927 results about "Kinematical model" patented technology

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 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.

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.

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 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 discloses a mine card simulation method and system based on log data, and relates to unmanned driving. The method comprises the following steps: obtaining operation log data of a mine card under multiple working conditions; extracting nonlinear response feature data according to the running log data; taking the historical control instructions and historical vehicle states of continuous N periods as input characteristics, taking the vehicle state of the current period as an output label, and constructing a training set; using the training set to train an LSTM model, inputting a current control instruction, a historical vehicle state and a current load value, and outputting a predicted vehicle response state; and inputting the predicted vehicle speed, acceleration, front wheel turning angle or hinge angle into a vehicle kinematic model, and calculating the position and attitude of the vehicle under the current load condition. Aiming at the problem that the time delay between a control instruction and an actual response in a super-large inertial system cannot be processed due to the fact that a non-linear dynamic response caused by the fact that mine truck large load change cannot be accurately simulated by adopting fixed dynamic parameters in mine truck simulation, the simulation precision is improved.

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 belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and system with dynamic risk perception and attention focusing and a vehicle, and the method comprises the steps: predicting the track of a surrounding vehicle in real time through a multi-feature Gaussian weighted particle filtering algorithm, and improving the prediction precision through combining a vehicle kinematic model and resampling optimization; constructing a comprehensive evaluation model fusing transverse and longitudinal risks, and dynamically quantifying the collision risk of the vehicle and surrounding vehicles; and inputting the risk value as a key state feature into a double-depth Q network based on attention mechanism enhancement, focusing key information through a feature attention distribution mechanism, and generating an optimal driving decision in combination with a multi-target reward function. Compared with the prior art, the method solves the problems of insufficient quantification of uncertainty factors, incomplete risk assessment and low decision-making efficiency of automatic driving in a complex dynamic environment, and significantly improves the risk perception capability and decision-making safety of the automatic driving vehicle.

A system for collaborative steering in steer-by-wire (SBW) vehicles includes sensors, a rack motor altering a position of the vehicle's steerable road wheels and an emulator altering a torque and position of a vehicle hand wheel. A collaborative steering system application (CSSA) obtains, static and dynamic information about the vehicle and the vehicle's environment, and generates a rack torque and / or angle command to the rack motor and an emulator torque and / or angle command to the emulator. The CSSA adjusts between ADAS and manual steering ratios and automatically transitions control between ADAS SBW control and manual control. The CSSA smooths transitions between ADAS SBW control and manual steering control and adjusts hand wheel stiffness by altering the emulator torque command, and causes the rack motor and emulator to operate according to a kinematic model while adapting road wheel response and vehicle operator steering feel based on scenarios and features currently enabled.

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.

The invention provides a coal face mining auxiliary method based on visual and point cloud data fusion modeling, and the method comprises the steps: collecting the visual image data of a coal face, collecting the three-dimensional point cloud data through a point cloud sensor, and obtaining the motion state information through an inertial sensor; based on internal and external parameters and IMU data of joint calibration, mapping point cloud data to an image plane, establishing a geometric mapping relationship between three-dimensional points and pixels, generating an attribute point cloud model containing geometry, color and semantics, and generating a high-precision global three-dimensional digital twinning model; and identifying and tracking equipment instances in the digital twinborn model, and resolving motion parameters of key components based on a URDF kinematics model and a principal component analysis algorithm to support remote control decisions. According to the invention, high-precision real-time sensing and three-dimensional visualization of the working space size, the equipment connection relation and the movement position of the coal face can be realized, and the remote control precision and the response efficiency of unmanned mining are remarkably improved.

The invention discloses an unmanned agricultural machine path tracking preset performance control method and system, and the method comprises the steps: collecting the pose information of an unmanned agricultural machine, building an unmanned agricultural machine path tracking error kinematic model, and designing a finite time preset performance function based on the unmanned agricultural machine path tracking error kinematic model. The constraint is converted through nonlinear mapping; based on the Lyapunov stability theory, adopting a backstepping method to recursively design an actual control law, and according to the actual control law, performing inverse transformation on the input of the unmanned agricultural machine tracking control system in the error kinematics model of the unmanned agricultural machine tracking control system to obtain the front wheel steering angle of the unmanned agricultural machine. According to the method, convergence is completed within finite time, and transient control performance and steady control performance of a path tracking control system are considered.

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 relates to a physical-data hybrid drive vehicle driving state estimation method, and belongs to the technical field of intelligent vehicle chassis dynamics state estimation. Constructing a mapping relation between the state estimation vector and sensor data by adopting a ridge regression method so as to explicitly express a vehicle movement mechanism; secondly, constructing a data driving model to implicitly extract vehicle sensor data features, and constructing an enhanced LSTM neural network with a double-stage attention mechanism as the data driving model; then, performing dynamic weighted fusion on an output result of the physical model and a sensor sample sequence output by the data driving model to realize deep coupling of physical mechanism knowledge and data driving characteristics; according to the method, the superiority of the method compared with other methods is verified through simulation testing and real vehicle testing.

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 application discloses a low-temperature cooling water cooling air-cooled island cleaning robot path planning method and system, and belongs to the technical field of robot control, comprising: based on the air-cooled island fin layout drawing, initializing the workspace grid, setting the reference parameter; driving the cleaning robot to collect multi-dimensional data, calculating the actual friction coefficient of the measured grid through the main friction coefficient and the torque auxiliary friction coefficient, for the unmeasured grid, linear extrapolation is carried out through the gradient to construct the real-time friction field; obtaining the ideal contact force vector, constructing the kinematics model to obtain the joint required torque, and calculating the joint angle adjustment amount, generating the control instruction and executing through the feedforward friction compensation and PID feedback control, constructing the wear amount estimation model, obtaining the current estimated wear amount, and calculating the core index, generating the rating report, continuously improving the operation efficiency, realizing the unity of the efficiency and equipment safety of the air-cooled island cleaning.

The invention discloses a low-altitude large-speed small-radius maneuvering control method for an unmanned aerial vehicle. The method comprises the steps of data acquisition and preprocessing, establishment of an accurate unmanned aerial vehicle model, design of a model prediction controller, sensor fusion, real-time control and the like. Flight state data is collected and preprocessed through various sensors, dynamics and kinematics models considering aerodynamic nonlinearity are established, a model prediction controller is designed by using a recurrent neural network and a particle swarm optimization algorithm, sensor fusion is performed by using an extended Kalman filtering algorithm, and a model prediction model is established. Accurate control of the attitude and track of the unmanned aerial vehicle is realized. Meanwhile, the functions of environment perception and obstacle avoidance, fault diagnosis and fault-tolerant control, real-time controller parameter adjustment, target optimization and the like are added, the maneuvering performance, adaptability and safety of the unmanned aerial vehicle are improved, and the unmanned aerial vehicle can be widely applied to the fields of military reconnaissance, logistics distribution, film and television shooting and the like.

The invention relates to the technical field of closed-loop drift control, and particularly discloses a closed-loop drift control method and system for an autonomous vehicle in an uncertain environment, and the method comprises the steps: building a vehicle kinematic model based on a Frenet coordinate system, and constructing an MPC path tracking controller with a preview mechanism; establishing a three-degree-of-freedom vehicle dynamics model with additional yawing moment; the method comprises the following steps: establishing a UniTile-Ctrl tire dynamic model; designing a robust drift controller by combining LQR and integral sliding mode control on the basis of the whole vehicle dynamic model and the tire dynamic model, designing a torque distribution module by taking minimization of distribution error and tire load utilization rate as targets on the basis of ideal rear wheel rotating speed and additional yawing moment output by the robust controller, and outputting a torque distribution model; according to the drifting vehicle tracking control method, closed-loop drifting control can be achieved, the drifting vehicle tracking control performance and robustness are improved, and safety guarantee is provided for limit control over the uncertain road surface of the vehicle.

The invention provides an automatic driving method and system and a readable storage medium, and the method comprises the steps: S1, obtaining point cloud data through a laser radar, and outputting the boundary information of an obstacle; s2, acquiring vehicle position information based on a Beidou positioning module; s3, the obstacle boundary information and the vehicle position information are input into a decision-making module, driving behaviors are determined through a preset state machine, and the driving behaviors comprise driving in a lane, lane-borrowing overtaking and decelerating for giving way; s4, track planning is carried out according to the driving behavior; s5, obtaining a track for control based on the smooth path and the planned speed; s6, generating a steering wheel turning angle by adopting an LQR algorithm based on a kinematic model on the basis of a track for control, and generating an acceleration compensation instruction by adopting a double-PID controller in which a position PID and a speed PID are connected in series; and S7, the vehicle executing mechanism controls the vehicle to run through the steering wheel rotation angle and the acceleration compensation instruction.

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 discloses a maneuvering target-oriented trigger type multi-aircraft cooperative guidance algorithm. The algorithm comprises the following steps of establishing a relative kinematics model of multiple aircrafts and a maneuvering target; based on the kinematics model, cooperative guidance laws capable of enabling state variables to converge consistently in fixed time are designed in a sight line tangential channel and a sight line normal channel respectively; an interference observer is arranged to estimate and compensate system interference in the cooperative guidance law; and the aircraft flies according to the cooperative guidance law after the system disturbance is compensated. According to the maneuvering target-oriented triggered multi-aircraft cooperative guidance algorithm disclosed by the invention, the guidance error can obtain a faster convergence speed, and the upper bound of the convergence time of the guidance error is not influenced by initial conditions, so that the communication burden can be effectively reduced, and the cooperative efficiency is 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.