And a false pull-in fault is thoroughly avoided.

The invention discloses a lower limb multi-joint torque intelligent prediction method and related equipment, and relates to the technical field of joint torque prediction.The method comprises the steps that original surface electromyography sEMG, kinematics data and ground reaction data of a subject under different lower limb movements are collected; kinematics data and ground reaction force data are used as input, and multi-degree-of-freedom angle data and moment data of three joints of the lower limb are obtained through inverse kinematics and inverse dynamics calculation; constructing a lower limb multi-joint torque prediction model through a deep learning technology, and performing model training by using the training set; and inputting the test sample set into the trained lower limb multi-joint torque prediction model to obtain a joint torque prediction result of the subject. According to the method, the problems of low joint torque prediction precision, high calculation cost and the like in the prior art are solved, the method has the advantages of being easy to implement, high in generalization, capable of processing large-scale data and the like, and prediction results are expected to be applied to three-dimensional compliance and modular control of the exoskeleton robot.

This invention belongs to the field of deep learning modeling technology, and particularly relates to a method for inverse dynamics modeling of MRE isolators based on deep fusion networks. The method includes the following steps: S1, constructing an inverse dynamics model of the MRE isolator based on a deep fusion network; S2, performing dynamic mechanical performance tests on the MRE isolator, collecting and recording the raw data of the device; and preprocessing the raw data to obtain training data; S3, using the obtained training data to train the constructed inverse dynamics model of the MRE isolator; S4, using the trained inverse dynamics model of the MRE isolator to control the MRE isolator; wherein, steps S1 and S2 can be interchanged. This method overcomes the problems of traditional parametric phenomenological models being difficult to inverse and the limited research and low accuracy of non-parametric neural network models, providing strong support for the intelligent control of MRE isolators.

The invention relates to the field of intelligent physical training and man-machine interaction, and discloses an interactive martial art immersive teaching system, which obtains macroscopic body movement and microscopic skin vibration signals of a user through a multi-mode acquisition module, and extracts real-time breathing phases in a non-contact manner by using an Euler video enhancement technology and Hilbert transform. On the basis, the system establishes a dynamic coupling mechanism of the breathing phase and the impedance characteristic of the virtual rigid body model, and the dynamic physiological stiffness is used as a physical constraint condition to execute inverse dynamic solution, so that the effective internal stiffness modulated by breathing is quantified. And finally, the immersive feedback module generates a dynamic fluid special effect and tactile feedback according to the gas-force coherence index, and adaptively controls teaching stepping. The technical pain points that in traditional martial art teaching, 'internal strength 'cannot be quantified, and breathing and actions are difficult to cooperate are effectively solved, and digital immersive guiding with shape and gas integration is achieved.

The invention relates to the technical field of semiconductor manufacturing, and discloses a wafer polishing treatment system based on pressure regulation and control, which comprises a partition pressure regulation and control module, a micro-disturbance excitation injection module, an interface rheological state analysis module and a dynamic hysteresis inversion control module. The dynamic viscoelastic modulus is calculated in real time, an inverse dynamic model is constructed according to the dynamic viscoelastic modulus to generate an advanced compensation component aiming at the viscoelastic hysteresis effect, the transient ballistic overshoot phenomenon is effectively restrained through the strain rate hardening and hysteresis characteristics of the active hedging polishing pad under high-speed pressure switching, and therefore the transient ballistic overshoot phenomenon is effectively restrained. The problems of over-polishing and defects caused by pressure response dislocation are solved, and the wafer planarization quality under the advanced manufacturing process is improved.

The invention discloses an operation control method for PCB drilling and milling equipment, and relates to the technical field of industrial automation, and the method comprises the steps: capturing the vibration spectrum characteristics of the PCB drilling and milling equipment through a gravitation lens array, carrying out the frequency domain transformation of the vibration spectrum characteristics, generating a displacement harmonic component, constructing an inverse dynamic model, inputting the displacement harmonic component, and outputting a compensation instruction; a quantum magnetic sensor is adopted to detect current magnetic field changes in the process of driving the motion mechanism, quantum state conversion is carried out on the current magnetic field changes, and a current feedback signal steady-state value is generated; and the drilling trigger signal is sent to a motion control card of the PCB drilling and milling equipment, and the motion control card starts a spindle motor of the PCB drilling and milling equipment and drives a drilling tool to execute the drilling process. The vibration spectrum characteristics are captured through the gravitation lens array, and machining errors caused by the fact that vibration is not detected are reduced.

The present application relates to the field of robot technology, especially a wheel-foot composite flat warehouse monitoring system and control method for unstructured granular medium, including adaptive four-wheel-foot composite mobile chassis, dual-mode operation module carrying flat warehouse mechanical arm and deep probe, multi-source perception hub and controller, the core lies in that through the multi-mode adaptive switching of wheel-foot composite chassis, the anti-sinking movement is realized on the loose grain surface; and the WBC whole body controller based on inverse dynamics and granular mechanics is adopted, so that the chassis can actively offset the huge reaction force generated by the flat warehouse operation of the mechanical arm, ensure the overall stability during heavy load operation, at the same time, the variable stiffness admittance control is adopted for the mechanical arm, high efficient flat warehouse and automatic obstacle crossing are realized, the present application completely solves the technical bottleneck that the existing equipment is easy to sink and operation unstable on unstructured granular medium, and realizes the full autonomous and integrated grain warehouse flattening and deep layer monitoring operation.

The application discloses a state representation method of a deformable object, comprising the following steps: S1: acquiring an RGB image of the deformable object; S2: acquiring the contour of the deformable object from the RGB image; S3: obtaining the centroid of the contour by using the acquired contour, establishing a polar coordinate system with the centroid as a center point, and dividing a plurality of sector regions; and S4: calculating the average distance of the contour points in each sector region to the centroid as a one-dimensional feature in a multi-dimensional feature of the state of the deformable object. The state representation method of the deformable object makes the inverse dynamics model pay more attention to the contour of the deformable object, so that the robot can better complete the task of operating the deformable object; and the inverse dynamics model combines the convolutional neural network and the attention mechanism in parallel, can capture local and overall features in the feature map at the same time, and thus improves the prediction performance of the inverse dynamics model.

The application discloses a kind of based on inverse dynamics unmanned vehicle trajectory tracking control method, belong to the technical field of unmanned vehicle trajectory control, it includes: obtaining the measured trajectory of aircraft in target area;The measured trajectory is gridded and smoothed;Determine the input and output of unmanned vehicle inverse dynamics based on the measured trajectory after processing;Based on the input and output of unmanned vehicle inverse dynamics, construct unmanned vehicle inverse dynamics model;Based on unmanned vehicle inverse dynamics model, calculate unmanned vehicle control variable;According to unmanned vehicle control variable, verify unmanned vehicle trajectory simulation accuracy.The application constructs the grid function of measured state variable, integrates unmanned vehicle dynamics equation, unmanned vehicle control constraint, realizes efficient target trajectory tracking control variable calculation, ensures that unmanned vehicle is controlled to known trajectory in shortest time Rate construction is completed, while reducing the amount of calculation, optimizing equipment demand.

The application provides a lower limb muscle strength prediction and balance function evaluation method based on plantar pressure and spatial pose fusion, comprising: collecting plantar pressure data, lower limb spatial pose data and human individual physiological parameters of a subject, and combining a force platform to obtain ground reaction force data; real lower limb muscle strength is calculated by using inverse dynamics and Hill muscle model, and a plantar pressure + spatial pose-lower limb muscle strength data set is constructed; the optimal sensor quantity and layout are obtained by combining a heuristic optimization algorithm and a machine learning regression model, taking prediction accuracy and information redundancy as indexes; a deep forest model is trained based on fusion features, real-time prediction of hip joint, knee joint and ankle core muscle group muscle strength is realized; further, muscle strength symmetry analysis, weak muscle group identification and hip-knee-ankle force ratio analysis are completed, and multi-dimensional quantitative evaluation of balance function is completed. The method can realize high-precision lower limb muscle strength prediction under limited sample conditions, and output multi-dimensional balance function evaluation results.

The present invention discloses a method for whole-body coordinated control of a humanoid robot based on a center of mass dynamics model, comprising: systematically modeling the humanoid robot according to the center of mass dynamics model; establishing the relationship between arm momentum and center of mass momentum based on whole-body kinematics and the principle of momentum conservation; constructing the entire nonlinear optimization control problem and solving it in real time, with the controller predicting the robot's motion trajectory for a period of time in the future; calculating the feedforward torque using inverse dynamics, and delivering the joint torque in conjunction with the joint PD controller. The present invention designs a nonlinear model predictive control problem for whole-body coordinated control of a humanoid robot, rationally plans the system state, arm end state, foot end state, and reference trajectory of the control input, and designs the equality and inequality constraints necessary for the robot's whole-body coordinated motion; adds task trajectory constraints and momentum conservation constraints to the arm end, making the arm's ability to assist in motion balance more prominent.

The present application provides an image data control method, device, electronic device and storage medium, including: acquiring motion capture data, temporarily resetting the motion capture data to zero, matching the temporarily zeroed motion capture data to the basic posture, establishing a control file and generating a temporary skeleton; creating an inverse kinematics controller on the temporary skeleton, and controlling the inverse kinematics controller through the temporary skeleton to generate inverse dynamics constraint data and forward dynamics constraint data; outputting the inverse dynamics constraint data and forward dynamics constraint data to the control file. The present application creates a temporary inverse kinematics controller system on the motion capture data by creating a temporary skeleton after acquiring the motion capture data. The inverse kinematics controller system can achieve seamless switching between inverse kinematics and forward kinematics, thereby avoiding problems such as dislocation and frame skipping during data conversion by generating forward kinematics through inverse kinematics conversion.

The invention discloses a multi-form robot motion control system based on AI perception decision. The multi-form robot motion control system comprises a perception fusion module, a multi-mode prediction module, a form management module, a motion control module and a control optimization module. The invention relates to the technical field of robot intelligent control, in particular to a polymorphic robot motion control system based on AI perception decision-making, according to the scheme, discrete morphologies are linearly combined into unified hybrid dynamics through micro-hybrid weights, and a multi-modal predicted safety violation probability and entropy regularization learning morphological strategy are combined to obtain a multi-modal robot motion control system. Smooth low-frequency switching and continuous balancing of energy consumption and risks are achieved, and coupling with model prediction control is achieved; the multi-modal collision probability is merged into model prediction control cost and constraint, hybrid dynamics is adopted for solving, high-frequency whole-body inverse dynamics is mapped into constrained joint torque, safety filtering is combined, priority avoidance and smooth executable control are guaranteed, energy consumption is reduced, and the task success rate and safety are improved.

The application provides a kind of distributed torque sensor mechanical arm calibration method, system and medium, belong to robot technical field, aim at solving the problem that systemic measurement error is caused to joint torque sensor due to assembly stress.The method comprises: installing true value sensor at the end of mechanical arm and collecting data, data includes joint state, joint measured torque and true value measured torque;Based on inverse dynamics model, and true value measured torque is input as external load, the theoretical torque of each joint is calculated;By optimization algorithm, the difference between theoretical torque and measured torque is minimized as the goal, and the calibration matrix of each joint torque sensor is solved.The application can effectively compensate assembly drift by calibrating at the whole machine level, improve the torque measurement accuracy of mechanical arm, and the method is efficient and widely applicable.

A numerical control device is provided that enables switching of a robot payload setting directly from the numerical control device. The device analyzes robot numerical control instructions in a program, generates corresponding robot instruction signals, and selects a payload setting from multiple payload information based on the analyzed instructions. The selected payload setting is transmitted to a robot control device, where it is applied to update payload data used in inverse dynamics torque calculations. By allowing payload settings to be switched and reflected in torque computation, the device improves efficiency and accuracy of robot control under varying payload conditions.

This invention discloses a method for reconstructing a three-dimensional motion and joint force dynamic model of the jaw system, relating to the field of medical technology. The method includes: S1, acquiring a multimodal dataset synchronously collected through a multimodal sensing system and unified to a skeletal coordinate system based on the mandible and temporomandibular joint; S2, constructing an individualized mandibular musculoskeletal model based on the patient's jaw CT image data; S3, using an optimization algorithm, taking the multimodal dataset as input, and under the constraints of the mandibular musculoskeletal model, performing inverse kinematics solution to deduce the six-degree-of-freedom motion trajectory of the mandible in three-dimensional space; S4, based on the six-degree-of-freedom motion trajectory, applying the optimization algorithm and the mandibular musculoskeletal model, and calculating the dynamic contact reaction force distribution of the temporomandibular joint through inverse dynamics analysis; S5, using the dynamic contact reaction force distribution as input, feeding it back to the mandibular musculoskeletal model to calculate the intensity distribution of key muscles involved in mandibular movement.

The application discloses a kind of optimal envelope trajectory tracking control methods of space non-cooperative target under multi-target constraint, mainly include the following steps: design the full-drive multi-finger space capture configuration with adaptive configuration adjustment and anti-escape capability, generate effective multi-finger envelope configuration using bidirectional Hausdorff distance based on finger feature and target edge point feature, and construct multi-finger dynamic capture domain according to target motion characteristics and joint feature length;Desired trajectory parameterization is carried out using continuous high-order Bezier curve, and smooth envelope trajectory is planned under minimum base disturbance based on multi-target constraint and multi-modal particle collaborative learning mechanism;Through the nonlinear coupling characteristics of inverse dynamics compensation system of multi-finger, design multi-finger collaborative hierarchical tubular model predictive control strategy based on disturbance observer to realize envelope trajectory tracking control.The method can effectively overcome the interference caused by target shape complexity, motion uncertainty, nonlinear dynamic coupling, realize the accurate tracking control of space non-cooperative target trajectory.

[PROBLEM] Provided is a joint load estimation program or the like for estimating a load of force applied to a joint of a subject on the basis of an image in which the exercise state of the subject is captured. [SOLUTION] The present invention is a joint load estimation program, which causes a computer to execute: a base exercise state acquisition step for acquiring an exercise state of a subject on the basis of an image in which the exercise state of the subject is captured; a rigid link model acquisition step for acquiring a three-dimensional rigid link model on the basis of a predetermined feature amount related to the human body of the subject; a rigid link system exercise state acquisition step for estimating the exercise state of the three-dimensional rigid link model in accordance with the exercise state of a subject 2 acquired by the base exercise state acquisition step, and acquiring exercise information about the three-dimensional rigid link model; and a joint load calculation step for calculating the joint reaction force applied to the joint tissue by inverse dynamics calculation based on the exercise information about the three-dimensional rigid link model.

The invention discloses a general six-axis robot position inverse dynamics controllable method in the technical field of communication, which requires that the position of a six-axis robot and the deviation between the position and a target position can be calculated for a group of six parameters in a given range, the deviation is a non-negative value and represents the deviation, and if the deviation is zero, the deviation is not negative. Representing that the six-axis machine has reached the target position; compared with a steepest descent-based method, the method provided by the invention is suitable for any form of forward dynamics, and does not need to analyze or numerical Jacobian matrixes, so that the method is insensitive to parameter derivative singularity caused by parameter structure reasons; the universal lock is not sensitive to rotation parameters; compared with a cyclic coordinate updating method, the method is based on a random number algorithm, so that the possibility of instability or parameter resonance caused by accidental circulation is effectively avoided.