748 results about "Kinematical model" patented technology

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 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 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.

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 relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

The invention relates to the field of robot vision positioning, and discloses an intelligent hand-eye calibration and self-adaptive correction system and method. According to the method, a mechanical arm is controlled to drive a camera to collect multi-modal calibration data, a convolutional neural network is utilized to identify a calibration plate mark point, and a three-dimensional coordinate under a camera coordinate system is calculated in combination with depth information; meanwhile, on the basis of an encoder and torque data, flexible deformation of the mechanical arm is compensated through a kinematic model and a self-adaptive rigidity model, and three-dimensional coordinates under a base coordinate system are obtained. And obtaining a hand-eye transformation matrix by solving a transformation relation between the two point sets. And repeatedly calibrating before and after operation, inputting the difference of the two transformation matrixes into the fault diagnosis neural network, outputting fault type probability distribution, and generating a maintenance strategy. According to the invention, high-precision hand-eye calibration, automatic compensation of flexible deformation and intelligent diagnosis of system state change can be realized, so that the long-term precision and reliability of a visual positioning system are improved.

The invention discloses an intelligent exhibition hall control method and system based on digital twinning, and relates to the field of exhibition hall control, and the method comprises the steps: carrying out the kinematics state calculation of a continuous position flow of a visitor, and precisely mastering the current motion state of the visitor; the key point is that the method is not limited to the current state, but carries out high-precision prediction on the future (moment) movement track of the visitor based on a kinematic model. And along the prediction track, a personalized dynamic environment bubble can be generated in advance in combination with the portrait of the visitor, and the environment atmosphere of the area where the visitor is about to enter is actively and progressively pre-rendered. Finally, the environmental bubbles are reversely analyzed into specific equipment control flows, so that pre-smooth adjustment of lamplight, sound effect and other equipment is realized, and the technical problems of abrupt control and poor experience caused by perception lag and lack of predictive ability of a traditional control scheme are solved.

The invention relates to the technical field of unmanned aerial vehicle monitoring and trajectory processing, and discloses a low-altitude unmanned aerial vehicle dynamic trajectory tracking and predicting method based on multi-base-station cooperation. The method comprises the steps that signal parameters are obtained through multi-base-station collaborative observation, and a multi-modal position prediction set is generated; and performing classification and scoring according to the spatial distribution characteristics of the candidate points and the historical track points, and screening out an optimal prediction position point. And inputting the optimal prediction point and the historical trajectory into a generative model, and dynamically adjusting the number of trajectory points by analyzing the point distribution probability to form a preliminary smooth trajectory. And performing physical feasibility verification and fine adjustment on the trajectory according to kinematics constraints to obtain a final smooth continuous trajectory. And matching degree calculation is carried out by fusing alternative trajectories generated by a multi-kinematic model, and the most probable target trajectory is identified. According to the method, the robustness of trajectory prediction and the structural rationality of the generated trajectory in a complex observation environment are improved.

The invention provides an unmanned tractor path planning method based on an improved PSO fusion DWA optimization algorithm. The method comprises the steps that a grid map is initialized; forming a preliminary global path by using an improved PSO algorithm; performing local refined optimization on the initial global path through single-dimensional neighborhood disturbance and greedy selection; discrete path serialization, redundant node elimination and smoothing processing are carried out on the initial global path through a greedy optimization method; a path point sequence of a final optimized global path is completely reserved as a local target point of a DWA algorithm, local path planning and real-time obstacle avoidance are guided, speed sampling and trajectory prediction are performed based on a kinematic model, speed constraint and the local target point, and an optimal prediction trajectory and speed space are selected through an improved evaluation function in the DWA algorithm. The method effectively solves the problems that an existing planning method is poor in smoothness, long in planning time, multiple in inflection points, discontinuous in path curvature and incapable of achieving global optimum.

The invention discloses a dynamic modeling tracking control method for a self-adaptive fiber placement compaction mechanism, and belongs to the technical field of specific model calculation systems, and the method comprises the following steps: S1, building a kinematic model of the self-adaptive fiber placement compaction mechanism; s2, establishing a workpiece surface model, and solving tangent point coordinates and position coordinates of the driving wheel, the driven wheel and the ground; s3, according to the force balance equation and the moment balance equation of each rod, the relation between each bearing reaction and the external force is solved; s4, establishing a dynamic model of the driving device; and S5, error analysis and optimization are carried out, and it is ensured that the main pressure is within the fluctuation range. According to the device, it can be ensured that the rollers output constant compaction force when the abrupt change molded surface structure is laid, and the forming defect caused by uneven compaction force is avoided.

The invention discloses a visual servo method for a transformer substation drainage wire disconnecting and connecting robot, and the method comprises the following steps: building a mapping relation between a mechanical arm tail end camera and a mechanical arm kinematic model, and building an image-based visual servo system IBVS; acquiring a drainage wire clamp image in real time, and extracting image feature points of a drainage wire clamp bolt by using the recognition model; the model prediction controller carries out rolling optimization based on the deviation between the extracted current image features and the expected image features, estimates and compensates external disturbance and unmodeled dynamics borne by the system in real time, and calculates an optimal, smooth and safe mechanical arm motion instruction; and the mechanical arm movement instruction is sent to a mechanical arm movement control unit to drive the mechanical arm to move. According to the method, the pose control of the drainage wire disconnecting robot can be realized, the error convergence in the visual servo process is rapid, the steady-state error is small, the dynamic response is timely, and the requirements of the drainage wire disconnecting operation on the precision, the stability and the real-time performance are fully met.

The invention discloses an unmanned ship control method based on deep reinforcement learning in a multi-task scene. The method comprises the following steps: constructing dynamics and kinematics models of an unmanned ship; the method comprises the following steps: constructing a high-fidelity simulation environment based on Isaac Sim and a parallel training framework thereof, and respectively designing a state space and an action space for various unmanned ship tasks; reward functions are respectively designed; constructing an unmanned ship control strategy, and designing an algorithm framework of a centralized importance sampling and shearing strategy optimization mechanism based on an end-to-end deep reinforcement learning algorithm; and for different task scenes, multiple times of training-verification are performed on the strategy network through a PPO algorithm, and the trained strategy network is used for realizing multiple control tasks of the unmanned ship. According to the method, a high-fidelity simulation environment is constructed through Isaac Sim and a parallel training framework thereof, parallelization support which is crucial to deep reinforcement learning training is achieved, and various actual control tasks of the unmanned ship can be achieved.

The invention provides a front and rear axle load estimation method and system and a steer-by-wire vehicle, and the method comprises the steps: (1) when the vehicle is in a longitudinal stable and straight driving state, updating the mass of the whole vehicle; (2) establishing a least square recursion equation with a forgetting factor based on the dynamic motion equation of the vehicle on the ramp, and calculating the gradient of the current ramp; and (3) calculating a front axle load and a rear axle load based on the whole vehicle mass and the current gradient. The whole vehicle mass estimation is started under the working conditions of longitudinal stability and straight driving, so that the reliability and the stability of the whole vehicle mass calculation process are ensured; besides, vehicle dynamics and kinematics models are fused, recursive calculation is performed by applying a least square method with a forgetting factor, the road gradient can be estimated in real time at high frequency and high precision under various driving working conditions of the vehicle, and the limitation of a traditional fixed parameter model is broken through.

The invention discloses a layered intelligent trajectory tracking control method and system for an under-actuated vehicle, and belongs to the technical field of intelligent control. In order to solve the problems that in the prior art, dependency on a model is high, a reward mechanism is rigid and perspective lacks, a double-loop framework combining outer-loop model predictive control (MPC) and inner-loop near-end strategy optimization (PPO) is adopted. The outer ring MPC generates a feasible speed reference instruction and a future prediction sequence based on a kinematic model; the inner ring PPO learns a non-linear inverse mapping of speed to thrust to compensate for uncertainty. The core innovation lies in that a look-ahead-response adaptive reward (PRAR) mechanism is designed, and the mechanism comprehensively utilizes real-time pose errors and a future prediction sequence of MPC to dynamically adjust the weight of a reward function, so that the controller has both the real-time error response capability and the future task complexity prediction capability. According to the invention, the trajectory tracking precision, robustness, learning efficiency and generalization ability of the under-actuated vehicle under complex disturbance are significantly improved.

The invention belongs to the technical field of roadway navigation, and particularly relates to an integrated navigation method and device based on inertial navigation and laser radar and a medium, and the method comprises the following steps: scanning by using the laser radar to obtain point cloud data, and calculating the position of a carrier based on the point cloud data; establishing a kinematic model of the mobile carrier, and calculating a distortion error of the mobile carrier; measuring motion information of the mobile carrier by adopting inertial navigation; and compensating the point cloud data based on the motion information of the mobile carrier, and completing navigation calculation based on the compensated point cloud data. The point cloud data acquired by the laser radar are compensated through the inertial navigation data, point cloud distortion is compensated in real time, and the geometric quality and the positioning precision of the point cloud are improved.

The invention relates to a vehicle obstacle avoidance trajectory planning method which comprises the following steps: in response to a trigger condition, planning an initial trajectory of a vehicle from a current pose to a target pose based on a vehicle kinematic model; the planning process of the initial trajectory comprises collision detection based on environment obstacle information sensed in real time, and the environment obstacle information comprises static obstacles and dynamic obstacles; and by taking the initial trajectory as an optimization initial value, performing trajectory optimization based on a preset constraint condition to obtain an optimized trajectory. Forward movement and backward movement generated by positive acceleration control and negative acceleration control are considered at the same time in the trajectory planning process, so that candidate movement strategies of the vehicle are multiplied, the vehicle can handle forward and lateral obstacles and can effectively cope with dangerous scenes such as rapid approaching of rear vehicles, and the vehicle driving safety is improved. The limitation that only forward obstacles are processed in the prior art is overcome, and the application scene and safety of the automatic driving function are improved.

The invention relates to a satellite-borne microwave radiometer cold air calibration attitude planning method based on multi-objective optimization, and belongs to the field of satellite microwave remote sensing, and the method comprises the steps: building a satellite attitude kinematics model and an attitude dynamics model through a geocentric equator inertial coordinate system, an orbital coordinate system and a satellite body coordinate system; establishing a brightness temperature sum model received by the microwave radiometer in the cold air calibration process; according to the method, a microwave radiometer receiving brightness temperature sum, brightness temperature fluctuation, platform maneuvering capability, energy and a satellite sensor field of view are comprehensively considered, a multi-target optimization model of microwave radiometer cold air calibration consideration constraints is established, solving is performed, constraint conditions are established, and a solution or a solution set is obtained. According to the method, the brightness temperature sum and the brightness temperature fluctuation in the observation area of the microwave radiometer in the cold air calibration process are optimized by adopting multi-objective optimization, and the on-orbit cold air calibration effect and the system reliability of the satellite-borne microwave radiometer are improved.

The invention discloses a land leveler automatic control and guidance method and system based on Beidou high-precision positioning. The method comprises the following steps: acquiring comprehensive state information of a land leveler including a three-dimensional position, a course, an attitude and a real-time elevation of a scraper knife; outputting future state parameters of a scraper knife elevation adjusting mechanism and a machine body steering mechanism of the land leveler in the rolling time domain through a preset land leveler kinematics model on the basis of the comprehensive state information and the current target working plane design information; based on the future state parameters, determining an optimal pre-control instruction sequence for a scraper knife elevation adjusting mechanism and a fuselage steering mechanism through a multi-objective optimization mode; and the optimal pre-control instruction sequence is sent to an electro-hydraulic control system to drive an execution mechanism to act. Complex factors such as topographic changes and fuselage dynamics coupling can be effectively coped with by predicting future states by adopting a kinematic model and prospectively generating a coordination control instruction, and the accuracy and smoothness of scraper knife control in the dynamic operation process are ensured.

The invention relates to the technical field of pipeline robots, and discloses a probing robot for automatically recognizing defects of a drainage pipe culvert under the water operation condition. The movement mechanisms cling to the inner wall of the pipeline and are used for detecting the movement of the robot; the rotating mechanism is rotationally mounted on the peripheral side of the robot body; the pressure mechanisms are fixedly mounted on the outer wall of the rotating mechanism, attached to the inner wall of the pipeline and used for collecting pressure value related electric signals; the detection system is mounted in the robot body; the detection system comprises a signal processor module, a wireless transmission equipment module and an infrared camera module; the pressure mechanism, the rotating mechanism and the detection system are matched to realize accurate positioning of defect points on the inner wall of the pipeline. Through combination of mechanical contact type detection and kinematic model positioning, high-precision spatial positioning and identification of the inner wall of the pipeline are realized in a high-turbidity and full-pipe water-carrying environment, and data support is provided for subsequent pipeline maintenance and repair.

The invention belongs to the technical field of robot safety control, and discloses a safe motion planning method for a dual-redundancy mechanical arm. And a linear mapping relation between the joint space speed of the redundant mechanical arm and the speed of an end effector is established. And establishing an end effector speed constraint, a safety distance constraint and a joint amplitude limiting constraint, and fusing a differential kinematics model and the three constraints to establish a multi-target combined constraint model. The optimal decision variable meets the Carlo demand-Kuhn-Tuck condition, the original expression form of the Carlo demand-Kuhn-Tuck condition is defined, and a nonlinear complementary function is introduced to reconstruct the Carlo demand-Kuhn-Tuck condition. Based on an improved Carlo demand-Kuhn-Tuck condition, a vector error function is defined, and an adaptive return-to-zero neural network is designed; and performing iterative solution on the vector error function through an adaptive return-to-zero neural network to obtain an optimal decision variable of the multi-target joint constraint model, taking the optimal decision variable as a mechanical arm joint motion control instruction, and outputting the mechanical arm joint motion control instruction to complete mechanical arm motion planning.

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 discloses a ground mobile robot path planning method and system, and relates to the technical field of intelligent robot autonomous control. The method comprises the following steps: constructing a grid map model of a robot moving environment; based on the grid map model, establishing a kinematic model of the robot and setting a movement rule, and initializing to generate a plurality of paths from a starting point to an ending point to form an initial path set; iteratively merging and optimizing the paths in the initial path set by adopting a group merging strategy, and screening out a current global optimal path; based on the current global optimal path guidance, a derivative path is updated based on an improved pigeon inspired optimization map and a compass operator, and a first-stage optimization set is obtained; and through iterative optimization of a landmark operator, dynamically calculating a landmark center according to a cost value and updating a path position to obtain a second-stage optimization set. And selecting an optimal path and carrying out connection smoothing processing to generate an optimal complete path. According to the method, the global optimal path can be efficiently and stably planned in a complex environment.

The invention provides a path planning method and system for a robot in an unknown dynamic environment, and the method comprises the steps: obtaining a first position, a second position and environment information of the robot on a map, and constructing a structured current state vector based on the first position and the second position; generating a first control action vector from the current state vector to a pre-trained deep reinforcement learning network; the current state vector is input into a model prediction control module, rolling optimization is carried out in a prediction time domain based on a kinematics model of the robot and obstacle information, and a second control action vector is generated; according to a preset weight determination rule, calculating an adaptive fusion weight, and based on the fusion weight, performing weighted fusion on the first control action vector and the second control action vector to generate a final hybrid control action vector; and controlling the robot to execute the hybrid control action vector. According to the method, the navigation safety and task success rate of the robot in an unknown dynamic environment are remarkably improved.

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.

The invention provides an unmanned surface ship obstacle avoidance navigation path generation and tracking method and device. The method provided by the invention comprises the following steps: constructing a ship kinematics model according to navigation motion parameters of an unmanned surface ship; recognizing obstacles on the initial navigation path, and constructing a dangerous area; calculating a geometrical relationship between the initial navigation path and the dangerous area based on a ship kinematics model, and generating an obstacle avoidance navigation path; adjusting the obstacle avoidance navigation path based on the interaction relationship between the obstacle avoidance navigation path and the plurality of dangerous areas; detecting the curvature of the obstacle avoidance navigation path at the access port, and generating a tangent circle optimization path tangent to the boundary of the dangerous area at the access port based on the curvature to obtain an obstacle avoidance navigation optimization path; on the basis of a finite time theory and a ship kinematics model, an expected navigation heading angle is generated according to an obstacle avoidance navigation optimization path; and constructing a navigation tracking adjustment module, and driving the unmanned surface ship to track the obstacle avoidance navigation optimization path based on a navigation tracking instruction output by the ship kinematics model.

An intelligent testing method for autonomous driving based on interaction of multiple background traffic participants, which includes the following steps: obtaining a test scenario and path information of traffic interactors in the test scenario, wherein the traffic interactors include a vehicle under test and background traffic participants, and the background traffic participants include background vehicles and background pedestrians; constructing a kinematic model of the background traffic participants based on the path information; dynamically constructing safety constraints between any two traffic interactors based on the path information; controlling the background traffic participants to conflict with the vehicle under test based on the kinematic model and the safety constraint, and constructing model prediction control to obtain a reference speed of the background traffic participants under collaborative interaction; and collecting real driving data of the vehicle under test and the background traffic participants in real time.

The invention discloses a factor graph optimization method for aircraft cluster positioning and navigation, which comprises the following steps: for each aircraft in an aircraft cluster, establishing a corresponding local factor graph based on a kinematic model and observation data of the aircraft; constructing a local optimization objective function of each aircraft based on the local factor graph; the method comprises the following steps: solving a local optimization objective function of each aircraft based on a distributed optimization algorithm through interaction and cooperation information among aircrafts in an aircraft cluster, and enabling position estimation of each aircraft to be converged to a global consistent solution; according to the method, the factor graph is constructed for the aircrafts in the cluster, the optimization problem is disassembled and distributed to each aircraft by introducing the idea of distributed calculation, the position of the cluster is solved through mutual communication cooperation among the aircrafts, the positioning precision is optimized, the problems of low calculation efficiency, insufficient calculation resources and the like existing in traditional centralized optimization are solved, and the method is suitable for large-scale popularization and application. And the robustness of the system under the conditions of communication limitation and satellite observation data deviation is enhanced.

The invention relates to a quadrotor unmanned aerial vehicle control method based on priori knowledge and a neural network model, and the method comprises the steps: introducing flight interference and uncertainty based on a first principle, and constructing a quadrotor kinematics equation; parameterizing the flight uncertainty by using a pre-trained neural network model, and constructing a four-rotor kinematics model; constructing a disturbance observer based on a pre-trained neural network model, predicting the interference of the flight, and obtaining a disturbance compensation amount; based on the four-rotor kinematic model and a disturbance observer, generating an optimal control quantity of the four-rotor unmanned aerial vehicle by solving a numerical optimization problem in each control period of the four-rotor unmanned aerial vehicle; and superposing the disturbance compensation quantity and the optimal control quantity to obtain control input of the four-rotor unmanned aerial vehicle. Compared with the prior art, the method solves the problem that the trajectory tracking precision of the quad-rotor unmanned aerial vehicle is insufficient due to model uncertainty and external disturbance.

The invention belongs to the field of unmanned aerial vehicle collaborative search, and particularly discloses an unmanned aerial vehicle water surface target collaborative search method, which comprises the steps of 1, constructing a multi-dimensional dynamic environment model, including dynamic environment modeling based on dynamic environment parameters; step 2, constructing a heterogeneous platform kinematic model based on the multi-dimensional dynamic environment model constructed in the step 1; step 3, constructing a distributed cooperative task allocation strategy based on the heterogeneous platform kinematic model constructed in the step 2; 4, predicting a target track by adopting an extended Kalman filtering algorithm to obtain optimal state estimation; and step 5, based on the optimal state estimation obtained in the step 4, performing collision-free path planning by adopting a TangentBug real-time obstacle avoidance algorithm. By adopting the scheme provided by the invention, the problems of difficulty in adapting to a dynamic environment, accurately predicting a target trajectory, robustly allocating tasks and safely avoiding obstacles in a complex ocean scene in the prior art can be solved.

The invention belongs to the technical field of robot intelligent control, provides a robot double-source joint information impedance control method and system, a medium and a robot, and aims at motion control of a 3R-configuration robot. Joint motion information including a filtering angle, a filtering angular velocity and a filtering angular acceleration is obtained, the pose and / or velocity information of the end effector is mapped to a joint space based on a kinematic model of the robot, and joint motion information including a mapping angle and a mapping angular velocity is obtained; fusing the angle and the angular velocity to obtain a fused angle and a fused angular velocity; and generating a control torque by combining the fusion angle, the fusion angular velocity and the filtering angular acceleration with a preset target impedance parameter and an expected trajectory. The angular velocity and the angular acceleration can be obtained without adding velocity and acceleration sensors on the motor, and the increase of the size, the complexity and the cost of the joint control motor is avoided.