247 results about "Robot kinematics" patented technology

The invention relates to the technical field of path planning, and discloses a robot path planning method, which comprises the following steps that a hybrid cost map is constructed, and the hybrid cost map comprises a static obstacle field, a dynamic obstacle probability prediction field and a terrain energy consumption field; constructing an anisotropic heuristic function, and searching on the mixed cost map to obtain an initial path; parameterizing the initial path into a group of piecewise polynomial curves, constructing a joint optimization target which takes the total curvature, the piecewise polynomial curves and the space-time overlapping integral of a dynamic obstacle probability prediction field, and performing trajectory optimization under the condition of meeting the kinematics constraint of the robot to obtain a smooth space-time trajectory; when the task is executed, the information divergence is continuously calculated, and when the information divergence exceeds a preset threshold value, the current state of the robot serves as a new starting point, and the complete path planning process is executed again. According to the method, the future collision risk is prospectively avoided, the energy consumption of the robot is considered, and the comprehensive quality of the path is improved from the source.

The invention discloses a covariance self-adaptive control method for a quadruped robot for spine joint adjustment. The method comprises the following steps that 1, a spine quadruped robot kinematics model with spine joints, a robot trunk dynamics model with the spine joints and a robot leg multi-rigid-body dynamics model are established; 2, based on the energy stability margin theorem and the spine joint coupling effect, a posture control method of the spine joint quadruped robot is provided by combining a covariance self-adaptive evolutionary strategy, and the angle, rigidity and damping coefficient of spine joints are estimated and adjusted according to the terrain gradient; 3, extracting the spatial relationship between the trunk mass center and the foot ends of the robot as an expected reference, updating the expected mass center position in real time, and constructing a model prediction control method to control the posture of the spinal joint quadruped robot; 4, the position of the foot falling point of the swing leg is designed in advance in combination with the additional freedom degree provided by the spine joint and MPC prediction state information. The motion stability of the robot in the complex terrain is improved.

The invention belongs to the technical field of image processing and robots, and particularly relates to an image processing method and device and a robot, and the method comprises the steps: obtaining a structured light coding image, a polarization imaging image and a binocular depth image; constructing a multi-dimensional feature vector by fusing decoded contour phase, polarization analysis material characteristics and depth image data; dynamically calibrating the multi-dimensional feature vector according to the ambient light and the pose of the robot, correcting color deviation through an adaptive white balance algorithm, compensating a camera installation error, and obtaining a calibrated descriptor; based on the calibrated descriptor, utilizing an improved Hough transformation and template matching algorithm to identify the object category attitude, and calculating six-degree-of-freedom pose parameters through a triangulation method; and according to the six-degree-of-freedom pose parameters, a collision-free grabbing path is generated in combination with a robot kinematics model and obstacle information. Therefore, the problems of insufficient image analysis capability, single compensation mechanism, lack of path optimization capability and the like in the prior art are solved.

The invention relates to the technical field of robot control, and particularly discloses a robot indoor moving path planning method and system. An environment sensing module collects environment data through a multi-mode sensor array and constructs a three-dimensional semantic map; the dynamic decision-making module generates a candidate path set based on the three-dimensional semantic map data output by the environment perception module, and transmits a decision-making result to the path optimization module through a priority arbitration mechanism; the path optimization module performs multi-objective optimization according to candidate paths of the robot kinematics constraint and dynamic decision module; the execution control module is used for converting an optimized path into a bottom layer driving instruction through kinematics calculation and feeding back an execution state to the dynamic decision-making module in real time to form closed-loop control. A dynamic environment modeling mechanism is adopted, through variable resolution perception and semantic map construction, a layered decision-making framework and multi-target collaborative optimization are adopted, and the dynamic environment modeling mechanism is optimized; and the adaptive control system is selected to significantly improve the environmental adaptability and reliability of the system.

The invention relates to the field of intelligent industry, and discloses a visual robot hand-eye calibration method based on a circular hole calibration plate, which takes the circle center of a calibration plate circular hole as a fixed point as a constraint, establishes a hand-eye calibration model combined with robot kinematics parameters, and performs a contrast experiment with a traditional standard ball method under the same experiment platform. After a calibration result is obtained, a certain high-precision plane is scanned with reference to a flatness error evaluation method, a point cloud of the plane is obtained, the distance root-mean-square error between the point cloud and the least square fitting plane is calculated, and the distance root-mean-square error serves as an evaluation parameter of the hand-eye relation calibration precision. According to the method, the position coordinates of the fixed points in the space are collected more accurately under the on-line laser coordinate system, and kinematics parameters of the robot are incorporated into the hand-eye calibration model, so that the root-mean-square error of plane measurement can be reduced, and the superiority of the method is verified.

The invention discloses a shaping robot, a shaping system and a grain processing method thereof. An integrated moving and transferring device is used as a chassis for bearing all modules; the shaping industrial robot is used for driving a cutter on the mechanical arm to conduct shaping machining. The medicine scrap sucking and cleaning device is used for cleaning scraps generated by processing; the tool changing magazine is used for placing and managing tools and comprises a quick-changing tool rest, and a detection sensor used for detecting whether the tools exist or not is arranged on the quick-changing tool rest; the control system comprises a coordinate system management module, a tool path planning module, a robot kinematics module, a collision detection module, a motion simulation module and a robot program generation module. Automatic machining positioning, automatic machining path planning and automatic cutting tool switching and management can be achieved; the automation degree of grain cutting machining is effectively improved; in the shaping process, temperature and contact force detection sensors are used for detection, and early warning protection in the machining process is achieved.

The invention provides a robot positioning prediction method and device, a medium and equipment, and relates to the field of industrial robot motion control. According to the method, on one hand, a physical-data double-branch collaborative network is designed, meanwhile, a DH parameter method is used for predicting and obtaining a theoretical position coordinate of a target robot, an improved Transform network is used for predicting and obtaining a coordinate compensation value of the target robot, so that a theoretical value and the compensation value are decoupled, and the limitation of single model establishment is avoided; and then, the two are fused, and high-precision end point location prediction for the target robot is realized by combining priori knowledge of robot kinematics and nonlinear expression ability of deep learning. And on the other hand, a spatial physical information mixed loss function is provided, and a spatial topological structure output by the DH model is used to guide the distribution of predicted values of the whole position prediction model, so that the geometric rationality of target robot end point location prediction is improved.

The invention relates to the technical field of robot navigation, and discloses an automatic path-finding intelligent robot and a path-finding method thereof. According to the method, multi-dimensional spatial data including obstacle distribution, ground features and dynamic target motion information is acquired through an environment sensing device, spatial discretization processing is performed on the data, and a topological map which has a hierarchical structure and includes a node connection relation and a region passing weight is generated. Then, according to the node connection relation of the topological map, a path planning model based on manifold learning is established, and an optimal path candidate set is determined by calculating the manifold distance between adjacent nodes; then extracting a key node sequence in the candidate set, dynamically optimizing a path in combination with a region passing weight, and generating a preliminary navigation track; and finally, inputting the initial track into a motion control model, carrying out smooth processing according to kinematics constraint of the robot, and outputting a final execution path. According to the invention, the robot can efficiently find the way in a complex environment.

The invention relates to the technical field of industrial robots, in particular to a five-degree-of-freedom industrial robot path planning method and system based on D-H parameters and dynamics modeling, and realizes high-precision path planning and dynamic environment adaptation by combining kinematics modeling, dynamics analysis and MATLAB simulation technologies. The method comprises the steps of building a robot kinematics model, building a kinetic equation, mixing a path planning strategy, solving inverse kinematics and performing multi-sensor fusion control. The system covers modeling, calculation, planning, simulation and feedback modules, and the operation efficiency and safety of the industrial robot in a complex scene are effectively improved.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The present invention provides a parameter deviation determination method, apparatus and tool, a storage medium, and a computer device. The method comprises: determining that an adapter board is fixed to the tail end of a robot, wherein a calibration probe and a camera are fixed on the adapter board, and the robot is fixed on a base; controlling the robot to move, such that the calibration probe comes into contact with a calibration board, and obtaining a first relative position relationship between the calibration board and the base; controlling the tail end to move toward a calibration point, and when the tail end moves to the calibration point, controlling the camera to capture an image, wherein the image comprises the calibration board; on the basis of the image and the first relative position relationship, determining a second relative position relationship between the camera and the base during image capturing; and on the basis of the second relative position relationship and an ideal kinematics parameter of the robot, determining a kinematics parameter deviation of the robot. The present invention solves the technical problems that the robot kinematics parameter calibration process has high costs and low efficiency, and a calibration device is not portable.

The invention provides a configuration optimization method before capturing of a space double-arm robot, and the method comprises the steps: constructing a kinematics and dynamics model of the space double-arm robot according to a three-dimensional model of the space double-arm robot; constructing a kinetic model of the to-be-captured target by using the motion parameters of the to-be-captured target in the space; according to the dynamic model of the space double-arm robot, the relative mass of the space double-arm robot under non-centering collision is obtained, and a maximum pre-contact collision force performance evaluation index is constructed; further constructing a double-arm closed chain inertia matching index performance evaluation index; constructing a configuration optimization model before the space double-arm robot captures the target; and through a multi-target optimization algorithm, the optimal configuration of the space double-arm robot before capturing the target is solved. The pre-contact collision force peak value of the double-arm robot during target capturing can be effectively reduced, and meanwhile the operability of a closed chain system formed after capturing is guaranteed.

The invention discloses a physical man-machine interaction contact safety protection method and system based on a Maxwell model, and the method comprises the steps: building a human body physical model, and expressing the effective mass, elastic coefficient and maximum allowable impact force physical attributes of each part of a human body; kinematics, statics and dynamics models of the robot are established, and the relation between the joint space displacement, speed, torque and mass of the robot and the tail end speed, tail end rigidity and effective mass is expressed; a physical man-machine interaction model is established, and the relation between the joint displacement, speed and mass of the robot and the physical man-machine interaction impact force is expressed; physical man-machine interaction contact characteristics are calculated, and impact duration, the maximum impact force, a contact damping coefficient and a recovery coefficient are calculated; and 5, robot safety protection measures are formulated, and the safety of physical man-machine interaction is guaranteed. According to the method, the solution of a Hertz contact stiffness parameter is avoided, the safety of physical man-machine interaction can be evaluated through the impact duration and the maximum impact force, and a theoretical basis is provided for the design of a robot man-machine interaction system.

The invention discloses a trajectory reduction and map reconstruction method capable of solving a time drift error and application, and relates to the technical field of data processing. The method comprises the following steps: constructing a robot kinematics model constraint, fusing real-time synchronous data streams of multiple sensors, and combining a dynamic sensor weight distribution function and Kalman filtering to generate an initial motion track; adjusting the trajectory based on dead reckoning and a pipeline kinematics model in a non-feature region, matching pipeline feature points through an ICP algorithm to calculate a pose deviation, and correcting a trajectory error by using Kalman filtering; iMU, vision and pipeline geometric constraints are optimized and fused through a sliding window graph, and an optimal track is obtained; generating a three-dimensional digital twinborn model with defect mapping in combination with laser point cloud and Poisson reconstruction; according to the method, the problems of large sensor accumulative error, feature deficiency positioning failure and low modeling precision are solved, high-precision positioning in a complex pipeline environment is realized, the detection efficiency is remarkably improved, and the manual dependence is reduced.

The invention discloses a multi-constraint path planning method and device based on intelligent heuristic search, and is suitable for a mobile robot in a complex and narrow environment. According to the method, a multi-source sensor is used for environment perception and fusion mapping, and a grid map is generated; a neural heuristic search algorithm based on a Mama structure and a selective state space model (Selective SSM) is adopted on the cost map, and a guide graph is generated; a continuous and collision-free initial path is generated through self-adaptive step size control in combination with robot kinematics constraints and channel characteristics; further establishing a multi-target constraint model based on a robot center Euclidean symbol distance field (RC-ESDF), and executing path smoothing and safety optimization by using a priori-guided particle swarm optimization algorithm (Prior-guided PSO) to obtain an optimal path meeting dynamic consistency; the system corrects the path in real time through a closed-loop feedback mechanism and triggers dynamic re-planning, so that the path feasibility, safety and real-time adaptive capacity of the robot in a limited environment are remarkably improved.

The invention relates to the technical field of spraying control, and discloses a wind power tower wall surface spraying method and system, and the method comprises the steps: scanning a wind power tower wall surface through a sensor, obtaining the surface contour data of the wind power tower wall surface, and forming reference geometric information; curvature distribution characteristics of the reference geometric information are extracted, key path points are extracted according to the curvature distribution characteristics, robot kinematics constraints are introduced to carry out smoothing and coverage completeness integrated processing on the key path points, and therefore a spraying path is generated; establishing a mapping relation between the curvature of the spraying path and nozzle parameters, and setting initial values of atomization pressure and coating flow based on the mapping relation; in the spraying execution process, the real-time thickness monitoring data and the preset thickness are fused to generate a parameter adjusting instruction, and an actuator is driven to conduct online correction on spraying parameters; according to the method, accurate adaptation of the spraying path and the outline of the tower drum and dynamic matching of spraying parameters are achieved, it is ensured that the thickness of the coating always fits the preset standard, and the stability and controllability of the spraying process are improved.

The invention provides a ship active stabilization control method and system, and relates to the field of ships. The ship active stabilization control system comprises a stabilization execution mechanism which comprises an upper platform constructed according to a ship body of a ship and a lower platform constructed according to multiple sets of water pressing plate execution mechanisms, and the multiple sets of water pressing plate execution mechanisms are arranged at any positions below waterlines of the two sides of the ship correspondingly; each water pressing plate executing mechanism comprises a water pressing plate assembly and a driving cylinder connected with the water pressing plate assembly; the data acquisition unit is configured to acquire ship body attitude and motion state information and environment data of the ship; the stabilization controller is configured to calculate and generate correction torque according to the parallel robot kinematic mechanics mechanism model, the ship posture and motion state information and the environment data, and distribute the correction torque to an execution instruction corresponding to each driving cylinder; and the driving cylinders drive the corresponding water pressing plate assemblies to execute water pressing movement or return movement according to the execution instructions.

The invention discloses a tractor trailer robot track generation method based on a polynomial and a symbolic distance field, and the method comprises the steps: dividing grids through a point cloud map, estimating the curvature and normal direction of each grid, extracting obstacle information, and calculating a symbolic distance value corresponding to each grid; taking the initial pose of the tractor trailer robot as a root node, and based on the obstacle information, utilizing a multi-end-point heuristic graph search algorithm for spatial search in SE (2) to generate an initial path for arriving at the target area; and in combination with the symbol distance value corresponding to each grid, modeling the trajectory generation of the tractor trailer robot as a constraint optimization problem, and solving the constraint optimization problem by taking the initial path as an initial value to obtain a final trajectory. The problem that track generation is difficult due to complex kinematics, high state space dimension and deformable structure of the traction trailer robot is solved, the problems that an existing algorithm is low in efficiency and loses solution space are solved, and efficient track generation of the traction trailer robot is achieved.

The invention discloses an industrial robot grinding and polishing compliance control method for reinforcement learning optimization. The method comprises the steps that S1, a five-degree-of-freedom industrial robot kinematics model is established; s2, an environment is created, an impedance control module is built, and expected polishing force and an expected position are set; s3, creating an intelligent agent, and setting a DQN hyper-parameter by adopting a DQN algorithm; s4, the action of the intelligent agent is output to a damping parameter Bd port of the impedance controller, and the damping parameter of the impedance controller is optimized; s5, training options, the maximum training turn number and the like are set, training is started, and corresponding agents are stored; and S6, the trained intelligent agent is used, and constant-force grinding of the robot is completed. According to the method, the optimal impedance parameter can be determined according to the environment, the relation between the overshoot and the adjustment time is balanced, it is guaranteed that the adjustment time is short while the overshoot is restrained, and grinding and polishing compliance control over the industrial robot is completed.

The invention discloses an industrial robot tail end vibration response calculation method and system, solves the problem of how to improve industrial robot tail end dynamic response prediction accuracy, and belongs to the field of industrial robot dynamics analysis. According to the method, elements of the industrial robot are regarded as rigid bodies or flexible bodies, all assemblies are integrated into a rigid-flexible coupling dynamic model in combination with a robot kinematic model, step-by-step parameter identification is conducted on the decoupled rigid body dynamic model and structural dynamic model, inertial parameters are identified by considering the influence of nonlinear joint friction, and the method is suitable for industrial robot control. Further identifying a rotational inertia matrix in the robot milling kinetic model; joint elastic parameters are identified by considering the influence of non-linear joint rigidity, and a damping matrix in a robot milling kinetic model is further identified. And the torque fluctuation of each joint is predicted, the vibration response of each joint is calculated in combination with a robot milling kinetic model, and finally the robot tail end vibration response is calculated in combination with robot kinematics.

The invention discloses a robot tracking control method, system and device adapted to an irregular discrete trajectory, and a storage medium, and belongs to the technical field of robots, the method comprises the following steps: dynamically selecting discrete points according to the change of a reference trajectory to quantify the curvature change of the reference trajectory; according to the curvature change of the reference trajectory, dynamically selecting an appropriate prediction step length; discretizing the robot kinematic model to obtain a robot discrete kinematic model; defining a trajectory tracking error function, and constructing a trajectory tracking target function in combination with the robot discrete kinematics model; and solving the target function, obtaining a robot trajectory tracking controller, and enabling the robot to move according to a reference trajectory, thereby completing a trajectory tracking target. The robot trajectory tracking control method with the dynamic adaptive capacity is provided for discrete reference trajectories which change irregularly, the robot trajectory tracking precision and the driving efficiency can be improved, and the robot trajectory tracking control method has important practical value.

The invention relates to an autonomous obstacle avoidance control method, device and equipment for an underground cable intelligent laying robot and a medium. The method comprises the following steps: acquiring underground environment data through a multi-source sensor array, performing multi-modal fusion on the position, shape and material information of an obstacle in the underground environment data, and generating an environment sensing model; classifying and identifying the obstacles based on the environment perception model, and outputting a classification result containing threat levels; wherein categories in classification identification comprise rigid obstacles, flexible obstacles and dynamic obstacles; according to a classification result and a robot kinematics constraint condition, generating an obstacle avoidance feasibility boundary; and generating a self-adaptive obstacle avoidance strategy based on the obstacle avoidance feasibility boundary, wherein the self-adaptive obstacle avoidance strategy is used for indicating the laying robot to output a control instruction. By adopting the method, the intelligent laying robot can make high-precision obstacle avoidance decisions on complex and diversified obstacles in an unstructured underground space.

The invention relates to the technical field of robot navigation, and discloses a map-free mobile robot autonomous navigation method and system and a storage medium, and the method comprises the steps: building a robot kinematic model according to robot incomplete constraints, robot physical parameter constraints and robot state parameters; generating a candidate set of tracks on which the robot can travel based on the kinematic model and the motion constraint conditions; constructing a single-frame grid map, and recording grids occupied by tracks and grids occupied by obstacles; according to the size of the robot and the safety margin, the occupied range of the grids occupied by the obstacles is enlarged; and according to the angle difference, scoring and preferential selection are conducted on the tracks meeting the safety requirement, and the optimal track is selected to drive the robot to move. According to the method, rapid decision making and control can be realized in a scene with a relatively high real-time requirement, the response speed is high, and the safety, the adaptability and the navigation efficiency are relatively high.

The invention relates to the technical field of three-dimensional point cloud processing, in particular to a self-adaptive point cloud registration method based on a GM robust kernel function. The method comprises the steps that a welding seam point cloud collecting system is built, a robot kinematics model and a hand-eye relation matrix of the welding seam point cloud collecting system are obtained, and the welding seam point cloud collecting system is calibrated according to the determined hand-eye relation matrix and the determined robot kinematics model; acquiring three-dimensional point cloud data of a welding seam through a welding seam point cloud acquisition system, and preprocessing the three-dimensional point cloud data; coordinate system conversion and overlapped part point cloud extraction are carried out on the preprocessed three-dimensional point cloud data; and in cooperation with a median absolute deviation adaptive adjustment mechanism, carrying out adaptive point cloud registration based on a GM robust kernel function on the extracted overlapped point clouds, and carrying out complete splicing on the registered point clouds. According to the method provided by the invention, the interference of abnormal values on registration errors in an existing registration algorithm is effectively suppressed, and the precision and stability of the algorithm are remarkably improved.

The invention discloses a robot rigidity modeling method and system based on joint module nonlinear rigidity parameter fitting, and the method comprises the steps: modeling the torsional rigidity of a joint module, and expressing the relation between the torsional angle of the joint module and the joint torque through a cubic polynomial function; modeling a robot rigidity model, and expressing a relation between a joint space rigidity matrix and a robot tail end rigidity matrix through robot kinematics; identifying a torsion angle and a torque of a joint module, and acquiring the torque of the joint module through a torsion sensor or based on joint current identification; a static flexibility experiment is conducted, and the corresponding relation between the tail end force vector and the tail end elastic deformation vector of the robot is obtained; non-linear stiffness parameter fitting identification is carried out, and non-linear stiffness parameter identification is realized through a least square problem model; according to the method, non-linear rigidity parameter identification of the joint module is achieved, whole machine rigidity modeling is completed on the basis, and a basis is provided for rigidity compensation of a robot and improvement of positioning precision.

The invention provides a universal industrial robot control system which comprises a communication module and a universal controller unit module, and a software function module comprises a motion planning module, a teaching module, a communication data conversion module and a motion instruction programming module. The motion planning module is used for generating a path conforming to robot kinematics constraints based on a unified URDF (Uniform Resource Description Framework) model; the teaching module expands the teaching function of the control system and supports path point storage and workpiece coordinate system definition; the communication data exchange module realizes real-time two-way communication, and transmits instruction data and feedback data between the control system and the robot controller; the motion instruction programming module provides standardized instruction interfaces for point-to-point motion, straight line motion, arc motion and the like, and supports parameter self-definition, definition and execution of basic motion mode combination tasks so as to adapt to different task requirements.

The invention relates to the technical field of robots, and discloses a robot kinematics calibration method based on conditional number indexes. A robot kinematics calibration method based on a conditional number index comprises the following steps: establishing a kinematics and error model, calculating an observation matrix and a conditional number thereof, optimizing a measurement attitude by using a particle swarm algorithm, generating an alternative attitude group and performing fitness value calculation, and finally collecting data and evaluating calibration precision through a laser tracker. The robot kinematics calibration system based on the condition number index comprises a robot body, a laser tracking device, a control module, a calculation module and a storage module. According to the robot kinematics calibration method based on the condition number index, the particle swarm optimization is improved to optimize the measurement attitude combination, the laser tracker is combined to achieve high-precision data collection, the calibration precision is comprehensively evaluated, an efficient and cooperative calibration system is constructed, and the calibration precision and the industrial application efficiency are improved.

Systems and methods for determining movement of a robot about an environment are provided. A computing system of the robot (i) receives information including a navigation target for the robot and a kinematic state of the robot; (ii) determines, based on the information and a trajectory target for the robot, a retargeted trajectory for the robot; (iii) determines, based on the retargeted trajectory, a centroidal trajectory for the robot and a kinematic trajectory for the robot consistent with the centroidal trajectory; and (iv) determines, based on the centroidal trajectory and the kinematic trajectory, a set of vectors having a vector for each of one or more joints of the robot.

The invention discloses an agricultural robot trajectory planning method and system with optimal energy consumption under collision constraint. The method comprises the following steps: acquiring historical state sequence data and environmental map information of a target machine and peripheral machines in real time by a self-machine; identifying the operation intention of the target machine, and outputting a multi-modal prediction trajectory; the method comprises the following steps of: generating a global coarse reference track covering an operation area by taking completion of an own operation task as a main target; generating a space-time safety corridor by adopting a space-time inheritance strategy, and reserving a safety space for the dynamic obstacle; constructing an optimization problem cost function, and comprehensively considering reference trajectory cost, collision risk cost and energy consumption cost; and under the boundary constraint of the safety corridor and the kinematics constraint of the robot, solving the optimization problem by using a numerical optimization solver to obtain an optimized local trajectory. According to the method, safe, efficient and energy-saving collaborative operation can be realized by fusing multi-modal trajectory prediction of the dynamic obstacle, refining energy consumption modeling and risk perception planning.

The invention discloses a motion state determination method, system and device based on human upper limb inverse kinematics and a medium, and relates to the field of robot kinematics control, and the method comprises the steps: obtaining an upper limb working space of a human upper limb; according to the upper limb working space, discrete sampling is carried out based on a Monte Carlo sampling method, and an initial discrete point set is determined; performing sampling density adjustment on the sensitive area, the boundary area and the singular area in the initial discrete point set to obtain a final discrete point set; determining a joint angle set corresponding to each discrete point, and constructing a joint angle database; retrieving a joint angle database based on a kd-tree method, and determining an initial joint angle; local optimization is carried out based on a gradient descent method, and a joint angle corresponding to each tail end position is determined; determining the motion state of the upper limbs of the human body; the joint angle corresponding to each tail end pose of the human upper limb can be quickly and accurately determined, and then the current motion state of the human upper limb is determined.