1099 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 belongs to the technical field of ship path tracking, and discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID, and the method comprises the steps: presetting a path point, and obtaining a real-time coordinate; obtaining an actual expected heading angle # imgabs0 # and a ship observation heading angle # imgabs1 #, subtracting the actual expected heading angle # imgabs0 # and the ship observation heading angle # imgabs1 # to obtain a heading deviation # imgabs2 #, and calculating a heading deviation rate # imgabs3 #; the course deviation and the course deviation rate are input into a fuzzy self-adaptive PID motion control module, and gain parameters are output through a fuzzy control algorithm; the gain parameter and # imgabs4 # are input into a PID controller to calculate a ship rudder angle # imgabs5 #; inputting # imgabs6 # into the motion mathematical model to update the motion state, and forming a closed-loop updated motion state to enable the ship to approach a preset path point; and judging a ship path after the state information is updated. The expected course of the ship is calculated in real time and the sideslip angle of the ship and the control parameters of the ship are adjusted in real time through self-adaptive sight guidance, and the control parameters are automatically adjusted in real time by means of the fuzzy self-adaptive PID motion control module to achieve accurate motion control, so that the ship travels according to a preset planned path.

The invention provides a multi-AGV path planning and obstacle avoidance method based on an improved artificial bee colony algorithm, and aims to solve the key technical challenges of multi-AGV system path planning and obstacle avoidance in modern industrial automation. According to the method, a dynamic updatable gridding environment model is constructed to accurately represent a starting point, an end point, static obstacles and dynamic obstacles in a working environment, an improved artificial bee colony algorithm is adopted to globally optimize path planning of multiple AGVs, constraint conditions of obstacle avoidance and multi-AGV path conflicts are met, and the optimal path planning of the multiple AGVs is realized. And the efficient operation of the system and the path collision-free property are ensured. In addition, the method further comprises path tracking control and conflict detection and obstacle avoidance control so as to ensure that the AGVs can accurately run along the planned path, conflict points in the paths of the multiple AGVs are detected in real time, and different coping strategies are adopted to achieve path coordination and scheduling. Finally, the cooperation efficiency and safety of the multi-AGV system can be effectively improved.

The invention relates to the field of intelligent mobile equipment control, and discloses a self-adaptive obstacle avoidance control system and an application method thereof, and the system comprises a data collection module, an environment modeling module, an obstacle segmentation module, a path planning module and a motion control module. The data acquisition module is used for acquiring multi-modal data of the surrounding environment of the vehicle; the environment modeling module fuses the multi-modal data, and generates a dynamic environment map including the spatial position, the dynamic characteristic and the free passing area of the obstacle; the obstacle segmentation module is used for segmenting the dynamic environment map and identifying an obstacle area and a free passing area; the path planning module calculates an optimal path with the minimum cost; the motion control module adjusts the motion state of the vehicle in real time through a PID control algorithm or a model prediction control method, and path tracking and dynamic obstacle avoidance are achieved. The method has high-precision environment perception capability, flexible path planning capability and efficient dynamic obstacle avoidance control capability, and can be widely applied to the fields of industrial automation and intelligent logistics.

The invention relates to the technical field of transformer substation monitoring and early warning, in particular to a monitoring and early warning method for an intelligent transformer substation and a control system of the monitoring and early warning method. And comprehensive coverage of the equipment state and the environment data is realized. Time sequence modeling is carried out through the long short-term memory network (LSTM), the dynamic trend of operation parameters is effectively captured, potential anomalies are recognized, the accuracy of equipment anomaly judgment is remarkably improved by combining the semantic segmentation technology and equipment type feature extraction, and the false alarm rate and the missing report rate are reduced. Through combination of a rotatable camera and a convolutional neural network (CNN) and multi-modal data fusion, accurate personnel identity recognition and behavior path tracking are realized, non-staff and abnormal behaviors are intelligently detected, and potential security threats are early warned in time.

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 discloses a robot dynamic path tracking method and system, and the method comprises the steps: constructing a nonlinear model prediction control model for a differential robot chassis; building a target cost function by considering the obstacle environment information around the robot; adjusting a control input vector in the nonlinear model predictive control model, performing real-time tracking control on the robot, calculating a target cost function value, taking the corresponding control input vector as an optimal control input vector when the target cost function value is minimum, and performing real-time tracking control on the robot. Performing dynamic path tracking control on the robot by using the optimal control input vector; the method has the advantages that real-time obstacle avoidance is reliably carried out while the robot tracks the path, complicated path planning calculation steps are not needed, the calculation process is simple, and the efficiency is high.

The invention relates to the technical field of intelligent unmanned sweeping, in particular to an unmanned sweeping vehicle edge path tracking control method and system. Through cooperative work of a path planning and edge feature detection module, a real-time positioning and attitude estimation module, an edge tracking control module, an obstacle detection and avoidance module, a path correction and feedback module and an energy consumption optimization and endurance control module, precise tracking of the sweeper on an edge path in a complex environment is realized. Through multi-sensor data fusion, extended Kalman filtering and a self-adaptive path tracking algorithm, the system can adjust the driving state of the motor sweeper in real time, it is ensured that the motor sweeper is driven accurately along the edge, through a dynamic path planning algorithm and an energy consumption optimization model, the ability of the motor sweeper in the aspects of obstacle avoidance, path correction and endurance control is effectively improved, and the motor sweeper driving stability is improved. Therefore, the working efficiency and the endurance time are improved on the premise that the cleaning task requirement is met. The device is suitable for various complex sweeping environments, and efficient edge sweeping operation is achieved.

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 an automatic driving vehicle steering and suspension cooperative control method based on reinforcement learning, and the method specifically comprises the following steps: 1, building a dynamic model of a vehicle steering system and a suspension system, and obtaining transverse and vertical vehicle motion states through controlling a steering angle and a suspension acting force; 2, constructing a control action and reward function of a vehicle steering system and a suspension system, and forming a reinforcement learning exploration strategy of vehicle steering and suspension cooperative control; 3, constructing intelligent agents of a steering system and a suspension system; and 4, constructing a reinforcement learning environment, and realizing cooperative control of the steering system and the suspension system through a multi-agent reinforcement learning method. According to the method, different vertical and transverse plane environments and different learning tasks are comprehensively considered, the data input dimension is reduced, the optimal motion control strategies under different working conditions are considered, and on the premise that accurate path tracking is guaranteed, the transverse roll stability and the vertical smoothness and comfort are improved through cooperative steering system and suspension system control.

The invention provides an unknown environment adaptive navigation control system based on deep learning, and aims to improve the autonomous navigation capability of a machine in a complex and unknown environment. According to the system, an advanced sensing module is adopted, multi-sensor data are combined, and a dynamic environment is quickly and accurately sensed through a deep learning algorithm. The online environment modeling mechanism can update the environment information in real time without depending on a pre-constructed map. And by utilizing a reinforcement learning algorithm, the system can autonomously learn and optimize path planning, and meanwhile, energy consumption, time efficiency and safety are considered. An innovative multi-mode obstacle avoidance strategy is adopted, and obstacle avoidance behaviors are dynamically adjusted according to environment complexity and machine states. The intelligent decision support system combines a decision tree and fuzzy logic, and provides fast and accurate decision ability. The adaptive motion control algorithm optimizes path tracking performance and adapts to dynamic characteristics and environmental conditions of a machine. In addition, the collaborative job mechanism supports efficient communication and task collaboration in a multi-machine system.

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 provides a hovercraft path tracking method and device based on reinforcement learning, equipment and a storage medium, and the method comprises the steps: obtaining the initial positions and motion information of a hovercraft and a dock landing ship, and determining the expected path of the hovercraft based on the initial positions and motion information; calculating a target heading angle of the hovercraft in the expected path based on a first guidance algorithm; taking the target heading angle as input of a reinforcement learning network and an anti-interference controller, generating a first control parameter, training the first control parameter based on a first neural network, and generating a target control parameter; and controlling the hovercraft to move according to the expected path based on the target control parameter.

The invention relates to a path tracking control method fusing reinforcement learning and adaptive preview, which is used for coping with the change of a dynamic environment and solving the problem of complex multi-target path tracking. The invention relates to the field of intelligent driving. The method comprises an upper layer and a lower layer, the upper layer determines an adaptive preview distance through reinforcement learning and inputs the distance to the lower layer, and the lower layer determines a control action through optimization solution and controls a vehicle to realize intelligent path tracking. Wherein the upper layer comprises an information acquisition module, an information processing module and a reinforcement learning module, and the lower layer comprises a reference trajectory sequence generation module, a model prediction control module and a transverse action conversion module. The upper layer reinforcement learning module optimizes an adaptive preview distance through interaction with a driving environment; and the lower reference trajectory sequence generation module outputs a reference trajectory sequence based on the self-adaptive preview distance and inputs the reference trajectory sequence into the model prediction control module to obtain control output, the control output is converted into control action through the transverse action conversion module, and the vehicle is controlled to realize intelligent path tracking.

The self-adaptive switching control method for the unmanned surface vehicle comprises the following steps: establishing a mathematical model of the unmanned surface vehicle, and determining a control target of the unmanned surface vehicle; designing a virtual control law and a first Lyapunov alternative function for controlling the unmanned surface vehicle based on a backstepping method; designing a second Lyapunov alternative function, establishing an adaptive neural network controller model and a robust controller model, and designing control input of the unmanned surface vehicle switching control system; designing updating laws of an adaptive neural network controller and a robust controller, and designing a third Lyapunov function to perform stability analysis on the system; and testing and simulating the unmanned surface vehicle system in a simulation environment by using the adjusted control strategy. According to the control method, on the premise of ensuring the stability of the system, the path tracking error of the unmanned surface vehicle is reduced, and the energy consumption of the controller is reduced.

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 invention discloses a wind power unmanned ship path tracking control method based on active disturbance rejection linear quadratic regulation control. The method comprises the following steps: building kinematics and dynamics models based on a ship body physical structure and a system parameter identification method; according to the method, a linear quadratic regulator (LQR) is introduced into an active disturbance rejection control framework, and the strong disturbance compensation capability of the ADRC and the control accuracy of the LQR are combined; an unmanned ship guidance system is constructed based on the model, a dynamic curvature navigation control (DCNC) algorithm is adopted to dynamically adjust the navigational speed, and path tracking is ensured by controlling the course angle. The method for calculating the weight proportion of the LQR coefficient in real time overcomes the system instability caused by environment change in actual engineering application to a certain extent; the ADRC control strategy adopted by the invention effectively improves the robustness and optimality of the system.

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 a distributed driving electric vehicle path tracking control method, system and device, and relates to the technical field of vehicle chassis control. The method comprises the steps that vehicle parameter information is received, a vehicle dynamics model and a magic formula tire model are constructed according to the vehicle parameter information, and a vehicle-road system model used for trajectory tracking control is constructed based on the vehicle dynamics model and the magic formula tire model. According to the method, various uncertain disturbances in the vehicle driving process are comprehensively considered, a model predictive control framework based on minimum-maximum robustness is designed, and the robustness of a path tracking control system is enhanced; meanwhile, an online observer based on a minimum robust positive invariant set is designed, real-time and accurate obtaining of key parameters of the vehicle is achieved without increasing hardware cost, meanwhile, an active front wheel steering and torque vector cooperative control method is designed, and coordinated operation between a steering system and a four-wheel drive system is achieved.

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.