534 results about "Kinematical model" patented technology

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.

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 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 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 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 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 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 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 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 discloses a forward and reverse design and simulation method for virtual excavation of a sunken vertical shaft heading machine. The method comprises the following steps: S1, initializing a terrain; s2, kinematic modeling of the heading machine; s3, track surface generation and virtual excavation; s4, forward design; s5, carrying out reverse design; and S6, outputting data. According to the forward and reverse design and simulation method for virtual excavation of the sinking type vertical shaft heading machine, geometric-level virtual excavation simulation can be realized based on geometric parameters, a kinematic model and stratum data of a VSM; the excavation effect of the preset excavation parameters can be verified through forward design, and a feasible excavation parameter set can be intelligently and reversely solved from the target excavation morphology through reverse design; and meanwhile, a standardized geometric model and structured data which can be directly used for subsequent engineering numerical analysis and construction guidance can be exported, so that the parameter determination precision, the process controllability and the scheme optimization capability before construction are improved.

The invention belongs to the field of hoisting track planning, and particularly discloses a bridge girder erection machine double-hook collaborative hoisting track planning method considering space-time coupling constraint, which comprises the following steps: constructing a kinematic model of a bridge girder erection machine and a three-dimensional obstacle geometric model of a hoisting operation environment, and forming a multi-dimensional configuration space; generating a hoisting operation sequence according to the type attribute of the to-be-hoisted component; in the global path planning stage, a sampling-based global path planning algorithm is adopted to carry out search sampling in a multi-dimensional configuration space, and a double-hook kinematics cooperative constraint detection mechanism is implanted to obtain an initial collision-free path; in the local trajectory optimization stage, a trajectory smoothing model based on quadratic programming is constructed, and a smooth and synchronous time state trajectory is generated by minimizing jerk and introducing synchronization error hard constraint. The problems of space-time coordination and anti-swing control in double-hook hoisting operation are effectively solved, and the optimal balance of efficiency and safety is achieved.

A monorail hoist positioning method combining rail joint beacons and inertial navigation. An odometry kinematic model for a monorail hoist is established by means of an encoder on the monorail hoist, and an extended Kalman filter is used to fuse a heading angle obtained from a kinematic model solution and a heading angle obtained from a strapdown inertial navigation solution. Beacons are arranged at rail joints for the monorail hoist, and vibration signals are acquired by a vibration sensor. By combining a strapdown inertial navigation system, the encoder, and a rail installation diagram, a rail joint determination model is constructed to perform filtering, so as to obtain impact signals at rail joint beacons that meet a requirement. Beacon identifiers are used to match the identified rail joint beacons against specific beacon coordinates in the rail installation diagram, so as to obtain a time-position coordinate sequence for the monorail hoist arriving at the rail joints. A combined positioning model integrating rail joint beacons and strapdown inertial navigation is constructed to realize integration of local relative positioning and global absolute positioning of the monorail hoist. The method can improve the positioning accuracy of the monorail hoist while reducing positioning costs.

The invention discloses a non-contact ice friction coefficient measuring method and device, and relates to the tribology testing technology, and the method comprises the following steps: obtaining a motion image of a slide block on an artificial ice rink by using a camera system; performing preprocessing and target detection on the acquired image to obtain coordinate information of the sliding block in a world coordinate system; and based on the coordinate data of the sliding block, fitting and solving the acceleration of the sliding block by adopting primary function expansion in combination with a Tikhonov regularization method, and carrying out inversion in combination with a friction mechanical model and a kinematic model to obtain an ice friction coefficient. Compared with a traditional contact type method, the in-situ high-precision measurement of the ice friction coefficient can be realized under the condition of no sensor attachment or external force interference, the defects of low measurement precision and strong invasiveness in the prior art are effectively overcome, and the method can be applied to the fields of winter road safety detection and ice and snow sports.

The invention provides a cloud sensing shared information fusion method for a vehicle and road cloud cooperative system. The method comprises the steps that the vehicle and road cloud cooperative system comprises a cloud, a vehicle end and a road end; the cloud end obtains environment sensing information from other terminals through network communication; a space-time two-dimensional calibration system is constructed, and the space-time two-dimensional calibration system is adopted to calibrate the environmental perception information; dividing a unified global ID for each target by adopting a global target feature matching method, and performing redundancy removal processing on the calibrated environmental perception information by adopting a confidence evaluation comprehensive redundancy removal mechanism; performing pre-estimation compensation on the environment perception information after redundancy removal by adopting a model based on state estimation and kinematics; transmitting the compensated information to a corresponding terminal by adopting a sensing information routing sharing strategy based on the global ID to complete information sharing; according to the method, a resampling optimization strategy and a collaborative prediction correction algorithm are adopted, compared with a traditional linear prediction compensation method, situation information real-time performance and prediction accuracy are remarkably improved, and timeliness and reliability of follow-up decisions are effectively guaranteed.

The invention relates to the technical field of path planning, in particular to a three-dimensional environment multi-agent path planning method based on deep reinforcement learning, which adopts a near-end strategy optimization algorithm and expands the algorithm to a multi-agent system, and by designing an independent strategy network and a central value network, a multi-agent path is planned. Cooperative work and barrier-free path planning of multiple agents are realized, and the problem of slow convergence caused by complex environment is solved. Moreover, a long-short-term memory network is introduced into a strategy network, and historical state information is modeled, so that an intelligent agent can better capture a time sequence dependency relationship in an environment, and the path planning capability of the intelligent agent in a complex environment is improved; an incremental collision penalty reward mechanism based on dynamic distance perception is designed in a reward function, radar ranging is simulated through a kinematic model, and an obstacle avoidance decision is optimized in real time. According to the method, the multi-agent collaborative obstacle avoidance capability of the agents in a complex three-dimensional environment is remarkably improved, and the method has relatively high efficiency and adaptability.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle multi-load synchronous control system, and the system comprises a linkage control unit which is used for detecting an image shot by an unmanned aerial vehicle, obtaining a camera imaging plane deviation according to a detection result, and combining with a kinematic model to solve an expected instruction of the unmanned aerial vehicle and a holder; the multi-load configuration unit is used for determining control logic according to the load type and the expected instruction and issuing an operation instruction to the load according to the control logic so as to realize linkage control of the load and the unmanned aerial vehicle controller; the control instruction adjusting unit is used for acquiring the flight state of the unmanned aerial vehicle when the expected instruction is executed, and calculating a disturbance value of the unmanned aerial vehicle to determine a final control instruction of the unmanned aerial vehicle by taking the flight state as input; the equipment is an airborne computing power unit. The active-disturbance-rejection pose correction technology is introduced, real-time compensation of disturbance such as wind disturbance, load sudden change and hanging point deviation is achieved, and the working efficiency and the complex scene adaptability are improved.

A method for controlling anti-skid braking in civil aircraft involves constructing an anti-skid braking model and flight dynamics and kinematics models of the aircraft to analyze the forces acting on the aircraft system and obtain the corresponding forces and torques. For the anti-skid braking system, a BSMC controller is designed to control the braking coefficient μ. Brake This invention controls the braking force to ultimately control the anti-skid braking system of civil aircraft. It uses a backstepping slip mode controller (BSMC) to control the aircraft's braking commands, ensuring the system maintains the optimal slip ratio during braking, maximizing braking efficiency, reducing wheel wear, and minimizing the cost of the aircraft's braking system.

The invention provides a deep learning trajectory prediction method introducing a vehicle kinematics constraint, and aims to solve the problem that trajectory prediction lacks physical consistency. The invention relates to the field of automatic driving. The system comprises a deep learning prediction module, a physical model prediction module and a model optimization training module. The deep learning prediction module receives vehicle and map information, extracts features and interacts with each other by using a gating cycle unit GRU and a graph convolution sub-graph network, and outputs a first prediction trajectory. And the physical model prediction module uses a PID controller to calculate a control quantity according to the reference position state, and drives a kinematic model to generate a second prediction trajectory conforming to kinematic constraints through closed-loop deduction. And the model optimization training module constructs joint loss and back propagation optimization network parameters based on a prediction error and a physical consistency error between the two trajectories, and finally loads an optimal model output trajectory.

Example methods and systems for kinematic calibration of a treatment couch for radiation therapy are described. In one example, a computer system may generate and send first instruction(s) to cause the treatment couch to move towards each of multiple calibration poses. The treatment couch may be associated with a kinematic model. The computer system may determine multiple measured poses that are associated with the respective multiple calibration poses. Next, the computer system may determine geometric correction data for updating the kinematic model based on the multiple measured poses. The computer system may then generate and send second instruction(s) to update the kinematic model associated with the treatment couch based on the geometric correction data.

The invention relates to a vehicle dynamics simulation method and system based on a recurrent neural network and a medium. The method comprises the steps that control quantity time sequence data and state quantity time sequence data of a real vehicle are collected; constructing a time sequence window as the input of a recurrent neural network based on the control quantity time sequence data and the state quantity time sequence data; processing the time sequence window through the recurrent neural network, and outputting a longitudinal acceleration predicted value and a yaw velocity predicted value at the next moment; and inputting the longitudinal acceleration predicted value and the yaw velocity predicted value into a kinematics model, and calculating to obtain velocity, course angle and displacement information at the next moment so as to form a hybrid dynamics closed loop combining neural network deduction and kinematics integration. According to the invention, model training can be carried out based on the real vehicle data to cover wider working condition scenes, so that the dynamic characteristics of the real vehicle can be better aligned, and the requirements of automatic driving simulation on diversified scenes are met.

The invention discloses an industrial robot motion pose error calibration method based on constraint solution. The industrial robot motion pose error calibration method comprises the steps that S1, joint angles, tail end coordinates and temperature data of multiple measurement points are collected; s2, correcting the kinematic model by using the thermal elongation and constructing a parameter identification model; s3, constructing a mixed integer nonlinear constraint pool according to mechanical limitation; s4, introducing a semi-definite programming slack variable to convert the non-convex constraint into a linear matrix inequality; s5, combining a primal-dual interior point method and an alternating direction multiplier method to iteratively solve the optimal kinematics parameter correction; s6, introducing a square root information filtering improvement mechanism to correct a state vector square root factor, and calculating an updated nominal kinematics parameter; and S7, based on the condition number of the error Jacobian matrix, judging observability and repeatedly calibrating. According to the method, the problem of precision reduction caused by thermal errors is solved, and the identification precision, the numerical stability and the self-adaptive calibration capability of the kinematics parameters of the robot are improved.

The invention discloses a quadruped robot advancing path selection method and system based on Kalman filtering, and belongs to the technical field of robot movement control. The method is used for solving the technical problems that sudden change of foot end contact force of a quadruped robot or terrain slope change can cause model mismatch, and then filtering divergence or estimation deviation is caused. A kinematic model is corrected in real time through a slope angle, so that system state prediction is more fit with an actual terrain, noise covariance is dynamically enhanced when contact stiffness is suddenly changed, and the situation that small-probability large sudden change is excessively suppressed by filtering is avoided; the method can dynamically adapt to changes of terrain gradient and contact rigidity, according to the output filtering optimal state estimation and in combination with a preset foot end landing point stability evaluation function, an advancing landing point meeting force-pose constraint is generated, the robot is driven to execute, and the quadruped robot can autonomously select a stable landing point in an unknown complex terrain.

The invention relates to the technical field of agricultural machinery operation, in particular to a field operation path planning method and system for agricultural machinery equipment in hilly and mountainous regions, and the method comprises the following steps: obtaining terrain data of the hilly and mountainous regions and operation data of the agricultural machinery equipment, so as to construct an environment equipment cooperation data set; analyzing the environment equipment collaborative data set to obtain a field operation segmentation area and a field operation priority as an agricultural machinery operation analysis result; based on the agricultural machinery operation analysis result, establishing a four-wheel steering kinematics model and an agricultural machinery trafficability constraint model as mechanical constraint conditions of agricultural machinery equipment; generating an initial agricultural machine operation path by combining the agricultural machine operation analysis result and the agricultural machine equipment mechanical constraint condition; and performing multi-target adaptive adjustment on the initial agricultural machine operation path according to an agricultural machine operation mode to obtain an optimal agricultural machine operation path. The method is combined with mechanical characteristics of agricultural machinery equipment, the accuracy of path planning is improved, and complex terrains of hills and mountains are effectively adapted.