696 results about "Path tracking" patented technology

The invention discloses a data anomaly detection system and method based on power equipment, and belongs to the technical field of power system data processing and fault monitoring, and the method comprises the steps: obtaining a multi-dimensional operation parameter sequence of voltage, current, temperature, harmonic waves, switching states, topological energy transfer vectors and the like; constructing a local disturbance response map, extracting a micro-disturbance driving factor, and generating a high-dimensional feature embedding matrix; constructing a multi-scale state density map on the basis of the embedded matrix, and mapping an equipment behavior evolution track; introducing a coupling evolution path tracking algorithm based on the density map, identifying an abnormal state set, and constructing a cross-time-period risk channel; matching the current monitoring data with the risk channel, and calculating a risk evolution value; when the value exceeds a set threshold value, abnormal early warning is triggered, and an evolution path model is output; the method has the advantages of high accuracy, strong trend identification capability and early warning, and is suitable for intelligent operation and maintenance of power equipment under complex working conditions.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention relates to the technical field of autonomous navigation, in particular to an AUV (Autonomous Underwater Vehicle) multi-sensor fusion autonomous navigation system based on ant colony-particle swarm collaboration.The AUV multi-sensor fusion autonomous navigation system is characterized in that a sensor weight decision module constructs a pheromone matrix based on an ant colony algorithm, retrieves a mapping relation between historical errors and pheromone concentration in real time and dynamically generates a sensor priority sequence; adaptive weight distribution in a data conflict scene is realized; the dynamic path correction module is combined with a particle swarm algorithm and pheromone concentration gradient constraint to balance global path optimality and real-time obstacle avoidance requirements in optical camera obstacle deviation correction; and the closed-loop coupling module is used for converting sonar and geomagnetic residual errors into pheromone concentration correction values and confidence coefficient regulation factors by fusing residual error compensation and path tracking error feedback, so that an error traceability-weight calibration-path optimization closed loop is formed, sensor drift is inhibited, and the long-endurance navigation precision is improved.

The invention relates to an underwater cleaning robot positioning and tracking method. Positioning comprises the steps that data of multiple sensors on an underwater cleaning robot are fused through an extended Kalman filter, and coordinates of the underwater cleaning robot on the surface of a ship body are obtained after sensor measurement noise is filtered through a first-order low-pass filtering algorithm. The tracking method comprises the step of realizing the path tracking of the underwater cleaning robot based on the positioning method in combination with an iterative linear quadratic regulator (ILQR). According to the invention, a multi-sensor fusion scheme of the encoder, the IMU and the depth meter is innovatively provided, and the positioning problem of the underwater cleaning robot in a wall attaching state is solved. And double-plane differential treatment is adopted, so that the working state of the robot on the ship wall or the ship bottom is better fitted, and positioning is more accurate. A target point updating mechanism of geometric distance constraint is provided, the nearest neighbor point of the reference trajectory is adjusted in real time, and the problem of tracking lag caused by updating rate mismatch in a traditional method is solved.

The present application relates to the technical field of vehicles, and provides a vehicle control method and apparatus, an electronic device, and a medium. The method comprises: on the basis of the transverse motion state quantity and the longitudinal motion state quantity of a first vehicle, solving for a tracking control problem for tracking a planning path and a planning speed, so as to obtain a first wheel steering angle control quantity and an expected acceleration of the first vehicle, wherein the first wheel is a front wheel or a rear wheel, the planning path comprises a reference value of the transverse motion state quantity of the vehicle at a series of time points in the future, and the planning speed comprises a reference value of the longitudinal motion state quantity of the vehicle at a series of time points in the future; obtaining the acceleration control quantity of the first vehicle on the basis of the expected acceleration of the first vehicle and the actual acceleration thereof; and controlling the first vehicle on the basis of the first wheel steering angle control quantity and the acceleration control quantity of the first vehicle. In the present application, vehicle control is carried out in a transverse and longitudinal combined tracking control mode, so that the speed tracking accuracy can be improved as much as possible while the path tracking accuracy is ensured.

The invention relates to the technical field of autonomous navigation of inspection robots, and provides a field inspection robot autonomous navigation and path planning method based on multi-modal sensing. The method comprises the following steps: collecting a laser radar point cloud, a camera image, inertial measurement and positioning data, obtaining an environment semantic feature set through spatio-temporal feature fusion, and generating a semantic occupation map; inputting the current position of the robot, the target point and the semantic occupation map into a trajectory generation network to obtain candidate trajectories meeting obstacle avoidance and path smoothness constraints, and completing task sorting and trajectory splicing in combination with inspection task points to form a global path; in the operation process, the reinforcement learning control model adjusts the linear speed and the angular speed in real time, and path tracking and dynamic obstacle avoidance are achieved. The navigation precision and the operation safety of the inspection robot in the complex field area are improved.

The invention provides a method and a system for dynamically optimizing a security defense strategy based on an AI Agent, and aims to solve the problems that the traditional network security defense strategy is static and cannot adapt to a dynamic network environment and novel network threats, and the processing efficiency of massive security data is low and the response is not timely. The method comprises the following steps: firstly, realizing whole network node data acquisition through a distributed AI Agent, performing accurate identification in combination with a multi-dimensional threat perception and fusion detection mechanism, and establishing a threat parameter quantitative evaluation model; secondly, the AI Agent generates a dynamic defense strategy according to the analysis result of the intelligent threat detection and the real-time state of the network in combination with threat features and risk assessment; and finally, according to the dynamic defense response result, realizing attack path tracking, accurate vulnerability positioning, automatic repair execution and traceability information retention processing. According to the method, real-time sensing and quick response to network threats can be realized, and the hysteresis of a traditional static defense strategy is overcome.

The invention discloses a financial traceability risk measurement and analysis method and system based on dynamic entropy transfer, and the method comprises the steps: constructing a dynamic entropy transfer graph through combining with various types of structure information, such as local guiding entropy, path exploration entropy and behavior collaborative entropy; the method can effectively describe the propagation path and the structure level of the risk factor in the complex financial network while keeping the calculation controllable. According to the method, the edge behavior sequence is constructed through the Gaussian kernel function on the basis of the node behavior time sequence, and the multi-scale permutation entropy is combined to model the inter-edge time sequence collaborative change, so that the expression ability of the system to the microscopic interaction behavior is remarkably improved, and the method has good adaptability and interpretability. The method can achieve the positioning of a potential high-influence risk source and the reconstruction of a propagation chain, is suitable for a plurality of scenes, such as financial supervision, risk early warning and abnormal path tracking, is high in processing efficiency, and is stable and reliable in result.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention relates to a navigation control method and device of agricultural machinery, electronic equipment, a medium and the agricultural machinery, and the method comprises the following steps: acquiring a positioning point coordinate, course information, real-time vehicle speed and yaw rate information of the agricultural machinery at the current moment, and calculating the navigation of the agricultural machinery according to the positioning point coordinate, course information, real-time vehicle speed and yaw rate information at the current moment; calculating transverse deviation, course deviation and estimated values of the front wheel turning angle between the agricultural machine and a preset target path point according to the geometrical relationship, calculating an expected value of the front wheel turning angle at the next moment of the current moment according to the transverse deviation, the course deviation and the estimated values of the front wheel turning angle, and calculating the expected value of the front wheel turning angle at the next moment according to the expected value of the front wheel turning angle at the next moment. The agricultural machine is controlled to advance. By means of the method, the expected value of the front wheel rotation angle at the next moment is optimized, so that accurate control over advancing of the agricultural machine is achieved, the path tracking accuracy of the agricultural machine is remarkably improved, deviation errors in the operation process can be effectively reduced, and it is ensured that the agricultural machine accurately travels according to the preset path.

The invention discloses an automatic driving path planning method based on multi-sensor fusion, and relates to the technical field of automatic driving, and the method comprises the steps: collecting environment, vehicle and positioning data through multi-source sensors, such as a laser radar and a camera; mapping a vehicle body coordinate system through coordinate conversion, and carrying out downsampling, correction enhancement and denoising preprocessing on data; extracting multi-sensor features, and distributing weights by means of a cross-modal attention mechanism to complete multi-modal fusion; recognizing obstacles and passable areas, and predicting a 5s track of a dynamic obstacle; 5-8 initial paths are generated based on the coordinate system and dynamic constraints; constructing a multi-objective function to screen an optimal path; and finally, a steering angle and an acceleration instruction are calculated through model prediction control, and accurate path tracking is realized. The sensing robustness is improved through multi-sensor fusion, the dynamic weight adjustment adapts to the complex environment, the emergency obstacle avoidance mechanism is quick in response, and the automatic driving reliability and the scene adaptability are integrally enhanced.

The invention discloses a construction waste tracking method based on a graph neural network. The method comprises the following steps: constructing preprocessed construction waste transportation multi-source data; constructing a construction waste transportation network weighted heterogeneous graph based on the preprocessed construction waste transportation multi-source data; a GraphSAGE node embedding vector is generated and updated; outputting an interruption point event consisting of the interruption position, the occurrence moment and the state element; extracting a local sub-graph with a fixed hop count from the weighted heterogeneous graph of the construction waste transportation network; a detour candidate path set with priority is obtained, and a self-healing tracking path with the highest score is selected; and forming an updated construction waste transport path tracking expected path, marking a potential illegal dumping risk node by combining compliance risk information in a GraphSAGE node embedding vector, and outputting a construction waste path tracking result. According to the method, detour path optimization sorting and high-risk path elimination are realized, and path reconstruction is enabled to better meet urban transportation scheduling and supervision requirements.

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 belongs to the technical field of track planning, and particularly relates to a track planning method for a track and non-track mixed region, and the method comprises the steps: dynamically distributing a path point sequence for an automatic guided vehicle, and switching a control mode according to the type of a region where a current target point is located: generating a first control instruction for path tracking in a track region; in the non-orbital area, the pose and curvature of the exit of the previous orbital area are used as initial reference quantities to plan a smooth trajectory, and a second control instruction is generated; constraint shaping is carried out on the first control instruction, the second control instruction is adjusted, it is ensured that the end point state of path tracking and the starting point state of trajectory planning meet the continuity rules of the position, the course and the curvature at the joint point of the two areas, and finally the instructions are sent to corresponding control loops to be executed. According to the invention, smooth and coherent navigation control of the automatic guided vehicle between the fixed track and the free driving area is realized.

The invention provides an ocean current interference resistant fault-tolerant control method and system for a robot multi-motor propulsion system, and the method comprises the steps: inputting the actual and expected position error and speed error of an underwater robot into a self-adaptive backstepping sliding mode path tracking controller, and obtaining the thrust needed by tracking motion; introducing a propeller weight matrix representing the fault degree of the propeller into a weighted pseudo-inverse method, optimizing the propeller weight matrix by adopting an intelligent algorithm to obtain an optimal propeller weight matrix, and performing thrust distribution on thrust required by tracking motion through the weighted pseudo-inverse method to obtain a thrust distribution result; according to the operation and thrust distribution results of the multiple motors, the parameters of all the motors are adjusted in a self-adaptive mode through a multi-motor fault-tolerant control strategy; and each motor remotely controls the underwater robot based on the parameters after self-adaptive adjustment, and obtains the actual position and speed of the underwater robot at the current moment for loop closing.

The invention discloses an intelligent automobile double-event trigger control method aiming at double communication networks. The method comprises the following steps: initializing; establishing intelligence to judge whether an SC event triggering condition and a CA event triggering condition are met or not; solving a front wheel steering angle control instruction; and performing intelligent automobile double-event trigger control. According to the invention, a double-event trigger condition is designed to determine whether a state signal of an intelligent automobile path tracking type 1 fuzzy model is updated to a type 1 fuzzy time-lag controller or not and determine whether a front wheel steering angle control instruction calculated by the type 1 fuzzy time-lag controller is updated to an actuator module or not; communication resources of double communication networks are effectively saved; the method has the capability of efficiently avoiding the problem of communication congestion, can save a lot of communication resources, and improves the reliability of the intelligent automobile in the path tracking control process. The fuzzy time-delay controller is designed to calculate a front wheel steering angle control instruction, and the reliability of path tracking control of the intelligent automobile is ensured.

The invention provides a vehicle stability control method, device and system and electronic equipment, relates to the technical field of vehicle active safety control, and aims to construct a control framework based on a two-degree-of-freedom dynamical model and realize accurate control of the yaw velocity and the side slip angle of a vehicle. By introducing the road adhesion coefficient and combining the vertical load of each wheel for independent torque distribution, the tire adhesion utilization rate and the environmental adaptability and safety of the vehicle under different road conditions (such as ice and snow, wet and slippery conditions and dry conditions) are improved. According to the scheme, calculation is easy and convenient, response is rapid, good engineering practicability is achieved, and the robustness and adaptability of the system under the complex working condition are enhanced while the vehicle stability and the path tracking capacity are improved.

The invention discloses an intelligent driving control method and system for an unmanned ship, and belongs to the field of unmanned driving control. Firstly, the position and speed of the unmanned ship and the positions and speeds of surrounding obstacles are obtained through a sensor and a positioning device, then dynamic path tracking is carried out in combination with global path planning and local path planning, and finally the unmanned ship is driven to sail along an optimized path by controlling the course and an accelerator. Local path planning adopts an improved dynamic window method, and an unmanned ship trajectory prediction evaluation function is modified by introducing a dynamic risk evaluation layer and a rule compliance layer; the dynamic risk evaluation layer is used for designing a collision risk evaluation function to dynamically screen a speed sampling space and rejecting a speed vector conflicting with a predicted trajectory of a moving obstacle; and the rule compliance layer classifies the meeting situation, introduces a rule evaluation function to perform weight correction on the candidate velocity vector, and drives the unmanned ship to generate a course adjustment strategy conforming to the collision avoidance rule. The unmanned ship realizes full-autonomous cruise, high-precision obstacle avoidance and path tracking in a complex marine environment.

The invention relates to the technical field of intelligent sensors, in particular to a workshop harmful gas abnormity monitoring method and system based on multi-modal data, and the method comprises the following steps: obtaining the multi-period concentration change of a node, recognizing the reverse direction, forming a bounce fragment, combining node behaviors according to the correlation of time and space, and constructing a diffusion path; and in combination with node toxicity residence and trend change and gas and time information at the convergence position, a workshop multi-modal toxicity anomaly monitoring result is obtained. According to the method, gas fluctuation characteristics are captured by recognizing the concentration change direction and the reversal amplitude, a behavior combination is constructed by combining time and space information, the linkage recognition capability between nodes is enhanced, a path sequence is constructed by depending on the trend direction and the node sequence to present a diffusion track, and extension and mutation nodes are extracted by superposing the residence state and the change trend. The dynamic monitoring range of toxic gas in multiple scenes is expanded, and correlation analysis of behavior traceability, path tracking and abnormal gathering areas is supported.

The invention discloses a reinforcement learning unmanned ship path control method based on double-track regulation and control random network distillation. The method comprises the operation steps that an unmanned ship builds a path tracking simulation environment and a kinetic model; the unmanned ship builds a core algorithm flexible action evaluation algorithm framework; the unmanned ship deploys a priority experience playback pool based on quality and success guidance; an uncertainty perception and risk perception mechanism is introduced into the unmanned ship; the unmanned ship builds a success rate-based reward attenuation and cold start module, and the unmanned ship calculates a total reward and designs a reward softening mechanism to smooth the total reward; the unmanned ship imports hyper-parameters of all the modules, starts training circulation in a simulation environment, and dynamically adjusts exploration intensity and the like; according to the method, uncertainty and risk indexes are introduced, the exploration intensity of the intelligent agent is controlled, the intelligent agent is prevented from making dangerous actions, and the robustness is improved; a priority experience playback pool based on quality and success guidance is introduced, high-quality samples are better played back, and strategy convergence is accelerated.

The invention provides a business path tracking and fault tracing method and device for a video monitoring system, and relates to the technical field of video monitoring fault diagnos.The video monitoring system is divided into a plurality of layers, and multiple types of data acquisition probes are deployed at key nodes of each layer; when a user initiates a video request, generating a Trace ID to be spread along with a video streaming signaling, and reconstructing an end-to-end service path in combination with node information captured by a probe; associating and fusing the multi-dimensional performance data by taking the TraceID as a key, and establishing an end-to-end total delay decomposition and dynamic health baseline; and positioning a root cause node based on the weighted directed graph, the node anomaly confidence score and the fault propagation consistency, displaying a path, data and a traceability result through a visual interface, and generating a precise alarm. According to the method, service link visualization is realized, traditional equipment alarm is upgraded to service fault root cause positioning, the troubleshooting efficiency is improved, and the method is suitable for an isomerized large-scale video monitoring system.

The invention provides a ship path tracking and rolling comprehensive control method and device based on a straight-wing propeller, belongs to the technical field of ship motion control, and solves the technical problem that path tracking and rolling control precision and efficiency of a ship are insufficient due to environmental disturbance. Constructing a four-degree-of-freedom ship motion equation, constructing a four-degree-of-freedom ship motion model with interference and measurement noise, reconstructing a ship motion state by adopting a Kalman filter, calculating a path tracking error by utilizing a self-adaptive sight guidance algorithm, and generating an expected course angle; determining a longitudinal thrust, a rolling stabilizing moment and a course control thrust; and the revolution speed and the rotation swing angle of the straight wing propeller are optimized, and the rotation swing angle is recalculated. The method is suitable for the ship provided with the straight-wing propeller, and high-precision path tracking and efficient rolling stabilization are achieved by controlling the revolution speed and the swing angle of the propeller.

The invention discloses a water surface unmanned ship control method and system under path tracking, and relates to the technical field of attitude control, and the method comprises the steps: building a kinetic model of a water surface unmanned ship; establishing a path tracking error equation for describing a system error of the unmanned surface ship relative to an expected path under a Serret-Frenet coordinate system; constructing a path tracking controller based on a linear active disturbance rejection controller; inputting the system error and a water surface unmanned ship control signal into a fuzzy RBF neural network, and outputting an optimal control parameter of a path tracking controller by taking minimization of the system error as a target; updating a path tracking controller according to the optimized control parameters; and re-estimating external disturbance by using the updated path tracking controller, outputting a control signal for optimizing the water surface unmanned ship, and controlling the water surface unmanned ship to advance along an expected path. According to the invention, the attitude control precision under strong ocean current and changeable weather is improved, and the path tracking precision of the water surface unmanned ship is greatly improved.

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 unmanned ship large-curvature path tracking control method based on curvature self-adaption and improved disturbance observation, and belongs to the technical field of unmanned ship autonomous navigation and path. The method comprises the following steps: loading a reference path and initializing parameters; calculating a transverse error and a path curvature in each control period, wherein the path curvature adopts a multi-stage calculation and smoothing method; determining a preview point through a self-adaptive preview distance calculation method based on the transverse error and the curvature; an improved state disturbance observer SDO is used for estimating disturbance, and the bandwidth of the observer is adjusted through a self-adaptive mechanism driven by two factors; calculating a control quantity through a control law in combination with deviation and disturbance; and updating the boat body state until path tracking is completed. According to the method, the preview distance and the observer bandwidth can be dynamically adjusted, disturbance can be effectively estimated and compensated, the precision and robustness of the unmanned ship in large-curvature path tracking are improved, and the accuracy of system state observation and the stability of control performance can be ensured when scenes such as a large-curvature path and a straight line segment are switched.

The invention relates to the technical field of agricultural informatization and supply chain risk control, and particularly discloses an agricultural supply chain risk intelligent sensing and early warning system based on multi-source data fusion. According to the system, six multi-source data including a producing area environment, an agricultural product category, a transaction behavior, a logistics link, credit and market information are integrated through a data acquisition layer; space-time alignment and cleaning are carried out through the data processing and alignment layer; through feature engineering and a multi-source fusion layer, depth features are constructed by comprehensively utilizing a knowledge graph and a graph attention network, Transform behavior sequence modeling, CNN-LSTM remote sensing time sequence analysis and Graph2Seq logistics trajectory prediction, and the Transform and the graph attention network are specially adapted for an agricultural scene; the dynamic risk scoring layer is used for comprehensively calculating five risks of producing areas, logistics, transactions, markets and credit, and a comprehensive risk score is output through weighted fusion; a decision basis is provided by integrating SHAP, attention visualization and map path tracking through an interpretability output layer; and finally, outputting a grading risk strategy by a strategy decision-making layer. According to the invention, dynamic, accurate and explainable intelligent assessment and early warning of full-link and multi-dimensional risks of the agricultural supply chain are realized.

The invention discloses an unstructured road path tracking and speed optimization control method and device, a medium and a product, and relates to the technical field of vehicle automatic driving, and the method comprises the steps: obtaining the current state information of a vehicle and a plurality of path points; carrying out smoothing processing on the path points; selecting a target path point and a plurality of continuous path points including the target path point, generating a preview interval, and determining the curvature of the preview interval; obtaining a reference control quantity based on the current state information of the vehicle and the curvature of the preview interval; acquiring state information of a plurality of continuous path points, and determining a reference state quantity of a target path point; constructing a linear error equation and a cost function; determining an LQR feedback gain matrix by adopting a mixed solution mechanism and combining a cost function; and obtaining a final control instruction based on the LQR feedback gain matrix so as to control the vehicle to run to the target path point. According to the method, the efficient and stable vehicle control effect can be achieved, and the vehicle control performance is improved.

The invention discloses a distributed driving electric vehicle path tracking and stability control method and system, and belongs to the technical field of electric vehicle active safety control. The method comprises the following steps: constructing a state-space equation based on a driver path tracking model and a vehicle three-degree-of-freedom motion model; establishing a dynamic envelope region based on a phase plane method, inputting the current parameters of the vehicle into the dynamic stable envelope region, and outputting a weight; constructing a controller based on the state-space equation, and taking the weight output by the dynamic stable envelope region as the control weight of the controller; inputting the vehicle state variables and the vehicle parameters into a controller, and outputting a longitudinal driving torque, an additional yaw torque, an additional front wheel turning angle and a driver intention turning angle required by driving; the additional front wheel turning angle and the driver intention turning angle are superposed to serve as input of a steering motor; a torque distributor is designed to distribute a longitudinal driving torque and an additional yaw torque, and the distributed torques are used as given values of the four hub motors.

The invention discloses an underwater robot path tracking control method and device, terminal equipment and a storage medium, and belongs to the technical field of path tracking, and the method comprises the steps: obtaining the current pose data of an underwater robot and the path parameters of a to-be-tracked path; then, according to the current pose data and the path parameters, current operation state parameters of the underwater robot are obtained through calculation; according to the current operation state parameter and a preset path tracking model, determining a current target operation action of the underwater robot; and finally, adjusting the running state of the underwater robot according to the current target running action, so that the underwater robot advances according to the to-be-tracked path. By implementing the method and the device, the problem that the control precision is reduced due to the fact that a PID controller based on fixed parameters is adopted to realize path tracking in the prior art and cannot adapt to the constantly changing running state of the underwater robot in real time can be solved.

The invention discloses an orchard transportation robot path tracking system, and relates to the technical field of robot programmes, and the system comprises the steps: collecting an alignment data set, extracting a stability feature, a terrain trafficability feature and initial trajectory deviation data based on the alignment data set, generating a deviation scoring result based on the initial trajectory deviation data and the trafficability feature, and carrying out the tracking of an orchard transportation robot path. According to the method, autonomous path tracking and avoidance control of the orchard transportation robot are realized through multi-source perception and intelligent decision, positioning, attitude and environment structure data are fused, the advancing stability and terrain trafficability are analyzed in real time, and the accuracy and the reliability of the orchard transportation robot are improved. The path deviation trend is predicted and adaptively corrected, high-precision navigation in a complex orchard environment is realized, the robot can keep stable operation in a multi-machine cooperation and edge-of-field steering scene through a dynamic channel regulation and control and feedback learning mechanism, and automation and operation efficiency of orchard transportation operation are remarkably improved.