934 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 provides a defect positioning accurate identification method considering the change of the section of a pile body, and the method comprises the steps: carrying out the simulation comparison of a constructed sound wave signal feature vector and a pre-constructed sound wave feature vector of a defect-free pile foundation, calculating the feature difference degree of the two, setting a difference degree threshold value, and if the feature difference degree exceeds the threshold value, carrying out the recognition of the defect-free pile foundation. If yes, preliminarily determining that the pile body has defects; on the basis of the adjusted sound wave detection, sound wave signals are collected again, signal processing and feature extraction are conducted through a self-adaptive path tracking algorithm, and the three-dimensional coordinates of the defect in the pile body are calculated according to the time delay and the propagation speed of reflected waves by combining the established multi-reflection model and an inversion sound wave propagation path; and according to the calculated three-dimensional coordinates of the defect reflection points, the influence degree of the defects on the bearing capacity of the pile foundation is calculated by combining the defect size estimated through the amplitude of reflected waves, the defect type recognized through waveform features and the strength parameter of a pile body material through a finite element method.

The invention relates to the technical field of automatic driving, and particularly provides an automatic driving vehicle track re-planning method, which comprises the following steps of: firstly, evaluating stability risk and defining three states of vehicle stability through real-time vehicle dynamic parameters, and designing intervention and exit criteria of track re-planning according to the three states; when the vehicle driving is evaluated to be in an unstable state, generating a virtual obstacle by using prediction information of a path tracking module, and converting a dynamic stability boundary into a spatial constraint; performing trajectory re-planning by adopting nonlinear model predictive control, and adjusting the weight between the path tracking precision and the obstacle avoidance demand according to the relative position of the virtual obstacle in combination with a dynamic weight adjustment mechanism; and finally, generating an optimized trajectory containing stability constraint, and realizing high-precision tracking through linear MPC. The method can effectively balance the path tracking precision and the vehicle stability under a complex working condition, and prevents the vehicle from entering a dynamic unstable region.

The invention provides an unmanned ship charging cable control method based on visual identification, and relates to the technical field of data processing, and the method comprises the steps: collecting continuous image frames of an unmanned ship charging area, identifying the spatial displacement and inclination angle change of an unmanned ship, analyzing the attitude offset of the unmanned ship caused by sea waves, calculating a water surface disturbance factor, and calculating the water surface disturbance factor. The method comprises the following steps: identifying the position of a charging interface, obtaining an initial positioning coordinate, carrying out prediction analysis on a disturbance trend in a preset time window, predicting a position change range of the charging interface, obtaining prediction coordinate data, planning a butt joint path of a cable and the charging interface, obtaining a compensation path sequence, and generating a cable propulsion instruction. And collecting an area image of the charging interface to obtain path tracking image data, judging whether the butt joint of the charging interface is successful or not, and if not, dynamically correcting the compensation path sequence. According to the invention, the cable is controlled through visual identification to charge the unmanned ship.

The invention relates to the technical field of orchard unmanned vehicles, in particular to an orchard unmanned vehicle intelligent navigation and control method based on multi-sensor fusion, and the method comprises the following steps: S1, global path planning: generating discrete plane path points, and then pre-fitting a prior navigation global path; s2, three-dimensional environment perception: extracting static obstacle and dynamic obstacle information in a working path in real time, detecting and identifying a target obstacle at the same time, and performing sensor information fusion; s3, path tracking control: controlling the orchard unmanned vehicle to run according to a priori navigation global path; and S4, dynamic path optimization: realizing dynamic planning of an obstacle avoidance trajectory, synchronously establishing a trajectory overlap ratio evaluation mechanism, and adjusting a driving mode to avoid the obstacle. According to the invention, the reliability of environment perception and path dynamic adjustment is improved, and the three-dimensional motion control model is constructed, so that the unmanned vehicle can safely and accurately complete autonomous navigation work.

The invention belongs to the technical field of unmanned ship tracking control, and discloses a self-adaptive path tracking control method and device for an unmanned ship. The method comprises the steps of monitoring position data in real time through a boundary judgment algorithm to ensure that a ship is always in a preset safe area; multi-source data of ocean currents, radiation, batteries and the like are synchronously collected, three constraint conditions of predicting a drifting track, a charging area and remaining endurance time are constructed, a global optimal path is dynamically generated by applying a path planning algorithm, and the shortest distance, low energy consumption and high charging benefits are balanced. For energy management, a multi-stage power threshold is preset to trigger a differential loss reduction strategy. In a control layer, calculating propeller control parameters in real time based on path deviation, course deviation and environment interference; through collaborative optimization of global path planning and local control, the autonomous navigation capability, the energy utilization efficiency and the task sustainability of the unmanned ship in a dynamic environment are improved, and the method is suitable for ocean monitoring, resource exploration and other scenes.

The invention provides an unmanned ship path tracking control method based on lightweight self-adaptive sight guidance. The method comprises a self-adaptive sight guidance algorithm, a foresight distance is adjusted in real time according to the current position and path deviation of an unmanned ship, ideal course information is obtained, and the path convergence speed and algorithm calculation efficiency are improved. And in the virtual simulation platform, marine environment information is combined, and unmanned ship dynamic model parameters are obtained through experiments. And constructing an objective function taking the path tracking error and the rudder angle change as evaluation indexes, and optimizing PID controller parameters by adopting an improved sparrow search algorithm. According to the algorithm, a dynamic additive updating strategy is introduced, so that the global search capability and the convergence stability are improved, and the optimal PID parameter is ensured to be obtained. The method has the advantages of being high in response speed, small in overshoot and high in path tracking precision in the complex marine environment, the real-time control performance and the environment adaptability of the unmanned ship can be remarkably improved, and the method is suitable for the unmanned ship path tracking control requirements in various task scenes.

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 belongs to the field of active steering of electric vehicles, and relates to an active four-wheel steering path tracking control method based on Nash game, which comprises the following steps: S1, establishing a two-degree-of-freedom transverse dynamic model of a vehicle; s2, designing a tire cornering stiffness correction coefficient by adopting a magic formula to correct the cornering stiffness in the prediction model in real time; s3, the Nash game is introduced into four-wheel steering control, the control target of front wheel steering is tracking precision, and the control target of rear wheel steering is transverse stability; and solving the optimal control quantity of each gamer through a distributed model prediction architecture.

The invention relates to a four-way shuttle vehicle based on ontology state perception and a self-calibration positioning method of the four-way shuttle vehicle. The method comprises the following steps: establishing a body state sensing system, collecting three-axis acceleration, three-axis angular velocity, wheel set displacement and attitude angle data of a shuttle vehicle body, and synchronously forming a standardized sensing data set; performing fusion calculation on the standardized sensing data set to obtain real-time position and attitude parameters; calculating an initial value of a positioning error based on matching of the real-time position and attitude parameters with pre-stored high-precision track map data; according to the positioning error initial value, adaptive correction calculation is executed, a real-time positioning error correction amount is determined, and positioning information is corrected; parameters of a shuttle vehicle track control system are updated according to the corrected positioning information, and track navigation control is carried out; accumulated positioning error correction data are periodically analyzed, self-calibration model parameters are optimized, and positioning accuracy and stability are improved. According to the invention, the positioning reliability and path tracking capability of the four-way shuttle vehicle in a complex environment can be improved.

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 present invention discloses a method for tracing power supply paths in important locations based on digital twins. In order to solve the problem that it is time-consuming and laborious in the prior art due to the fact of requiring manual data collection and mapping in the case where the mapping of a single object in a digital space needs to be restored according to the actual situation on the spot, the present invention hierarchically establishes digital twin scenarios, constructs a complete power supply path according to a signal flow relationship between power topology and equipment, and conducts power supply path tracing in the event of equipment failure to determine a scope of influence, thus being fast and accurate in tracing.

The invention discloses an AUV (Autonomous Underwater Vehicle) path tracking method based on predicted line-of-sight angle guidance, which comprises the following steps: acquiring a real-time navigation state through inertial navigation, acoustic positioning and DVL fusion, introducing a fixed time disturbance predictor to estimate a sideslip angle and an ocean current velocity component on line, adaptively adjusting a line-of-sight distance according to transverse error-navigational speed-curvature, and tracking the path of the AUV. Calculating a predicted line-of-sight angle and outputting an expected heading; and then a propeller and a control surface are driven by adopting a fixed time feedback control law, so that track and attitude error fixed time convergence of the under-actuated AUV under the conditions of strong time-varying ocean current and large sideslip is realized. The method does not need a side thruster or an ocean current prior model, is simple in algorithm structure, is suitable for a high-speed high-curvature track, and has the advantages of high speed, high steady-state precision, low energy consumption and high robustness.

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 invention relates to the technical field of data visualization, in particular to a digital twinning-oriented workshop data visualization system, which comprises a heterogeneous data space-time stamp reconstruction module for acquiring a sensor data stream, an MES service data packet and an environment monitoring data set and generating a space-time aligned fusion data frame; the dynamic topology compensation modeling module is used for constructing a digital twin body with a time sequence attribute and outputting a dynamic topology relation matrix comprising a geometric deviation compensation value; the event-driven incremental rendering engine module is used for generating a visual model only updating a change part by adopting a difference region recognition technology, and outputting a three-dimensional visual interface with an abnormal coordinate mark; and the bidirectional feedback control channel module is used for analyzing the abnormal coordinate mark, generating an equipment parameter adjustment instruction set through a reverse path tracking algorithm, and transmitting the equipment parameter adjustment instruction set back to the physical equipment controller. According to the invention, a bidirectional feedback closed loop with a physical equipment controller is realized, and dynamic adjustment and adaptive control are supported.

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 discloses a rope-driven mechanical arm motion control method based on model predictive control, which comprises the following steps: taking a joint angle error as a state variable, taking a rope-driven rotation angle as an input variable, and establishing a nonlinear state-space equation of a rope-driven mechanical arm according to the state variable and the input variable; the nonlinear state-space equation is subjected to linearization processing, a linear approximation model is generated, and the linear approximation model comprises a linearization matrix; defining a prediction interval, expanding the linear approximation model into a multi-step prediction form, and generating a prediction model; constructing a cost function, and combining the prediction model into the cost function to solve and obtain an optimal control sequence; and extracting the optimal control increment at the current moment from the optimal control sequence, and updating the state variable, the input variable and the linearization matrix according to the optimal control increment to form closed-loop control. Accurate path tracking control can be carried out on the rope-driven mechanical arm under complex dynamic constraints, and it is ensured that the mechanical arm moves according to the planned trajectory.

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 a man-machine dynamic cooperative steering control method integrating reinforcement learning and a game theory, and aims to optimize dynamic weight distribution of a driver and a controller so as to guarantee driving safety and path tracking precision of a man-machine co-driving vehicle. The invention relates to the field of intelligent driving. The system comprises an expected path module, a reinforcement learning module, a non-cooperative game module and a man-machine co-driving vehicle module. The expected path module is used for generating an expected path sequence of the driver and the controller and an expected path at the current moment; the reinforcement learning module receives the expected path at the current moment, the vehicle dynamic information and the optimal rotation angle of the driver and the controller, and training is carried out to generate a driver control weight; and the non-cooperative game module combines the driver control weight, the expected path sequence of the driver and the controller and the state information of the vehicle, optimizes the optimal steering angle of the driver and the controller through the non-cooperative game, and realizes the cooperative steering control of the man-machine co-driving vehicle.

The invention belongs to the technical field of civil aviation automatic driving safety detection, and discloses a civil aviation airport apron airplane vehicle collision detection and prevention method and system. The method comprises the steps of super-large geographical range data fusion, target path prediction, target path tracking, danger approach prompt, smoothing of business data, collision detection of aircraft and vehicle operation and avoidance operation in various modes according to priority under the condition of full evaluation of delay and jitter of various data sources. The method breaks through the traditional limitation, formulates a scientific evaluation standard, optimizes the complex data processing capability, balances the real-time performance and accuracy, avoids the reduction of the guarantee capability caused by the traditional simple waiting flight, provides the precise data to support the civil aviation operation for the airport with the busy airport apron operation, provides the higher-efficiency guarantee support, and improves the safety of the airport. And the decision-making efficiency and the operation management level are obviously improved.

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 is suitable for the technical field of automatic driving vehicle control, and provides a full-state control method for dynamic drifting of a distributed driving vehicle, which comprises the following steps of: tracking a reference path to obtain an expected state quantity; establishing a dynamic error of a full state to obtain expected state derivatives of the velocity, the side slip angle and the yaw velocity; calculating a rear tire force weight, and implementing rear tire force constraint of the distributed driving vehicle; inverting the vehicle dynamics model to obtain expected longitudinal force and lateral force of each wheel; performing tire model inversion to obtain an expected front wheel slip angle and wheel speed of each wheel; and according to the expected front wheel slip angle and the wheel speed of each wheel, an actuator is adjusted, and control instructions of the steering wheel angle and the torque of each wheel are generated. The state of the vehicle can be accurately controlled, the maneuverability of the vehicle is good during conventional turning and limit drifting, and the vehicle can flexibly cope with complex working conditions; the stability and safety of the vehicle are enhanced, and the full-state dynamic drift control of the distributed driving vehicle is realized.

The invention relates to the field of power optical cable facilities, in particular to a power optical cable fault detecting and positioning method based on an extreme learning machine. The OTDR signal is subjected to multi-scale decomposition and de-noising processing based on wavelet packet analysis, and high-frequency noise is effectively suppressed and key event point features are reserved by refining frequency band division, so that the signal quality is improved. And constructing a fault classification model based on an extreme learning machine, and realizing efficient classification and fault mode identification of de-noised OTDR data. And in combination with a white whale optimization algorithm (BWO), carrying out optimization search on a fault detection result output by the classification model so as to improve the detection precision and algorithm robustness in a complex optical cable environment. And an improved Dijkstra algorithm is introduced to realize accurate positioning and path tracking, so that the accuracy and response speed of fault positioning are remarkably improved. Through multi-level algorithm fusion, the problems of signal noise interference, insufficient pattern recognition accuracy and low positioning precision in a complex network environment in optical cable fault detection are solved.

The invention discloses a synchronous localization and mapping method based on PID real-time correction fusion vision, and relates to the technical field of automatic control and environmental perception, and the method comprises the steps: collecting the environmental data of a robot in real time, and carrying out the preprocessing; the direction angle of each feature point is extracted and calculated based on the environment data, and path information is calculated; a state space and an action space are defined based on the path information, and a control signal of the robot is adjusted through an SAC-PID controller; and performing visual SLAM by using ORB-SLAM to output a global consistent map. According to the method, the robot path is intelligently optimized and adjusted and dynamically corrected through the SAC-PID controller combined with reinforcement learning, so that the precision and stability of path tracking are improved, a global consistent map is constructed and optimized through the ORB-SLAM technology, the navigation ability of the robot in a complex dynamic environment is further improved, and the navigation efficiency of the robot in a complex dynamic environment is improved. The self-adaptability of the controller is enhanced, the environment perception technology is optimized, and the defects of insufficient control precision and poor map consistency in the prior art are overcome.

A conflict control method for shared driving, and an electronic device are provided. The method includes: establishing, based on a deterministic steering torque of a driver and a stochastic steering torque of the driver, a game model for human-machine path tracking control corresponding to a human-machine interaction behavior; obtaining human-machine torque conflict information by solving the game model for human-machine path tracking control; determining a shared control strategy according to the human-machine torque conflict information; and controlling a shared driving vehicle according to the shared control strategy.

The invention discloses an intelligent vehicle transverse and longitudinal cooperative personalized control method based on reinforcement learning, and the method comprises the steps: 1, obtaining target path information and vehicle state information, and constructing an intelligent body state space; 2, vehicle control parameters are obtained, and an intelligent agent action space is constructed; 3, constructing a vehicle dynamics safety boundary according to the vehicle dynamics model; 4, according to different driving style types, designing differentiated reward functions in combination with path tracking targets and dynamic safety boundary constraints; and 5, constructing a strategy network and a value network, and training the strategy network and the value network based on the reward functions of the three driving styles to obtain an optimal control model. According to the method, the transverse and longitudinal cooperative control model considering the dynamic factors and the driving styles is trained by using the reinforcement learning method, personalized control requirements are met, and meanwhile, the control precision and stability of the intelligent vehicle are improved.