214 results about "Forward kinematics" patented technology

The invention discloses a robot anthropomorphic motion control method and system based on skeleton mapping. The method and system are used for enhancing anthropomorphic motion performance and environment adaptability of a humanoid robot. The method comprises the following steps: calculating a scale proportion and a posture transformation matrix between a human body and the humanoid robot based on initial posture data; according to the motion data, recursively calculating original pose information of each human body joint in a world coordinate system through a forward kinematics algorithm; mapping the original pose information into expected pose information of the humanoid robot on the key coordinate system by applying a scale proportion and a pose transformation matrix; a target joint angle of the humanoid robot is solved according to the expected pose information, and contact state information of legs of the humanoid robot and the ground is calculated according to the target joint angle; and the target joint angle and the contact state information serve as input, a motion control strategy of the humanoid robot is trained through a deep reinforcement learning algorithm, and humanoid motion control over the humanoid robot is achieved.

The invention discloses a kinematics parameter identification method and system for a six-degree-of-freedom heavy-load industrial mechanical arm, and the method comprises the steps: adding rotation parameters to parallel mechanical arm joints of a six-axis robot based on a Modified D-H method, constructing a mechanical arm forward kinematics model, and obtaining a matrix conversion relational expression of the mechanical arm; a position error model of the tail end of the mechanical arm is constructed, and error parameters to be identified are obtained; on the basis of the position error model of the tail end of the mechanical arm, global sensitivity analysis is conducted on the to-be-identified error parameters, and global sensitivity values of the to-be-identified parameters are obtained; and performing two-stage identification processing on the error parameter to be identified to obtain a mechanical arm kinematics parameter identification result. According to the method, key parameters can be accurately screened out, waste of computing resources on non-key parameters is avoided, and the parameter identification efficiency and precision are improved. The kinematic parameter identification method and system for the six-degree-of-freedom heavy-load industrial mechanical arm can be widely applied to the technical field of robot parameter identification.

The invention relates to the technical field of wearable exoskeleton data acquisition and processing, and discloses a wearable exoskeleton data acquisition and processing method and system based on digital twinning, and the method comprises the steps: building a simulation mapping model of exoskeleton joints through employing the digital twinning technology according to upper limb motion data; according to the upper limb motion data, executing a kinematics modeling strategy, calculating an actual pose of the end effector by using a forward kinematics algorithm, and substituting a target pose into inverse kinematics for solving, specifically, obtaining a joint angle, executing a Jacobian matrix modeling strategy, and establishing a model between the actual pose and the joint angle; the influence of the variable quantity of the joint angle on the actual pose of the end effector is obtained; if the pose error is smaller than or equal to an error threshold value, acquiring a joint angle at the moment, and recording the joint angle as a target joint angle; if the pose error is greater than an error threshold value, executing a joint angle tuning strategy, adjusting the joint angle, and repeating the steps S2-S5; the simulation precision is improved, and the man-machine coordination is improved.

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 robot perception and control method and system based on a dynamic nerve symbol distance field. The method comprises the following steps: acquiring each original grid file of a robot; constructing an overall robot according to the grid file, performing random sampling, calculating an SDF true value from a sampling point to each robot joint, and performing fitting to obtain a static nerve symbol distance field; the joint position of the robot is sampled, a forward kinematics result is obtained and serves as input, the static nerve symbol distance field is distilled, and a dynamic nerve symbol distance field is obtained; the robot is trained by utilizing a reinforcement learning technology, the dynamic nerve symbol distance field is used as external perception input, the joint angle is used as output of the robot, and a teacher strategy is obtained through training; and distilling the teacher strategy to obtain a student strategy for motion control of the robot. Compared with the prior art, the method has the advantages that the collision condition is pre-judged in advance, the control is accurate, the processing efficiency is high and the like.

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 spine tactile sensing system and method based on a multi-modal sensor, and the method comprises the steps: obtaining an original tactile signal, carrying out the preprocessing of the original tactile signal, carrying out the feature extraction based on the preprocessed original tactile signal, and obtaining a spatial distribution feature and a time dynamic feature; on the basis of tail end coordinates collected by forward kinematics of the tail end of the mechanical arm, space mapping of the tactile signals and the spine anatomical structure is constructed, and anatomical position features are obtained on the basis of the preprocessed original tactile signals and the space mapping; constructing task-oriented feature vectors based on the spatial distribution features, the anatomical position features and the time dynamic features, wherein the task-oriented feature vectors comprise a sliding state recognition feature vector and a spine ordinal number positioning feature vector; and constructing a multi-stage adaptive evolution hidden Markov model, and realizing spine ordinal number positioning in combination with the task-oriented feature vector. According to the method, through multi-dimensional feature fusion and dynamic model adjustment, anatomical prior knowledge and real-time tactile signals are combined, and spine segments are accurately recognized.

The invention provides a method and a device for predicting an operation track of an action-free labeling robot based on body flow representation, and the method comprises the steps: taking an initial point position in the body flow of the robot, a text feature in a language task instruction and a visual feature in an initial image as multi-modal condition features; the multi-modal condition features are input into the robot trajectory prediction model to obtain body flow information, and the robot trajectory prediction model can comprehensively consider motion of the robot, object related information contained in a language instruction and visual features in an initial image, so that more accurate body flow information is generated; the robot can accurately interact with a specific object; according to the method, the motion constraint information of each joint of the robot is obtained, the body flow information is decomposed into the motion component of each joint through forward kinematics mapping based on the motion constraint information of each joint, and kinematics constraints of different joints of the robot are fully considered, so that the accurate control of the robot motion is realized.

The invention belongs to the technical field of robots, in particular to the technical field of teleoperation robots, and particularly relates to a teleoperation master manipulator based on a power-off brake, which comprises a pose acquisition part and a tail end operation part, in an actual use scene, an operator holds a handle at the tail end and pulls a trigger, a power-off brake is powered on for contact braking, and the operator pulls a main hand to move; in the movement process, the absolute encoders of all the shafts collect the current angle and directly transmit the current angle to the isomorphic mechanical arm, or the tail end pose of the main hand is settled through forward kinematics and then mapped to the heterogeneous mechanical arm through inverse kinematics; and the trigger is released, the power-off brake is immediately powered off, all joints of the master manipulator are locked, then the posture is locked, and the master manipulator enters a posture keeping mode. A joint motor in the main hand is replaced by a brake, so that a motor servo control system is also removed, and the cost and the out-of-control risk are reduced.

The invention discloses a security constraint type inverse kinematics solving method and device and a storage medium. The method comprises the following steps: firstly, forming a joint angle-tail end pose pairing data set by students through forward movement of a robot; training an improved RBF (Radial Basis Function) network by using the data set to establish initial mapping from a tail end pose to a joint angle to obtain an initial approximation model; taking the prediction output of the model as an initial population of a PSO algorithm, and updating the speed and position of particles in the initial population through iteration; designing a comprehensive cost function, introducing a joint angle constraint and an obstacle collision constraint, and evaluating the advantages and disadvantages of each particle; and when the PSO algorithm reaches the maximum number of iterations or the comprehensive cost function value is lower than a set threshold value, outputting a global optimal particle position as a final inverse kinematics solution. On the premise of ensuring the solving precision, the joint limit constraint and the obstacle avoidance constraint are explicitly processed, so that an accurate, safe and feasible inverse solution is output, and the reliability and the safety of robot operation are remarkably improved.

The invention provides a digital twinning five-axis machine tool contour error prediction method and system and electronic equipment, and belongs to the technical field of numerical control machine tool precision control and digital twinning. According to the method, servo axis data are collected in real time at a numerical control system end, electromechanical parameters are identified on line, comprehensive disturbance is estimated, and an autoregressive exogenous (ARX) model is updated accordingly; an interpolation instruction is generated in a digital twin body end mirror image numerical control system process, an updated model is utilized to predict a shaft position with deviation, a tool path is obtained through kinematics positive solution, and finally contour errors are calculated through geometric alignment. According to the digital twinning five-axis machine tool contour error prediction method and system and the electronic equipment, dynamic and high-precision prediction of the five-axis machine tool machining track contour error is achieved through the online identification and digital twinning technology, the limitation of a traditional static compensation method is overcome, and the precision of the digital twinning five-axis machine tool contour error prediction is improved. And a key basis is provided for prospective process optimization and precision control, and the machining precision and efficiency are effectively improved.

The invention provides a flexible glove based on sparse tensile sensing and a three-dimensional hand fine-grained reconstruction method, and relates to the technical field of man-machine interaction. The method comprises the following steps: constructing a biological constraint sensing sensor array layout; according to the sensor array layout, a wearable glove integrated with six-channel sparse stretching sensing is designed; constructing a three-dimensional multi-modal dynamic hand data set according to the wearable glove; designing a data flow processing pipeline; inputting the data set into a data stream processing pipeline to obtain a preprocessed data set; constructing a multi-stage graph attention network; inputting the preprocessed data set into a multi-stage graph attention network to obtain space mapping data from a 6-channel signal to a 15-joint signal; the mapping data are input into a multi-scale Transform model, and motion feature data of the 15 joints on the time sequence are obtained; according to the motion feature data, a forward kinematics model is adopted for decoding, and the rotation quaternion of the 20 skeletons is output. According to the invention, the stability of the wearable interactive glove can be improved.

The invention provides a gesture recognition method and system for screen flying interaction, and relates to the technical field of gesture recognition, and the method comprises the steps: processing video data through employing a background subtraction algorithm, and obtaining a foreground region in the video data; extracting a foreground region in the video data by adopting human body posture estimation to obtain a hand key point set of each human body; constructing a hand skeleton model by adopting forward kinematics according to the hand key point set of each human body; utilizing the hand skeleton model to simulate the movement of a hand, and extracting a gesture area and an actual space position of each human body; according to the gesture area and the actual space position of each human body, by combining distance measurement and a collision detection algorithm, whether the gestures of different users are spatially overlapped and crossed or not is judged, the independent gestures and the interactive gestures of the users are distinguished, the problem of misjudgment of a conventional gesture recognition method in the gesture overlapping and crossing state is avoided, and the user experience is improved. Therefore, the accuracy of gesture recognition is improved.

The invention relates to a double-robot automatic calibration method based on self-adaptive point location planning in a restricted space, and belongs to the technical field of double-robot calibration, and the method comprises the steps: building a three-dimensional simulation model of a double-robot operation space on an Rviz platform based on an ROS architecture; a collision detection module is used for randomly generating a calibration point composed of collision-free double-robot joint angle vectors; when the robot is driven to move, data of the motion capture camera and positive kinematics Cartesian coordinates of the double robots are synchronously collected, a calibration result is obtained through optimization of a least square method by adopting a hand-eye calibration algorithm, and the effectiveness of the calibration result is judged through the average value of data calibration precision until calibration of the double robots is completed. Simulation modeling, collision detection and a hand-eye calibration technology are fused, a complex calibration problem is converted into a virtual environment parameter optimization process through a self-adaptive sampling strategy and a data synchronous acquisition mechanism, calibration robustness and precision are remarkably improved, and guarantee is provided for high-precision cooperative control of double robots.

The invention provides an automatic calibration method and device for a mechanical arm, electronic equipment and a storage medium, and the method comprises the steps that the mechanical arm is controlled to conduct RCM motion in the Y-Z axis direction, and the pitch angle range is determined; performing multiple RCM rotation sampling in the pitch angle range, and determining a motion deviation error of each rotation position point based on the Y-axis unit pixel actual distance, the first needle point pixel coordinate, the first space point coordinate, and the second needle point pixel coordinate and the second space point coordinate of the rotation position point under each RCM rotation sampling; determining a calibration matrix based on the motor position of each rotation position point and a forward kinematics equation of the mechanical arm; and least square method processing is conducted on the multiple motion deviation errors and the calibration matrix, and the calibration amount of the to-be-calibrated parameters of the mechanical arm in the YZ axis calibration direction is determined. The device is low in cost, low in calibration time consumption, high in calibration accuracy and automatic, and factory calibration of the mechanical arms can be carried out on a large scale.

The present invention discloses a master-slave control method and system for a master-slave heterogeneous teleoperation system, including: an operator inputs the link parameters of the slave robot for forward kinematics and inverse kinematics analysis to obtain forward kinematics and inverse kinematics models; the operator modifies the master-slave mapping scale coefficient to achieve the movement of the slave robot with different efficiencies and accuracies; a variable-scale incremental master-slave mapping method is designed to realize the mapping from the position increment of the master robot to the position increment of the slave robot, and a BP neural network is designed to predict and compensate the master-slave following error, further reducing the master-slave following error. The present invention can not only realize the large-range movement of the slave robot in free space, but also ensure the movement positioning accuracy in the fine operation space, with strong versatility and convenient implementation.

The invention discloses a multi-degree-of-freedom pose adjustment method for a large-length-diameter-ratio target, and belongs to the technical field of multi-degree-of-freedom pose adjustment of automatic spraying equipment.The multi-degree-of-freedom pose adjustment method comprises the steps that firstly, the tail end pose deviation value of the target is obtained; and establishing a five-connecting-rod model and a forward kinematics model of the pose adjusting device. A five-connecting-rod model is constructed along the tail end of the large-length-diameter-ratio target, the pitching unit, the rotation unit, the transverse moving unit and the supporting foot unit in sequence; constructing a forward kinematics transformation matrix based on a product of coordinate transformation of the five connecting rods; and based on the forward kinematics transformation matrix, inversely solving the adjustment amount of each motion axis of the pose adjustment device at the target tail end pose deviation amount. And finally, according to an inverse solution result, adjusting each motion axis of the pose adjusting device so as to realize multi-degree-of-freedom pose adjustment of the tail end of the target with the large length-diameter ratio. According to the method, high-precision adjustment of the tail end posture is achieved, the purpose of precise adjustment of the automatic spraying length-diameter ratio target posture is achieved, and good practicability is achieved.

The invention relates to an excavator trajectory planning method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of engineering machinery trajectory planning. The method comprises the following steps: establishing a forward kinematics model through a D-H homogeneous coordinate transformation method; based on the forward kinematics model, establishing an inverse kinematics model through a geometric method; setting six path points in the track according to the mining track; based on an inverse kinematics model, converting the space coordinates of the six path points into space angles of joints; interpolation is carried out on the joint path points through a five-time B spline curve, and a track is generated; determining an optimization objective function according to actual application requirements, and setting constraint conditions according to a specific excavator type; and finally, the optimization problem is solved through an IADEMOPSO algorithm, an optimal Pareto solution is selected by using an average optimal evaluation formula, and an optimal track is generated. The track planning of the excavator can be realized.

A medical devices en-vivo positional determination system using a plurality of optical time of flight (TDF) transceivers along with gyroscopic angular and mechanical distance sensors for the determination of absolute linear and angular positional information of the tool bit tip for the purposes of more accurate hand-held drilling or cutting, on the workpiece using forward kinematic equations calculated on a microcomputer to provide a real-time display of the 3-linear position and 2-angular orientation of the tool bit (such as a drill, scalpel, or wire driver).

The invention discloses a mechanical arm autonomous obstacle avoidance planning method based on an improved artificial potential field method, and relates to the field of mechanical arm path planning and obstacle avoidance, in particular to the mechanical arm autonomous obstacle avoidance planning method based on the improved artificial potential field method. The objective of the invention is to solve the problem of low efficiency in an obstacle avoidance planning process of an existing method. The method comprises the following steps: constructing a forward kinematics transformation matrix from a base coordinate system to an end effector of the mechanical arm; a collision-free feasible path of the tail end of the mechanical arm from a starting point to a target point is solved by considering obstacles of a static part; key path points are extracted; the key path points containing the mechanical arm joint angle information are sequentially stored in a path point queue from the starting point to the end point to serve as temporary target points for improving the artificial potential field method; and key path points are selected from the path point queue to serve as current temporary target points, and an improved artificial potential field method and a collision early warning mechanism are used for guiding the mechanical arm to advance towards the temporary path points till the mechanical arm moves to the target points.

The invention provides a UE4-based mechanical arm real-time simulation and visualization method, and belongs to the technical field of virtual simulation and robots, and the method comprises the steps: building a mechanical arm model based on a UE4 editor, and configuring parameters to generate a conversion matrix and a tool coordinate system matrix; the UE4 editor establishes communication with a mechanical arm controller, and receives a current mechanical arm joint angle and output data sent by the mechanical arm controller according to a preset frequency; the UE4 editor resolves the current mechanical arm joint angle through a forward kinematics algorithm based on D-H parameters, and position information and attitude information of a mechanical arm end effector are obtained; and the conversion matrix is updated based on the tool coordinate system matrix, the position information and the attitude information so as to control the position and the attitude of a mechanical arm joint of the mechanical arm model, the motion trail of a mechanical arm end effector is generated based on the position information, and the color of the motion trail is changed based on the output data.

The invention discloses an intelligent robot grabbing control method and system based on AI vision, original vision and kinematics flow are fused through space-time alignment, and a physical-level dynamic shielding mask is accurately extracted through forward kinematics and reverse perspective projection. Then, the limitation of traditional pure semantic matching is broken through, a narrow-band gradient reference field of a shielding boundary is explicitly extracted to serve as a spatial topology bias injection attention mechanism, feature illusion completion guided by physical continuity prior is executed in a hidden space, and high-fidelity perspective features are generated; on the basis, the network synchronously regresdes six-degree-of-freedom grabbing pose and execution uncertainty, the uncertainty serves as an independent variable to dynamically attenuate and adjust the impedance rigidity of the mechanical arm, finally, self-adaptive compliant interaction in a blind area is achieved in a bottom layer physical servo link, and space positioning precision and operation safety are both considered.

The application discloses a mobile redundant manipulator real-time grabbing and trajectory tracking method and system based on optimization theory. In the method, the mobile redundant manipulator platform carries out real-time distance measurement on a target object based on a camera, and when the distance is less than a set threshold, a grabbing operation is performed. The application constructs a mobile redundant manipulator platform dynamics model based on robot forward kinematics, establishes a mobile redundant manipulator platform optimization model based on kinetic energy loss, designs a corresponding objective function, and sets constraint conditions of the corresponding optimization model in combination with physical limit conditions of the mobile redundant manipulator platform. By sampling time, inputting a current state of the mobile redundant manipulator platform, solving joint angular velocity and mobile platform speed at a next time, real-time trajectory tracking of the mobile redundant manipulator platform is realized. The application greatly increases the working range of the manipulator, the model is simple and easy to solve, accurate trajectory tracking can be realized, and the application prospect is wide.

The invention provides a digital twinning five-axis machine tool kinematics transformation method, system and device and a medium, and belongs to the field of numerical control technology and digital twinning crossing technology. The method comprises the steps that axis parameters are collected from a five-axis machine tool controller; constructing a unified five-axis parameter set comprising axis types, axis reference points and direction vectors; a hierarchical homogeneous transformation chain is constructed in a machine tool base coordinate system based on the parameter set, and forward kinematics transformation from a mechanical coordinate system to a workpiece coordinate system is achieved; and based on the unified parameter set and the transformation chain, reversely solving an axis position vector from a tool position instruction of the workpiece coordinate system and mapping the axis position vector back to a controller coordinate. By adopting the digital twin five-axis machine tool kinematics transformation method, system and equipment and the medium, the problems of poor model reusability and inconsistent virtual-real mapping caused by machine tool structure difference and numerical control system diversity are solved, cross-platform and cross-model general reversible kinematics transformation is realized, and the method has the advantages of high practicability and high practicability. And the engineering applicability and deployment efficiency of the digital twin model are improved.

The invention discloses an IMU (Inertial Measurement Unit) drift error calibration method for long-term attitude measurement of an industrial robot. The IMU drift error calibration method comprises the following steps: deducing a pose matrix between a robot base coordinate system and a navigation coordinate system; deriving a pose matrix between a robot tail end coordinate system and a sensor body coordinate system; kinematics modeling is conducted on the industrial robot, and motion parameters are obtained; deriving a homogeneous transformation matrix between two adjacent joints according to the kinematic parameters; acquiring actual joint corner information when the robot runs and actual information measured by a tail end IMU when the robot runs; wherein the actual information of the IMU comprises actual angular velocity information and actual attitude information; the collected actual joint rotation angle information is substituted into the homogeneous transformation matrix, theoretical attitude information of the tail end of the robot is obtained, and the theoretical attitude information is converted into a navigation coordinate system; converting the theoretical attitude information converted into the navigation coordinate system into an Euler angle; designing a Kalman filter; according to a Kalman filter, data fusion of theoretical attitude information and actual attitude information is completed, and calibration is achieved. According to the method, joint angle information, the industrial robot end theoretical attitude obtained through forward kinematics calculation of the kinematics model and actual attitude data obtained through IMU measurement are fused, and accumulated error calibration formed in long-term attitude measurement of the IMU is achieved.

The invention discloses a robot whole body control method and system based on real-time inertia motion capture and reinforcement learning, and belongs to the field of robot control. The system comprises a motion capture module, a data processing and forward kinematics module, a motion redirection and optimization module, a general motion strategy control module and a real-time control and driving module which are connected in sequence, and an end-to-end closed-loop control assembly line is formed through a shared memory and a high-speed communication protocol. According to the method, human body joint data are collected through inertia motion capture equipment, high-quality mapping from a human body posture to a robot model is achieved through posture calculation, non-uniform scaling and two-stage differential inverse kinematics optimization, and a stable joint instruction is generated in combination with a reinforcement learning strategy trained by a PPO algorithm. The method solves the problems of lack of feedback closed loop, joint deadlock, contradiction between fidelity and stability and the like in the prior art, has the characteristics of high robustness and high expansibility, and can be widely applied to scenes of remote operation, action reproduction and the like.

The invention discloses a robot multi-component low-cost high-precision calibration method based on a consumer-level camera, and the method comprises the steps: photographing a calibration plate through the consumer-level camera, and optimizing the internal parameters of the camera through corner detection and nonlinear optimization; a camera is installed at the tail end of the mechanical arm, about 50 sets of calibration plate images are shot, the pose is obtained by means of forward kinematics of the mechanical arm, and the pose relation between the camera and the mechanical arm is analyzed through the formula AX = XB; then the chassis and the camera on the mechanical arm are used for joint calibration, the calibration relation between the mechanical arm and the trolley is solved by moving the trolley and the mechanical arm observation calibration plate, and finally the calibration precision is verified by calculating the re-projection error. The method does not need to depend on high-precision equipment and a professional calibration field, calibration can be completed by using a common consumer camera and a simple calibration board, the method can adapt to various cameras, the requirement for computing equipment is not high, and the requirement can be met by a common workstation. The method can be carried out in a dynamic scene, the calibration time is within 1 hour, and the precision is within 5 mm.

The invention discloses a multi-degree-of-freedom mechanical arm inverse kinematics real-time solving method and device, and the method comprises the following steps: building a forward kinematics model of a mechanical arm, so as to obtain a forward kinematics solution of the mechanical arm; defining a loss function, and calculating a current pose error of the mechanical arm in combination with a forward kinematics solution; when the current pose error is greater than a set error threshold value, calculating the gradient of the pose error; gradient normalization processing is carried out to prevent gradient explosion or disappearance; gradient filtering after gradient normalization is carried out, and interference of noise on a resolving result is reduced; the step length is adaptively adjusted through the PID controller; step length delta is set and limited, impact is prevented, joint variables are updated, and real-time performance is guaranteed; a track is obtained through calculation, and smooth filtering processing is carried out on the track; and recalculating the error until a set threshold condition is met. According to the method, through the improved gradient descent algorithm, the convergence speed and the calculation precision of inverse kinematics solution of the mechanical arm are effectively improved, and robustness and applicability are achieved.

The invention relates to the technical field of intelligent control, and particularly to a gantry platform kinematics modeling method considering parallelism and perpendicularity errors, which is applied to a gantry platform device. The gantry platform device comprises a crossbeam, a first guide rail and a second guide rail, one end of the crossbeam is in sliding fit with the first guide rail, and the other end of the crossbeam is in sliding fit with the second guide rail; the first guide rail is provided with a first longitudinal motor, and the second guide rail is provided with a second longitudinal motor; and the crossbeam is provided with a transverse motor configured to drive the gantry platform to move along the crossbeam. The method comprises: establishing a two-dimensional coordinate system; constructing a forward kinematics solution model; and constructing an inverse kinematics solution model.

Provided are a fusion system of a mechanical arm and a dexterous hand, and a control method therefor. The fusion system comprises a four-degree-of-freedom mechanical arm, a dexterous hand wrist mounted at the tail end of the four-degree-of-freedom mechanical arm, and a dexterous hand mounted on the dexterous hand wrist, wherein the dexterous hand wrist is a single-joint spatial orthogonal two-degree-of-freedom wrist. In a motion control method, the fusion system of the four-degree-of-freedom mechanical arm and the dexterous hand is regarded as a six-axis series mechanical arm, and according to a joint angle, a position and an attitude of the tail end of the dexterous hand wrist can be obtained by means of a forward kinematics method; and according to the position and attitude of the tail end of the dexterous hand wrist, the joint angle can be obtained by means of an inverse kinematics numerical solution method, and then a control instruction is obtained and sent to a servo position controller, so as to drive the tail end of the dexterous hand wrist to reach a preset position and attitude. A six-degree-of-freedom series-parallel mechanism combining the mechanical arm and the dexterous hand wrist has multi-degree-of-freedom redundancy, such that rotation of the five-finger dexterous hand can be achieved without a wide-range motion of the mechanical arm.