1484 results about "Kinematical model" patented technology

The invention discloses a multi-sensor fusion-based position and posture estimation method for a working device of a mining robot, which realizes accurate position and posture estimation by installing a body posture sensor and an environment sensing sensor and combining a kinematic model, visual inertia state estimation and a nonlinear least square optimization algorithm. The method comprises the following steps: firstly, carrying out preliminary prediction on a kinematic model through feedback data of an encoder and an inclinometer, and constructing a local visual inertia estimator by utilizing a camera and an MEMS to correct a prediction result; and then, a combined constraint modeling and optimization method is adopted to improve the precision of the pose of each working mechanism. Besides, geometric features of the bucket are extracted through a semantic segmentation network and a template matching technology, six-degree-of-freedom pose estimation of the bucket is completed, finally data fusion of a body pose sensor and an environment perception sensor is performed through a factor graph optimization algorithm, and comprehensive pose estimation of the whole working device is achieved. The working pose of the mining robot can be monitored in real time, the operation precision is improved, human errors are reduced, the working efficiency and safety can be optimized through data analysis, and more effective support is provided for path planning of a working device of the mining robot.

The invention relates to the technical field of intelligent driving, in particular to a method for generating an obstacle avoidance and planning cooperative path in an urban complex environment, which comprises the following steps: acquiring environment real-time sensing data through a vehicle-mounted multi-sensor, and constructing a dynamic semantic traffic matrix in combination with high-precision map static semantic information; based on the matrix, a multi-objective optimization algorithm is adopted to calculate the security cost, the efficiency cost and the rule conformity cost of the path, and a global optimization path is generated; inputting the global optimization path and the dynamic obstacle motion vector into an intention prediction model, generating dynamic obstacle future trajectory probability distribution and interactive intention classification, and further generating an avoidance strategy and adjusting a local path in real time; and inputting the adjusted local path into a kinematics model to carry out kinematics feasibility verification, and outputting an executable track or path re-planning. According to the method, the integration of dynamic environment understanding, path planning and obstacle avoidance strategies is realized, and the method is suitable for the path generation task of an automatic driving system in an urban complex traffic scene.

The invention relates to the technical field of robots, and discloses a multi-source data fusion method of a tracked robot, which realizes dynamic environment adaptation and autonomous decision optimization through a multi-source sensing cooperation mechanism, and comprises the following steps: dynamically adjusting exposure parameters of a visual module based on environment illumination intensity, and avoiding overexposure or underexposure of an image through a feedback adjustment mechanism; fusing data of the accelerometer and the gyroscope to generate attitude angle compensation parameters; the heat dissipation system is intelligently regulated and controlled by comparing infrared thermal imaging and temperature sensor data; dynamic task priority distribution is realized; optimizing an advancing route to reduce complex terrain navigation deviation, in addition, establishing a multi-source sensing coordination mechanism, monitoring health indexes such as motor temperature and vibration frequency in real time, and triggering abnormal state judgment; track optimization is implemented in combination with pavement mechanical characteristics and a kinematic model; and executing the staged energy management strategy. According to the method, the environment sensing precision and the motion control reliability of the tracked robot in a complex environment are remarkably improved.

The invention discloses a multi-unmanned-ship cooperative multi-target uniform hunting control method, and relates to the technical field of multi-unmanned-ship cooperative multi-target hunting. In order to solve the technical defects that in the prior art, an existing multi-unmanned-ship cooperative hunting technology is low in efficiency, insufficient in hunting precision and stability, complex in gain design and prone to generating overlarge gain, the technical scheme provided by the invention comprises the steps that kinematics models of a multi-unmanned-ship system and a multi-target system are constructed; defining a target state of the hunting task; based on the kinematic model and the target state, establishing a distributed target center estimator; constructing a surrounding controller according to target geometric center information provided by the distributed target center estimator; based on the control strategy of the hunting controller, the angular velocity of the unmanned ship is adjusted to achieve the consistency of phase interval angles; and dynamically adjusting parameters of the hunting controller, and outputting the parameters to the multi-unmanned ship system. The method is suitable for being applied to the cooperative multi-target uniform hunting control work of multiple unmanned ships.

The invention discloses an intelligent logistics scheduling method and device based on a dynamic weight and a medium, and the method comprises the steps: carrying out the fusion processing of the operation environment data of a logistics carrier, so as to construct a dynamic environment model, and carrying out the fusion processing of the operation environment data of the logistics carrier based on a reinforcement learning model according to a real-time environment state and a multi-target optimization demand. Dynamically adjusting the weight vector of each logistics parameter in the logistics carrier; predicting the space-time trajectory of each logistics carrier based on the kinematic model of the logistics carrier, mapping the space-time trajectory to a three-dimensional grid, identifying a potential conflict area between the logistics carriers, and calculating a corresponding conflict severity index; based on the dynamic weight vector and the conflict severity index, generating an obstacle avoidance path of the logistics carrier by adopting a global path re-planning algorithm, and optimizing the obstacle avoidance path through a local track to generate a corresponding smooth track; and executing a scheduling instruction corresponding to the smooth trajectory, and collecting an execution feedback result in real time to update the reinforcement learning model parameters and the dynamic weight strategy.

The invention discloses an inspection robot and an inspection method thereof, and relates to the technical field of robots, and the inspection robot comprises a remote control center which is in communication connection with an environment perception and mapping module, an intelligent movement and obstacle avoidance control module, and an intelligent analysis and user interaction module; the environment sensing and mapping module integrates a laser radar, an ultrasonic sensor and an RGB-D camera, and realizes multi-dimensional environment data acquisition; the intelligent movement and obstacle avoidance control module adopts an SLAM algorithm to position and map, adopts an RRT algorithm to plan a path, adopts a CNN algorithm to recognize an obstacle, and adjusts movement through a PID controller and a kinematic model to realize intelligent obstacle avoidance; the intelligent analysis and user interaction module reduces delay through edge calculation, provides real-time data processing and a graphical user interface and displays the state and operation guide of the robot. On the whole, by integrating various sensors and advanced algorithms, efficient and intelligent inspection operation is realized, and the inspection efficiency and safety are improved.

The invention provides a narrow-space-oriented truck-mounted crane manual arm operation path planning method and system, and relates to the technical field of path planning, and the method comprises the steps: carrying out the multi-source information fusion through binocular vision and laser ranging, and building an operation space three-dimensional model; a convolutional neural network is adopted to identify an obstacle, a kinematics constraint is combined to establish an obstacle avoidance cost function, and a bidirectional probability sampling algorithm is utilized to generate and optimize an initial path. Afterwards, a target path is screened by considering the dynamic constraint of the load weight, and dynamic adjustment is carried out based on a comprehensive evaluation function of path length, smoothness and safety degree and a virtual spring damping model; and finally, performing polynomial fitting on the adjusted path, calculating a joint angle and performing local correction according to a kinematic model, and finally outputting a working path meeting constraint conditions. According to the invention, the working efficiency and safety of the truck-mounted crane in a narrow space are effectively improved.

The invention relates to a multi-sensor fusion positioning method suitable for a multi-axle steering vehicle, and the method comprises the following steps: S1, installing a plurality of sensors on a multi-axle all-wheel steering vehicle platform, collecting IMU data, a laser radar real-time scanning point cloud, a GNSS signal, the reading of each wheel angle sensor, and the reading of each wheel speed meter, and carrying out the real-time scanning of the IMU data, the laser radar real-time scanning point cloud, the GNSS signal, the reading of each wheel angle sensor, and the reading of each wheel speed meter; and the timestamps of all the collected signals are aligned. And S2, establishing a kinematic model of a to-be-estimated state, and performing distortion removal on the laser radar point cloud data according to the IMU data to realize motion compensation of the point cloud. And S3, establishing an error state discrete model, a laser radar observation model, a multi-axis steering vehicle kinematics observation model and a GNSS observation model. And S4, considering the influence caused by multi-wheel non-Ackerman steering, and providing a method for adaptively adjusting the observation noise covariance of the multi-axle steering vehicle so as to ensure the correct weighting of chassis observation data. And S5, fusing IMU, GNSS, laser radar, and rotation angle and rotation speed observation information of each wheel of the multi-axis steering vehicle, constructing a sequential iterative error state Kalman filter, and obtaining an optimal positioning estimation result through iterative calculation.

The invention discloses a seven-degree-of-freedom mechanical arm inverse kinematics solving method, system and device based on numerical iteration. The method comprises the steps that S1, a D-H kinematics model of a mechanical arm is established based on a joint configuration of the seven-degree-of-freedom mechanical arm; s2, calculating a Jacobian matrix according to the joint angle sequence; s3, introducing a weight matrix to suppress the joint speed at the edge of the joint range; s4, introducing a damping factor, and calculating to obtain an optimized Jacobian pseudo-inverse matrix; s5, the pose error between the expected tail end pose and the current tail end pose of the tail end actuator of the mechanical arm is calculated; s6, converting the pose error into a joint angle adjustment amount by using the optimized Jacobian pseudo-inverse matrix to obtain a joint angle iterative formula; and S7, based on the pose error, the joint angle of each rotating joint of the mechanical arm is determined through a numerical iteration method. According to the method, the problem of unstable solution of a traditional analytical method under the singular configuration can be avoided, and the calculation efficiency and the numerical stability are improved.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

The invention belongs to the technical field of robot control, and discloses a bionic robot control method and system based on a muscle fiber model.The method comprises the steps that a multi-scale muscle model is constructed based on a scale division mechanism, and the multi-scale muscle model is optimized by means of physiological state variables and fatigue accumulation variables to obtain a muscle fiber model; according to the muscle group cooperation relation and the kinematics model of the robot limbs, geometric structure adaptation is carried out on the model in combination with a deformation compensation strategy; on the basis of the fused multi-sensor data, a muscle fiber model and a model after geometric structure adaptation are utilized to construct a feedback adjustment mechanism; and on the basis of an adaptive neural network algorithm, the muscle fiber model, the model after geometric structure adaptation and a feedback adjustment mechanism are optimized, solving is carried out in combination with a multi-objective optimization algorithm, and command information of the limbs of the bionic robot is obtained and executed. Accurate control over the limb joint position, the torque and the impedance of the bionic robot is achieved.

The invention relates to the technical field of numerical control machine tool error compensation, in particular to a space geometric error iterative compensation method and system of a five-axis numerical control machine tool. The method comprises the following steps: constructing a tool kinematic chain topology model and a workpiece kinematic chain topology model of the five-axis numerical control machine tool, and establishing a theoretical forward kinematic model and an actual forward kinematic model of a tool relative to a workpiece; measuring and identifying the geometric error of each motion axis of the five-axis numerical control machine tool; separating a cutter shaft direction vector part and a cutter point position part during iteration; calculating the pose deviation of the cutter according to the cutter shaft direction vector and the correction model of the cutter point position; and continuously iterating to generate a correction operation position of each axis, and realizing error compensation of the tool space pose of the five-axis linkage numerical control machine tool. According to the method, the actual space pose of the cutter is directly corrected through the iterative algorithm in the offline state, so that the actual space pose continuously approaches the theoretical pose, and the error compensation precision and efficiency are improved.

The invention provides an intelligent inspection equipment control method for construction quality evaluation, and relates to the technical field of construction quality detection, and the method comprises the following steps: constructing a virtual twin model of a construction site, and combining an inspection kinematics model of a motion mechanism of inspection equipment based on the virtual twin model to obtain an inspection kinematics model; forming a three-dimensional navigation topological graph containing detection target priorities, obstacle avoidance weights, equipment reachable pose nodes and path curvature constraints; taking the three-dimensional navigation topological graph as input, performing multi-step state prediction and accumulative reward evaluation in a virtual space by adopting a double-estimation reinforcement learning algorithm, and generating a multi-step global motion control strategy; carrying out feasibility verification on the control strategy, and if the verification is passed, issuing a multi-step global motion control strategy to an inspection equipment execution mechanism; the inspection equipment is controlled to conduct three-dimensional laser scanning on the target component according to the verified path, high-density point cloud data are obtained, the control precision of the inspection equipment is improved, and the inspection efficiency is improved.

The invention discloses an intelligent ship autonomous collision avoidance method based on COLREGs and a DDPG algorithm, and relates to the technical field of an intelligent ship technology and an autonomous collision avoidance algorithm, and the method comprises the steps: building an intelligent ship kinematics model based on the motion parameters of a ship in a north-east coordinate system; aIS, radar and visual information are fused, COLREGs rule constraints are embedded, an environment model is constructed, and a ship size safety threshold value is calculated. According to the method, COLREGs and a DDPG algorithm are combined, a high-precision environment model is constructed by utilizing multi-source sensing data, and an algorithm structure is optimized aiming at a typical collision avoidance scene, so that the intelligent ship can automatically identify the meeting situation type, determine the way-giving responsibility or the direct navigation obligation and generate an optimal collision avoidance path in a complex marine environment; compared with a traditional collision avoidance method depending on manual driving and fixed rules, the autonomous collision avoidance capability of the ship in dynamic and complex ocean traffic scenes is remarkably improved, and the collision risk caused by human factors is reduced.

The invention relates to the technical field of flight simulation control and precision measurement, provides a precise positioning method, system and device for a three-dimensional adjustable connecting piece of a flight simulator and a storage medium, and solves the problems of flight simulation control and precision measurement. The method comprises the following steps: acquiring the real-time stroke length of a motion control rod, rotation angle change data and a connecting piece vibration image, outputting a displacement prediction parameter through a platform kinematics model, and generating three-dimensional point cloud data; performing sub-pixel identification and distortion compensation on the image through a mark point identification algorithm to generate a three-dimensional visual coordinate; fusing the point cloud and the visual data, and processing through a Kalman filter to obtain a space pose offset; finally, a displacement compensation instruction is generated, the pose of the connecting piece is adjusted through a closed-loop controller, and accurate positioning of the connecting piece relative to a flight target reference coordinate system is achieved. According to the invention, the accuracy and stability of the pose control of the connecting piece in the dynamic vibration environment are improved.

The invention discloses a robust visual constraint control method and system for a mechanical arm and computer equipment, and the method comprises the steps: building an overall dynamic model of a visual servo system through combining a mechanical arm dynamic model, a visual servo kinematics model and a hand-eye calibration relation between a camera and the mechanical arm; in combination with a visual servo kinematic model, by designing a time-varying asymmetric control barrier function, a constraint satisfaction condition for constraining a visual servo characteristic error within a preset range is obtained, and an optimal visual servo speed controller is designed, and in combination with a robot dynamical model, an optimal visual servo speed controller is designed. A constraint satisfaction condition for constraining joint angular velocity within a preset range is obtained through a time-varying asymmetric control obstacle function, an optimal torque controller is designed in combination with a visual servo characteristic error constraint satisfaction condition, a torque control signal meeting performance constraint is obtained in combination with quadratic programming, and the robot is driven to reach an expected characteristic position. The success rate of a visual servo task is improved, and high-precision visual guide assembly work of the mechanical arm is achieved.

The invention relates to an aircraft-trailer system coordination control method fused with reinforcement learning, and the method comprises the steps: constructing an aircraft-trailer system mathematical model, and determining the coupling relation between control variables and state variables, the control variables including trailer movement control variables and system steering control variables; aiming at trailer movement control variables, designing a model-based self-adaptive controller; aiming at the steering control variable of the system, designing an attitude compensation controller based on reinforcement learning; the self-adaptive controller and the attitude compensation controller are fused, corresponding control signals are output to the aircraft and the trailer, and motion control over the aircraft and the trailer is completed. Compared with the prior art, the self-adaptive control law based on the virtual variable is designed through the decoupling kinematics model; and aiming at system parameters and external uncertainty, designing a compensation method based on reinforcement learning. A series of simulation is carried out under different conditions, and results show that the method can ensure coordinated movement of the aircraft and the trailer under different conditions.

The invention discloses a missile avoidance and rotary enemy locking maneuvering strategy generation method, which comprises the following steps: S1, establishing a kinematics model and a dynamics model of an airplane and a missile based on Newton's law of motion, and simulating flight tracks and states of the airplane and the missile; s2, based on the kinematics model and the dynamics model, establishing a 1v1 beyond-visual-range air combat environment; s3, designing a state space, an action space and a reward function of deep reinforcement learning for the quadruple of deep reinforcement learning, and integrating the three into an over-the-horizon air combat environment to complete the design of a state transfer function; s4, simulating a warplane by using an intelligent agent, and performing repeated simulation confrontation by using the deep reinforcement learning framework to optimize and adjust a strategy and improve the survivability and combat efficiency in an air combat; and through continuous iterative training of the reinforcement learning model, the intelligent agent autonomously learns an effective maneuvering strategy so as to cope with threats of enemy planes and missiles from different directions.

The invention discloses a cable underwater robot trajectory tracking control method for coping with ocean current disturbance, and belongs to the technical field of underwater robot trajectory tracking, and the method comprises the following steps: S1, constructing a kinematics and dynamics model of a cable underwater robot, and simplifying the kinematics model of the cable underwater robot; s2, combining speed information of a Doppler velocimeter sensor, designing a super-spiral expansion state observer, and compensating a cable underwater robot system; designing a self-adaptive super-spiral expansion state observer by combining a self-adaptive law; and S3, designing a self-adaptive super-spiral integral terminal sliding mode controller based on the self-adaptive super-spiral expansion state observer. The real-time dynamic performance and stability of the underwater robot in sliding mode control are improved, the buffeting problem existing in sliding mode control is solved, it is ensured that under the condition of ocean current disturbance, the ROV can still achieve accurate trajectory tracking, and the robustness is very high.

A method for operating an adjustment system for an interior of a motor vehicle, wherein the adjustment system has motor-adjustable interior elements, which are adjustable between different configurations by respective drive arrangements with actuators via adjustment kinematics, wherein a control arrangement is provided, by which the drive arrangements are activated in an adjustment routine in order to adjust the motor-adjustable interior elements from an initial configuration into an end configuration via the adjustment kinematics. The control arrangement has an obstacle representation of objects in the interior for collision testing during the adjustment. A path planning routine is carried out by the control arrangement, in which a collision-free adjustment path from the initial configuration into the end configuration is determined on the basis of a kinematics model of the adjustment kinematics and of the obstacle representation, and that the activation in the adjustment routine is carried out by the control arrangement.

A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.

The invention discloses a multi-vertical fixed-wing unmanned aerial vehicle flight path planning and intelligent obstacle avoidance method, and relates to the technical field of unmanned aerial vehicle flight path planning. Comprising the following steps: according to the dynamic characteristics of the multi-vertical fixed-wing unmanned aerial vehicle in a vertical take-off and landing mode and a horizontal flight mode, constructing a unified kinematics model and initializing unmanned aerial vehicle parameters and an environment model; carrying out global pre-flight path planning by adopting an improved particle swarm optimization algorithm, and generating a global flight path of the multi-vertical fixed-wing unmanned aerial vehicle according with flight characteristics; in the actual flight process, in combination with a deep reinforcement learning DDPG and an improved interference fluid power system IIFDS algorithm, flight modes of the unmanned aerial vehicle are intelligently switched, and multiple dynamic obstacles are efficiently and automatically avoided; and after obstacle avoidance is completed, the unmanned aerial vehicle rapidly restores the fixed-wing flight mode and flies along the preset route again. According to the method, the problem of multi-vertical fixed-wing unmanned aerial vehicle cooperative flight path planning in multiple complex scenes can be solved, and the purpose that the cost of task execution of multiple unmanned aerial vehicles is minimum is achieved.

The invention provides a mechanical arm time optimal trajectory planning method based on an improved genetic particle swarm algorithm, and belongs to the field of robot control. A mechanical arm kinematic model is established, the mapping relation between a joint space and a Cartesian space of an end effector is derived, and a joint angle sequence corresponding to an end path point is calculated; under the target of time optimization, joint speed and acceleration constraints are combined, a fitness function is constructed, and an improved genetic particle swarm optimization algorithm is used for optimization; a 3-5-3 segmented interpolation method is adopted, and based on the joint angle sequence, joint position, speed and acceleration trajectories are generated; the inertia weight is dynamically adjusted in the optimization process, and the search efficiency is improved; the motion time of each section of track is further optimized, the convergence speed and precision are improved through a hybrid optimization algorithm in combination with a time optimal target and joint motion constraints, finally, the mechanical arm motion track with the optimal time is output, the time for the mechanical arm to complete a target task is effectively shortened, the working efficiency is remarkably improved, and high precision and stability are achieved.

The invention relates to the technical field of unmanned ship trajectory tracking, in particular to an unmanned ship trajectory tracking control method based on self-adaptive dynamic event triggering, and establishes a kinematic model, an expected trajectory and a position error dynamic equation of an unmanned ship. A dynamic event triggering mechanism and self-adaptive dynamic planning are combined, a cost function containing a dynamic event triggering control law and a Hamilton function are given, and resource consumption of an unmanned ship controller is reduced. And an unknown cost function and an optimal control law are approximately obtained by using the good nonlinear approximation capability of the neural network. And applying the calculated optimal control law to a control system of the unmanned ship, and driving the unmanned ship to perform accurate tracking according to a predetermined trajectory. The method provided by the invention can effectively improve the precision of trajectory tracking of the unmanned ship, and saves system calculation and communication resources.

The invention discloses an intelligent cavity operation navigation method and system based on multi-mode perception fusion, and relates to the technical field of surgical medical treatment. Firstly, bionic instrument posture data and cavity environment data are obtained; then, based on the bionic instrument posture data and the orifice environment data, the target curvature of each section is calculated through a bionic kinematics model, and the magnetorheological fluid pressure is dynamically adjusted to control the local rigidity; meanwhile, the cavity point cloud topological graph is converted into an obstacle density graph, and path weight matrixes of different paths are quantified based on a swarm intelligence algorithm; and finally, according to the target curvature, the local rigidity and the path weight matrix, driving a bionic instrument to execute compliant motion and cooperative obstacle avoidance operation. According to the method, multi-modal data (curvature, rigidity and contact force) are fused through a bionic kinematics model, navigation deviation correction is achieved in combination with a curvature-force nonlinear mapping model and three-dimensional Euclidean distance monitoring, and the navigation precision is improved in combination with a path re-planning mechanism.

The invention discloses a flexible surgical instrument-oriented multi-source sensing data adaptive fusion method and a related device. The method comprises the following steps: acquiring optical, mechanical and pose multi-mode sensing data based on a timestamp synchronization technology in a working environment; clock compensation and space coordinate transformation based on an instrument kinematics model are carried out on the multi-modal data, and space-time registration is completed; inputting the registered data into a neural radiation field model to generate continuous space implicit scene representation; inputting the implicit representation into a pre-trained RWKV fusion network to obtain a fusion feature; and superposing the fusion feature and the original low-frequency component through residual connection, and integrating to generate three-dimensional voxelization environment state data. According to the method, multi-source information high-fidelity fusion is achieved through a single path, data redundancy is remarkably reduced, and the real-time performance and precision of intra-operative environment perception are improved.

The invention discloses a dynamic trajectory control method for an SCARA (selective compliance assembly robot arm) robot. According to the kinematics model, a homogeneous transformation matrix is used for analyzing the coordinate transformation relation between each joint and the connecting rod, and an initial pose and a target pose are determined; establishing a kinetic model based on a Lagrange equation, and determining kinetic parameters needing to be identified; performing parameter identification by adopting a random weight particle swarm optimization algorithm; according to an identification result, a sliding mode robust item RBF neural network adaptive controller is constructed, real-time track control is achieved, and the end manipulator is made to move to a target pose along a preset dynamic track; a real-time pose is detected through a sensor and compared with a target pose, and arrival is judged when the error is smaller than a threshold value. Model precision and identification efficiency are improved through modeling and efficient parameter identification, the composite controller effectively compensates uncertainty, disturbance and nonlinear factors of the model, closed-loop control and online adjustment are achieved, and trajectory tracking precision, system stability and dynamic response speed are improved.

The invention provides a method and a device for redirecting human hand actions to a heterogeneous manipulator in a multi-task scene, and relates to the technical field of robots and intelligent control. The method comprises the following steps: acquiring visual data and tactile data; performing task type classification by adopting a pre-trained Perciver IO model to obtain a task type code; according to a physical constraint dynamic correction mechanism, correcting the joint weight of the machine finger to obtain a normalized finger weight; key point extraction is conducted on the human hand, and vectors between all joints of the human hand are calculated through a geometric vector method; performing driven joint constraint processing according to the established joint mapping matrix, calculating an adapted Jacobian matrix, and obtaining an adapted standardized robot arm kinematics model; and carrying out nonlinear optimization on the vectors between the human fingers and between the robot fingers to obtain each joint angle of the robot arm, so that the robot arm completes action redirection. By adopting the method, the generalization ability of cross-task and heterogeneous models can be improved.

The invention discloses an underwater vehicle tracking control method with state constraint under input saturation. The method comprises the following steps: firstly, constructing an AUV control system containing model uncertainty and unknown interference based on a kinematics model and a dynamics model of the AUV, and obtaining a reference trajectory of a tracking target; establishing an error model of the AUV control system in combination with the error conversion function, and obtaining a design variable related to the position tracking error; meanwhile, deducing a design variable related to a speed tracking error; constructing a fixed time integral sliding mode lumped disturbance observer based on a design variable related to a position tracking error; and finally, combining a fixed time integral sliding mode lumped disturbance observer and design variables related to a speed tracking error to construct a trajectory tracking control system, and designing a fixed time convergence sliding mode controller by using a continuous input signal and a position and speed adaptive law. According to the invention, the tracking error is stabilized to zero in a fixed time; and continuous control input is realized.