705 results about "Inverse kinematics" patented technology

The invention relates to the technical field of equipment control, and provides a vision-based collaborative robot end control method and related equipment thereof. Multi-modal dynamic anti-interference filtering and semantic fusion are carried out on original visual data to obtain three-dimensional semantic point cloud data, and coordinate system alignment is carried out on the three-dimensional semantic point cloud data according to a robot-based coordinate system to obtain a target pose parameter set; performing dynamic path planning on the target pose parameter set to obtain a joint space motion track, and performing inverse kinematics solution and motor instruction synthesis on the joint space motion track according to robot D-H model parameters to obtain a motor control instruction, and performing closed-loop feedback optimization on the motor control instruction according to the state data of the end effector to obtain a dynamic correction control instruction. According to the method, through full-link optimization from sensing, planning to execution, the operation precision and safety of the collaborative robot in an uncertain environment are remarkably improved.

The embodiment of the invention provides an unhooking robot control method based on multi-source visual fusion, which is applied to the technical field of robot control and comprises the following steps: acquiring an RGB image, a depth image, an infrared image and IMU data through a multi-source sensing system mounted at the tail end of a robot; carrying out feature fusion identification by adopting a double-branch neural network, and outputting the boundary contour of the lifting hook and the three-dimensional coordinates of the optimal grabbing point; the visual coordinates are unified to a robot base coordinate system through a registration correction mechanism; a Transform prediction model is constructed based on the visual and inertial signals, and future pose changes of the lifting hook are estimated; a feedforward control track is generated to counteract swing of the lifting hook, and track correction is carried out in combination with visual servo feedback; and a joint instruction is generated through path planning and inverse kinematics solution, and the mechanical arm is driven to complete precise unhooking operation. According to the method, the recognition precision, the anti-interference capability and the operation success rate of unhooking operation in complex illumination and dynamic environments are effectively improved.

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

The invention relates to the technical field of automatic control, and discloses a path planning method and device for mechanical arm and laser synchronous machining, and the method comprises the steps: obtaining a workpiece three-dimensional model, a material and technological parameters, and extracting an irregular curved surface to generate a discrete point set; the tail end pose of the mechanical arm is calculated through inverse kinematics, and a preliminary joint angle and a track sequence are obtained. The optimal laser parameters are matched in combination with the parameters, a change curve is generated and synchronously mapped to a joint angle sequence, and a synchronous joint angle sequence is obtained through spline interpolation fine adjustment. And fitting a smooth track by using a B spline curve, and generating a motion track control instruction in combination with the synchronous joint angle. And the mechanical arm and the laser move according to instructions, the track compensation coefficient is dynamically adjusted when the environment changes, online compensation is conducted through the self-adaptive Kalman filtering algorithm, and a correction control instruction is obtained. According to the method, irregular curved surface machining details are extracted, the mechanical arm and the laser working sequence are aligned, the machining environment is sensed in real time, the adaptability of path planning is improved, and therefore efficient and accurate machining is achieved.

The invention relates to the technical field of numerical control machining, in particular to a CAM post-optimization method and system for geometric error compensation of a multi-axis linkage numerical control machine tool. The method comprises the following steps: firstly, carrying out kinematics analysis and geometric error modeling on a target machine tool, and deploying the target machine tool in a CAM post-optimization module; then actual state parameters of the target machine tool in the machining process are collected in real time through a laser interferometer; a motion instruction of a target machine tool and a detection result of a laser interferometer are synchronously obtained through a CAM post-optimization module, and then geometric errors of a linear axis and a rotating axis in the operation process of the target machine tool are calculated in real time through a kinematic model and a geometric error model. According to the method, different linear axis compensation modes are adopted for different instructions; and a cutter shaft attitude vector error and a cutter cutting point position error are calculated in combination with inverse kinematics, and are corrected step by step. The problem that the error compensation precision of a controller in an existing numerical control machine tool is insufficient can be solved.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

The invention discloses a multi-fingered dexterous hand grabbing posture generation method and system based on CVAE and a Ball Query algorithm, and belongs to the technical field of robot grabbing control. The method comprises the following steps: (1) a data sampling step; (2) a data preprocessing step: carrying out standardization processing on the collected data, generating enhanced point cloud data and constructing a training data set; (3) a model training step: through a multi-scale feature extraction module, combining global semantics and local geometric features extracted by a Ball Query algorithm, generating a grabbing attitude by using a conditional variation auto-encoder, and optimizing model parameters through reconstruction loss and KL divergence; and (4) real-time deployment: integrating the trained model to a physical platform, screening candidate grabbing postures based on parallel collision detection, and realizing real-time grabbing control in combination with inverse kinematics verification. The problem that a traditional method is insufficient in generalization ability in complex object grabbing is effectively solved, and self-adaptive grabbing of unknown objects is achieved while grabbing stability is guaranteed.

The invention discloses a dynamic path planning method and system for a multi-axis collaborative welding robot, and belongs to the field of robot welding. The method comprises the steps that basic data of a welded workpiece is collected, and a welding basic database is constructed; constructing a geometric model of the welding seam path according to the data in the welding basic database, and parameterizing the geometric model of the welding seam path; on the basis of the parameter result, an initial track is generated between two adjacent weld joint sections of the welding path, time parameterization is conducted on the initial track, and a time-dependent time parameterization spinor track is obtained; and constructing a spinor mapping model based on inverse kinematics and a spinor theory to convert the time parameterized spinor trajectory into a target spinor. The welding quality is improved.

The invention discloses an automatic dovetail welding seam grinding control method based on robot visual positioning, and relates to the technical field of visual positioning. The method comprises the steps that after welding is completed, a dovetail welding seam image is collected and converted into a three-dimensional coordinate through a vision algorithm, and a three-dimensional point cloud is generated; smooth interpolation is performed on the three-dimensional point cloud through a B spline curve method to obtain a parameterized curve, and a control point set is optimized through a genetic algorithm to generate a global optimal polishing path; the polishing robot executes a task according to a path, a tail end sensor collects a real-time path and calculates a deviation value with a global path, and when the deviation value exceeds a preset threshold value, inverse kinematics is triggered to solve and correct the path; the hardness of the dovetail weld is measured through laser-induced breakdown spectroscopy, a comprehensive hardness value is obtained in combination with a matrix hardness database, meanwhile, a visual sensor collects the surface state, and polishing process parameters are dynamically adjusted according to the surface state; after the task is completed, the welding seam angle and flatness are detected. According to the method, the optimal path is generated through visual positioning, and automatic grinding of the dovetail welding seam is achieved.

The invention discloses a physical fitness evaluation method based on human skeleton trajectory tracking, which comprises the following steps: S1, acquiring whole-process video data of a physical fitness test through video acquisition equipment, and generating a video frame according to an acquisition frequency; s2, processing the video frame by using a skeleton dynamic analysis algorithm, and extracting human skeleton key points; s3, extracting identity features of the testee through a motion map identity recognition network, and tracking and confirming the identity features; s4, performing action recognition, fragment segmentation and compliance judgment on the skeleton key point time sequence track; s5, aiming at the abnormal skeleton key points, performing complementation and time sequence smoothing by adopting an inverse kinematics inference method; s6, inputting the complete skeleton key point time sequence data into the human body action evaluation model for processing, and quantizing and outputting a physical ability evaluation index; and S7, generating a physical ability evaluation report and giving action feedback and optimization suggestions. According to the invention, objectivity, accuracy and intelligent level of physical ability evaluation are effectively improved.

The invention relates to the technical field of control adjustment, in particular to an intelligent multi-head welding synchronous control system which comprises a data processing module, a modular cooperative control module, a load sensing module, a data transmission module, a self-adaptive pose execution module and a fault-tolerant module. The data processing module extracts three-dimensional geometrical characteristics of a workpiece through laser scanning, PID control parameters are migrated across working conditions in combination with a dynamic time warping algorithm, the inverse kinematics process is optimized, and the self-adaptive pose execution module drives phase synchronization of multiple welding heads based on a global reference trajectory and predicts and compensates thermal deformation errors through a thermal expansion coefficient. And the fault-tolerant module adopts a wavelet packet decomposition identification fault mode, and updates a historical parameter library in combination with an elastic weight algorithm. According to the method, three-dimensional space pose error accumulation caused by mechanical load fluctuation and signal asynchronization is inhibited through cooperation of multiple modules, and the welding process uniformity and the system dynamic stability are improved.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.

Provided is a method for improved hand-eye calibration method based on a structured light camera. The method includes: step 1: establishing a pinhole camera model, and using a depth camera to detect a three-dimensional (3D) coordinate to obtain a physical coordinate of each point in an image coordinate system with a known depth relative to a camera coordinate system; step 2: establishing a Denavit-Hartenberg (DH) model of a robotic arm, and moving the robotic arm to a determined coordinate using inverse kinematics; step 3: collecting n sets of point cloud data, applying depth scaling coefficients to the n sets of point cloud data to perform Singular Value Decomposition (SVD), and solving for an optimal depth scaling coefficient using a Nelder-Mead algorithm; and step 4: completing the hand-eye calibration of the robotic arm based on the solved optimal depth scaling coefficient. The method offers strong operability and robustness.

The invention relates to a ball action evaluation method, device and equipment based on a large model, and a medium, and the method comprises the steps: collecting the three-dimensional coordinates of the joints of a student and the trajectory data of sports equipment, and generating a standardized action sequence through space-time compensation and noise suppression; based on inverse kinematics and differential kinematics technologies, human body joint rotation features and instrument motion parameters are separated and extracted, and a multi-dimensional motion feature vector is constructed; calculating a dynamic mahalanobis distance and a key joint contribution degree by adopting a comparative learning mechanism and a standard action template, and generating a quantitative evaluation result; and finally, through inverse kinematics optimization and three-dimensional visual rendering, outputting an action correction scheme. According to the method, the limitation of traditional single-mode evaluation is broken through, deep analysis of instrument-human body collaborative motion features is realized, the technical problems of inaccurate action detail capture, single evaluation dimension and insufficient feedback visualization in a high-speed scene are effectively solved, and the scientificity and real-time performance of training guidance are remarkably improved.

The invention relates to the technical field of image data processing and intelligent control, and discloses a patch board spot welding track intelligent control method and system based on visual perception. According to the method, synchronous image streams are collected through a binocular vision sensor, and epipolar correction image pairs are generated through timestamp synchronization, ROI extraction and epipolar correction processing; calculating a disparity map by adopting a stereo matching algorithm, and constructing a compensated three-dimensional point cloud model based on deformation vector field fusion multi-frame point cloud data of a radial basis kernel function; welding spot position error vectors are generated by extracting welding spot feature points and performing spatial filtering optimization; and carrying out inverse kinematics calculation on the mechanical arm by adopting a damping least square method, carrying out safety constraint optimization in combination with prospective collision risk assessment and real-time pose data, and generating a trajectory compensation instruction. According to the method, the problems of dynamic deformation compensation and motion safety in patch plate welding are solved, the submillimeter welding spot positioning precision is achieved, and the welding quality stability and the system robustness are improved.

The invention provides a lead screw precision milling closed-loop optimization and real-time control process based on multi-axis linkage, which comprises the following steps of: acquiring geometric parameters of a workpiece, material attributes and kinetic parameters of a machine tool, designing a cutter and clamp scheme, and setting initial processing parameters based on a cutting database; constructing a lead screw spiral groove high-precision curved surface model by using an NURBS curve, and optimizing a path through curvature adaptive discretization and quintic spline interpolation; an idle stroke path is iteratively optimized by adopting a real number coding genetic algorithm, and the total time of the path and the reversing frequency are shortened; multi-axis synchronous control is achieved through inverse kinematics solution, and the cutter inclination angle is dynamically adjusted in real time based on the contact angle; and virtual simulation and finite element analysis are utilized to predict cutting force, thermal deformation and interference risks. According to the method, the continuous and smooth multi-axis synchronization instruction is generated, the positioning error of each axis is effectively reduced, meanwhile, the cutter inclination angle is dynamically corrected through force feedback of the contact angle, cutting force fluctuation is effectively restrained, and it is ensured that the tooth surface roughness of the spiral groove meets the requirement.

The invention provides a real-time monitoring system and method for sorting of a digital twin-driven Delta mechanical arm. According to the system, firstly, a physical experiment platform is built, and a virtual model is built through SolidWorks, 3DMAX and Unity 3D. A communication framework is designed, and wireless communication of the PLC, the sensor, the mechanical arm, the conveying belt and the digital twin system is achieved. Positive and inverse kinematics analysis and Cartesian space trajectory planning are carried out on a Delta robot, and a related algorithm is packaged into an MATLAB function and compiled into a DLL library for C # calling. The system collects joint angle data in real time based on a rotary encoder, transmits the joint angle data to a digital twin system through an OPC UA protocol, and drives a virtual mechanical arm model after resolving, so that high-precision and low-delay virtual-real synchronous pose reconstruction is realized. In addition, the system also develops a collision detection function based on a bounding box algorithm, and performs visual detection by using camera calibration and a YOLO model.

The invention belongs to a robot control method, and provides a human-guided robot-environment interaction self-adaptive control method and a related device for solving the technical problems that an existing human-guided robot-environment interaction control method is insufficient in motion stability, low in human operator safety and low in control precision. And estimating an optimal impedance parameter through a state space model, and learning a human-guided reference trajectory through a neural network in combination with the optimal impedance parameter. Then, in combination with a robot Jacobian matrix and a human-guided reference trajectory, a reference joint speed and an estimated acceleration of a joint space are obtained through closed-loop inverse kinematics, then network parameters of a robot dynamic model are obtained through approximation of an uncertain dynamic model, and finally, the robot dynamic model is obtained through combination with the reference joint speed and the network parameters of the joint space. And the mechanical arm control torque is obtained through the offset width fuzzy neural network. Accurate and compliant control under uncertain motion and dynamics conditions is realized.

The invention relates to the technical field of surgical robots, and discloses an inverse kinematics control method for a surgical robot, which comprises the following steps: establishing a kinematics model of the surgical robot, including the position and posture of an end effector, a joint angle and a Jacobian matrix, and defining an operability index; constructing a hierarchical optimization framework based on operation task priorities, decomposing an inverse kinematics problem into RCM constraint, end effector pose control, joint limiting constraint and operability maximization tasks, and sequencing according to the following priorities: the RCM constraint is the highest priority, the end effector pose control and the joint limiting constraint are the secondary priorities, and the operability maximization task is the highest priority; the maximization of the operability is the lowest priority; and sequentially solving the optimal joint speed of each priority task according to a priority sequence through a hierarchical quadratic programming framework, and ensuring that the solution of the low-priority task is optimized in the null space of the high-priority task by utilizing a null space projection matrix. The operability of the robot is maximized.

The invention relates to an all-humanoid robot motion redirection method and device based on motion capture equipment, and the method comprises the steps: converting a human body posture quaternion collected by the motion capture equipment into a rotation matrix, and achieving the precise mapping from a Cartesian space to a robot joint space in combination with an Euler angle decomposition strategy; a quaternion-disassembled joint angle is used as an initial solution, various physical constraints are introduced in real time according to the actual supporting state of the robot, and the lower limb joint angle is corrected online by constructing and solving an inverse kinematics problem with various constraints, so that the robot reproduces the action of an operator, and the joint angle of the lower limb can be corrected online. And the requirements of joint limiting, self-collision avoidance, dynamic balance and the like are strictly met, so that the accuracy of motion simulation and the stability of the system are effectively unified.

The invention discloses a path planning method and system for curved surface integrated conformal jet printing, and belongs to the field of additive manufacturing. According to the method, the geometric data of the non-deployable curved surface are obtained and converted into the discretized grid model, then the plane pattern is designed and mapped to the three-dimensional curved surface, the mapped pattern is locally optimized, and the complete printing area model is generated. Path planning is divided into initial jet printing path generation and five-axis linkage motion path planning, conformal characteristics are achieved through an adaptive interpolation algorithm and NURBS curve optimization, five-axis motion parameters are calculated based on an inverse kinematics algorithm, and the positioning relation between a jet printing head and a curved surface is kept through the RTCP technology. The method solves the problems of precision and path planning efficiency in complex curved surface manufacturing, and is suitable for manufacturing heterogeneous complex curved surface functional structures in the fields of aerospace, medical instruments and the like.

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 provides an object grabbing method and system based on image segmentation and object pose estimation, and relates to the technical field of computer vision, and the method comprises the steps: obtaining an original image of a to-be-grabbed object; preprocessing the original image to obtain a target image; according to the target image, generating an object mask of the to-be-grabbed object; according to the object mask, determining a 2D bounding box of the object to be grabbed in the target image; the pose of the to-be-grabbed object is preliminarily estimated through the rotation and translation sampling technology, and the preliminary pose is obtained; performing refining estimation on the pose of the to-be-grabbed object through the pose refining network to obtain a refined pose; the refining pose is converted from a camera coordinate system to a mechanical arm coordinate system; on a mechanical arm coordinate system, based on predefined fixed transformation, a target grabbing pose is calculated; and through an inverse kinematics algorithm, the target grabbing pose is converted into a joint angle of the mechanical arm, and the mechanical arm is controlled to grab the to-be-grabbed object according to the joint angle.

The invention belongs to the technical field of industrial robots, and particularly discloses a six-axis robot trajectory planning method in a non-structural environment, which comprises the following steps: presetting starting points of an initial motion trajectory for a six-axis robot in a non-structural environment, performing interpolation between the starting points, and performing kinematics inversion solution of the six-axis robot, determining the position of a joint at each interpolation point; fitting and deriving all interpolation points by utilizing a quintic B-sample curve, and determining the speed, acceleration and impact of each joint; by taking minimization of total motion time, average acceleration and impact of joints as optimization objectives and taking acceleration and impact as constraint conditions, an objective function of trajectory planning of the six-axis robot in the non-structural environment is constructed; an improved multi-target grey wolf optimization algorithm is adopted to optimize the target function, and the position, the speed, the acceleration and the impact of the optimized six-axis robot joint are obtained; and determining the motion track of the six-axis robot based on the optimized position, speed, acceleration and impact of the joint of the six-axis robot.

The invention discloses a manipulator grabbing control method and equipment based on position and posture recognition. The manipulator grabbing control method comprises the following steps that S1, initial image information of a target object is acquired through image acquisition equipment; s2, performing preprocessing and feature extraction on the initial image information, and obtaining three-dimensional position coordinates and attitude parameters of the target object through a preset position and attitude recognition algorithm; and S3, according to the three-dimensional position coordinates and the posture parameters, a grabbing path of the manipulator is planned by combining a kinematic model of the manipulator. By introducing technical means such as a deep learning recognition algorithm, forward / inverse kinematics model collaborative planning, real-time posture dynamic adjustment and force sensing feedback control, the problems that a traditional mechanical arm is low in grabbing precision, poor in adaptability and insufficient in operation stability are solved, intelligent and high-precision grabbing control in a complex scene is achieved, and the grabbing precision of the mechanical arm is improved. And the requirements of the modern industry on high efficiency, reliability and flexibility of automatic equipment are met.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling a robot to achieve a motion trajectory that simultaneously optimizes joint manipulability and joint-limit distance. In one aspect, a system comprises receiving a first waypoint that represents the current pose of a robot comprising a plurality of joints, receiving a second waypoint that represents a target pose of the robot, obtaining a first joint state that defines an angular joint configuration that corresponds with the first waypoint of the robot, generating a motion between the first joint state and a second joint state that defines an angular joint configuration that corresponds with the second waypoint of the robot by performing an optimization process that simultaneously optimizes a unified joint-manipulability-and-joint-limit-distance metric of the robot, and generating control rules for the motion to be followed by the robot that achieves the target pose of the second waypoint.

The invention relates to the technical field of cooperative control, and discloses an anti-corrosion pipe winding angle and linear speed cooperative control method and system. The method comprises the following steps: collecting winding angle and linear speed time sequence data, and constructing a three-layer scale feature vector and a real track deviation; inputting a three-branch LSTM model to output a prediction deviation sequence; calculating a feed-forward compensation amount through an inverse kinematics model; and performing model increment updating based on the prediction error, calculating a feedback compensation amount, and adaptively fusing feedforward and feedback compensation to obtain a cooperative control instruction. The technical problems that in traditional fixed parameter control, dynamic cooperation of the angle and the speed is lacked, compensation response lags behind, feedforward and feedback conflicts exist, and a prediction model cannot conduct self-adaptive evolution are solved.

The invention provides a ship motion prediction and compensation method based on domain adaptive transfer learning, and aims to solve the prediction and compensation problems of rolling motion of a service workboat in offshore wind power construction under complex sea conditions. According to the method, ship rolling angle data are collected in real time through a sensor, and time similarity quantitative division is carried out on the data to divide the data into source domain motion data and target domain motion data. And then, processing the data by using a domain adaptive transfer learning model, and predicting ship rolling motion at a future moment. The prediction result and actual data are subjected to inverse kinematics solution to obtain the expansion and contraction amount of the six electric cylinders of the Stewart platform, the expansion and contraction amount is fed back to a ship control system, the ship attitude is adjusted in real time, and the operation safety and efficiency are improved. According to the method, model parameters can be dynamically adjusted according to feature differences under different sea conditions, the prediction precision is remarkably improved, the method is suitable for rolling motion prediction and compensation under variable sea conditions, and improvement of the safety and stability of SOV operation is facilitated.

The invention discloses a wafer carrying speed control method and system, and the method comprises the steps: obtaining the three-dimensional data of a carrying path, decomposing the data into a straight line and a bent segment, and obtaining geometric features; the curvature of the bent section is calculated and smoothly connected, and continuous curvature distribution is determined; dynamically constraining the speed upper limit of each point according to the curvature, and generating a preliminary speed curve; the speed of each axis is calculated through inverse kinematics, iterative optimization is carried out until the speed of each axis is coordinated, and a multi-axis cooperative speed sequence is generated; calculating attitude deviation based on real-time sensor data fusion; and dynamically adjusting the acceleration parameter, optimizing and reducing the deviation, and obtaining a final control instruction. According to the control method, through curvature dynamic planning, multi-axis collaborative optimization and real-time posture self-adaptive adjustment, synchronous improvement of the carrying speed and stability is systematically achieved.

The invention discloses a robot action prediction method utilizing displacement vector traction and fusing historical information, which comprises the following steps: collecting training data in a simulation environment, including text description of a task executed by a mechanical arm and gray level images of the tail end and the head of the mechanical arm; a multi-modal fusion encoder is used for carrying out multi-modal fusion encoding on the text description and the grayscale image, and multi-modal fusion encoding information is output; a historical information encoder is adopted to encode the multi-modal fusion encoding information and the historical joint posture information to obtain historical encoding information; calculating a joint displacement vector and an end effector displacement vector; the multi-modal fusion coding information, the displacement vector, the robot joint posture information, the end effector posture and historical coding information are input into a transformer architecture, the action partitioning and time integration technology in ACT is adopted, the end poses of the robot at multiple moments in the future are predicted, and the robot joint angles at the multiple moments in the future are obtained through inverse kinematics conversion.