1676 results about "Route planning" patented technology

The invention relates to the field of airline planning, and discloses a digital twin system for low-altitude airline planning and a construction method, and the method comprises the steps: obtaining a spatial perception data set of a preset sensor in a target airspace, carrying out the fusion of the spatial perception data set, obtaining the fusion perception data, obtaining the historical flight hidden danger data of the target airspace, and obtaining the historical flight hidden danger data of the target airspace; performing multi-layer convolution on the fusion perception data by using the historical flight hidden danger data to obtain a risk analysis result, performing fuzzy fusion on preset risk factors, generating a dynamic obstacle risk field according to the risk factors after fuzzy fusion and the risk analysis result, performing flight simulation on the dynamic obstacle risk field, and obtaining a flight simulation result; and collecting flight simulation real-time data in the flight simulation process, generating an air route space cost map according to the flight simulation real-time data, and performing optimal path search on the air route space cost map to obtain a low-altitude air route scheme. In the face of airspace actual risks, the searching efficiency of the optimal path in low-altitude route planning is improved.

The invention discloses a reinforcement learning unmanned aerial vehicle route planning method and system based on a safe flight corridor, and relates to the technical field of route planning. A safe flight corridor is generated through a grid map of a target scene and starting point information; calculating a plurality of initial B spline control points through observation information of the unmanned aerial vehicle; iteratively generating a new B-spline control point based on the plurality of initial B-spline control points, the key features extracted based on the safe flight corridor and the time variable through a reinforcement learning algorithm; and generating a B spline path of the unmanned aerial vehicle according to all B spline control points. According to the invention, a feasible flight area in a complex environment is abstracted as a safe flight corridor. Environment characteristics are hidden in the safe flight corridor, and common application in different complex environments can be achieved through training in one environment. A B-spline control point is iteratively generated by using a reinforcement learning algorithm, and environment change can be iteratively adapted in real time, so that the generated B-spline path better meets the actual flight demand of the unmanned aerial vehicle.

In various examples, a technique for generating a path between a current location and a target waypoint is disclosed that includes receiving a route plan that is associated with a plurality of waypoints representing locations in a physical environment. The technique also includes identifying a search space that includes the route plan, and identifying a target waypoint of the plurality of waypoints—the target waypoint being in a portion of the search space between a current location of a mobile robot and an end waypoint of the route plan. A path between the current location of the mobile robot and the target waypoint may then be generated.

The invention discloses an unmanned aerial vehicle optimal path self-adaptive planning method adopting particle swarm optimization, and relates to the field of unmanned aerial vehicle path planning, and the method comprises the steps: constructing a space constraint model of an unmanned aerial vehicle flight task; initializing a particle swarm based on the spatial constraint model; combining the path smoothness, the path threat probability and the voyage efficiency to establish a multi-target fitness function, and calculating the fitness value of each particle based on a particle swarm; hierarchical particle swarm optimization iteration is executed, in each iteration, control parameters are adaptively adjusted based on the current particle swarm distribution characteristics, and the path node positions of particles are updated; and when a convergence condition is satisfied, outputting a global optimal path as a final flight path of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to execute. According to the unmanned aerial vehicle optimal path self-adaptive planning method based on particle swarm optimization, the problems that the unmanned aerial vehicle route path optimization target is single and difficult to cooperate, and the route planning effect is poor are solved.

The invention discloses a line planning method and system, and relates to the technical field of electric power line inspection. According to the line planning method, a plurality of feasible inspection paths are generated by obtaining a list of tasks to be inspected and three-dimensional coordinates of an electric tower and an inspection part of the electric tower, combining regional environment data, constructing a semantic knowledge graph and a spatial topological structure chart, determining an optimal path through evaluation, screening and analysis, and transmitting the optimal path to an unmanned aerial vehicle to execute an inspection task. According to the method, the flight state is monitored in real time, the abnormal condition is found in time, and the residual path is dynamically replanned in the inspection process, so that the smooth execution of the inspection task is ensured. The flight state and the environment interference of the unmanned aerial vehicle are monitored in real time, and the dynamic path is replanned according to the actual condition; according to the method, the flexibility and emergency response capacity of the inspection task are remarkably improved, the efficiency and safety of the inspection task are effectively improved by dynamically adjusting the path, and the adaptive capacity of the unmanned aerial vehicle in a complex environment is enhanced.

The invention discloses an intelligent warehouse management method and system for hazardous wastes, and the method comprises the steps: carrying out the real-time monitoring of the state of an object in a hazardous waste warehouse through a deployment sensor network, obtaining the basic data of the position of the object and the environment parameter, and transmitting the basic data to a central processing platform, thereby obtaining a preliminary data set; performing integration processing on multi-source information by adopting a data fusion technology according to the preliminary data set, eliminating redundancy and noise, and determining a standardized data record; aiming at the standardized data record, dynamically analyzing the distribution of goods locations in the warehouse by utilizing a path optimization algorithm, and if the change of the occupation state of the goods locations is detected, adjusting a goods location allocation scheme in real time, and outputting an optimal storage location combination; according to the optimal storage position combination, a dynamic route planning scheme is generated in combination with warehouse layout information, and the optimal moving track of the operation equipment is obtained; according to the invention, intelligent management of the hazardous waste warehouse is realized, and the storage efficiency and safety are improved.

The invention discloses an electric power communication multi-route intelligent planning method and device based on scene classification. The multi-route intelligent planning method in the electric power field comprises the steps that S1, multi-source heterogeneous data fusion processing is carried out; s2, classifying three-dimensional scene features; s3, dynamic routing modeling is carried out; s4, a hybrid intelligent optimization algorithm; and S5, real-time verification and feedback optimization are carried out. According to the multi-route intelligent planning method in the electric power field, the problems of static route planning, poor adaptability and insufficient multi-source data fusion capability in a traditional electric power communication network are solved, intelligent perception and dynamic adaptation to a complex communication environment are realized, and the multi-source data fusion capability is improved. And the network resource scheduling efficiency, the flexibility of the routing strategy and the overall service quality are improved.

The invention discloses a method and a system for processing route planning data for electric power line inspection. The method comprises the following steps: acquiring line distribution and topographic feature data; gridding the power network region, determining the patrol priority, and generating a region priority sequence; the overall patrol task is decomposed, a plurality of task subsets are generated, one task is allocated to each unmanned aerial vehicle, and each task subset comprises an initial position and a target line section; generating an initial route path based on the topographic features; comprehensively analyzing all initial paths, identifying potential airspace intersections and performing conflict prediction, and generating a conflict-free route path set by performing height hierarchical adjustment on related paths; and calculating predicted flight time and energy consumption of each unmanned aerial vehicle, redistributing boundary tasks according to a load balancing principle, and finally generating a balanced multi-unmanned aerial vehicle cooperative route scheme. According to the method, the safe and efficient multi-machine collaborative inspection route can be automatically generated through systematic task decomposition, conflict prediction and load balancing optimization.

The invention provides an accompanying robot scenic spot route intelligent planning management method driven by reinforcement learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an initial touring path through a reinforcement learning strategy based on user physiological data, scenic spot opening time constraint and real-time people flow information, and dynamically adjusting the path according to the real-time physiological monitoring signal, optimizing the residence time, and outputting a physiological adaptive path. According to the invention, the service intelligence level and the user experience of the accompanying robot are improved.

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

The invention discloses an unmanned aerial vehicle route planning method for low-altitude surveying and mapping, and relates to the technical field of unmanned aerial vehicles. The method comprises the following steps: regularizing and partitioning a surveying and mapping area to obtain surveying and mapping sub-areas, acquiring topographic feature data and demand data of the surveying and mapping sub-areas, and calculating priority values of the surveying and mapping sub-areas; unmanned aerial vehicle performance data and surveying and mapping sub-area performance demand data are collected, single-parameter performance matching is carried out to obtain feature matching values, and an unmanned aerial vehicle surveying and mapping sub-area adaptation matrix is constructed by combining the surveying and mapping sub-area priority values; based on the unmanned aerial vehicle surveying and mapping sub-area adaptation matrix, distributing unmanned aerial vehicles through a Hungary algorithm and generating a local initial route; constructing a flight path conflict analysis model through the terrain factors, and inputting the local initial flight path into the model to obtain an adjusted flight path of the unmanned aerial vehicle; when the unmanned aerial vehicle executes a task, the operation state and progress data are collected in real time, a risk value is calculated, and if the risk value exceeds the threshold value, early warning is performed and the unmanned aerial vehicle is exchanged to realize safe and efficient route planning.

Disclosed is route planning using a worst-case risk analysis and, if needed, a best-case risk analysis of GNSS coverage. The worst-case risk analysis identifies cuboids or 2d regions through which a vehicle can be routed with assurance that adequate GNSS coverage will be available regardless of the time of day that the vehicle travels. The best-case risk analysis identifies cuboids or 2d regions through which there is adequate coverage at some times during the day. In case path finding using the worst-case risk analysis fails, a best-case risk analysis can be requested and used to find alternate potential path(s). Time dependent forecast data that covers regions along the alternate potential path(s) can be requested and used to route vehicles, including autonomous drones, from starting points to destinations. This includes generation, distribution and use of risk analysis data, implemented as methods, systems and articles of manufacture.

A navigation route planning apparatus includes processing circuitry configured to receive a planned route indicating a route and a current course direction of a movable body, receive movable body information that includes a position, a travelling direction, and a speed of the own ship, receive obstacle information that includes a position, a traveling direction, and a speed of an obstacle, determine a collision risk value associated with the planned route along the current course direction based on the movable body information and the obstacle information, and determine whether the movable body requires to at least one of evade the planned route and continue traversing on the planned route based on the collision risk value.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

The invention discloses an intelligent parking space guiding system for a hospital parking lot, relates to the technical field of intelligent traffic, and is used for solving the problems that the hospital parking lot is seriously jammed in peak hours and special vehicles lack exclusive passing authority. The system comprises a parking space monitoring module, a vehicle identification module, an intelligent guiding module and a user interaction module. The parking space state is monitored in real time through the multi-mode sensor array; recognizing entering and exiting vehicles through a deep learning recognition algorithm based on cameras deployed at entrances and exits, and obtaining corresponding vehicle information; based on parking space state information and vehicle identification information, a route optimal value is dynamically calculated, and a whole-course navigation service from an entrance to a target parking space is provided for a driver, so that accurate detection of the parking space state, special vehicle priority distribution, high-low peak dynamic route planning and whole-process intelligent guidance are realized, the hospital parking efficiency is remarkably improved, and the hospital parking experience is improved. And the congestion condition in the peak period is relieved.

The invention relates to the technical field of unmanned aerial vehicle low-altitude flight safety monitoring, in particular to a low-altitude safe flight data monitoring system and method based on multi-source perception. The specific implementation process comprises the following steps: constructing a feature vector analysis module, fusing multi-source heterogeneous data through a space-time correlation model, and analyzing a flight feature vector; constructing a safe flight route planning module, and planning a safe flight route on the drawn flight safety map according to the flight feature vector; a risk factor correction module is constructed, when it is monitored that the deviation of the flight route exceeds a threshold value, a risk factor causing the deviation is deduced and quantified into a correction parameter, and dynamic re-planning of the flight route is triggered. According to the method, the flight feature vector and the flight safety map are combined, so that the low-altitude flight data of the unmanned aerial vehicle are monitored, and the continuous safety and the risk avoidance capability of the unmanned aerial vehicle in task execution in a complex, dynamic and unknown environment are effectively improved.

The invention discloses a polar region sea area ice area route dynamic planning method based on multi-source data, and the method comprises the following steps: obtaining sea ice, ocean current and wind field historical data of a target channel sea area, and carrying out the preprocessing; inputting a preprocessing result into a constructed sea ice forecasting model to obtain a sea ice concentration and sea ice thickness distribution forecasting result of a planning day; combining the forecast result with ocean current and wind field forecast data, and utilizing a pre-established random forest navigational speed reasoning model to obtain target channel sea area planning daily navigational speed space distribution data; and carrying out route planning with minimum time cost through an improved A * algorithm to obtain a dynamic planning result of the target channel sea area route. The invention relates to the technical field of ship navigation in a sea ice area, and relates to the technical field of ship navigation in the sea ice area, a polar sea area navigation speed background field is deduced according to a fitting relationship through random forest modeling and fitting of the relationship between the navigation speed and sea ice, ocean current and a wind field, and dynamic route planning with minimum time cost is carried out.

The invention belongs to the technical field of hydraulic engineering measurement, and discloses a seawall measurement method and system based on multi-source data fusion, and the method comprises the steps: collecting geological data and hydrological data through a data collection module; the route planning module formulates a route; the overwater data acquisition equipment and the underwater data acquisition equipment are used for monitoring; the auxiliary equipment provides a high-precision positioning reference; the meteorological data module obtains meteorological data; the data processing module processes the data; the three-dimensional modeling module carries out point cloud registration and DEM model construction; the comprehensive analysis module performs comprehensive evaluation and predicts potential problems and risks; when an abnormal condition or a potential risk is monitored, the early warning module gives an alarm. Multi-source data acquisition equipment such as unmanned aerial vehicle aerial survey, a laser radar, a multi-beam depth finder, a side-scan sonar, a shallow profiler and a micro-motion exploration instrument are integrated, seawall overwater and underwater full-dimensional data acquisition, processing, modeling and volume calculation are realized, and high precision and reliability of measurement results are ensured.

The invention discloses an unmanned aerial vehicle target tracking and intelligent route planning method based on YOLO and DSM, and the method comprises the steps: enabling an unmanned aerial vehicle to collect video streaming, thermal imaging and distance information in real time through a multi-modal sensor, fusing the multi-modal data through Kalman filtering, detecting a target through a YOLOv11 model, and outputting the position, category and confidence information of the target; a DeepSORT algorithm is adopted to continuously track a target, Kalman filtering is combined to predict a target motion trajectory, and a Hungary algorithm is adopted to realize matching of target detection and a tracking trajectory, so that stable tracking in a complex environment is ensured; the flight path of the unmanned aerial vehicle is planned by using terrain elevation information provided by the DSM and combining with an improved A * algorithm, so that the unmanned aerial vehicle is ensured to avoid obstacles and keep continuous tracking of a target in a complex terrain environment; through a reinforcement learning algorithm, the unmanned aerial vehicle can dynamically adjust the route planning according to the motion state of the target and the environment change, and it is ensured that the system can automatically re-plan the route when the target is lost or the environment obstacle is newly added.

The invention provides a low-airspace public route construction method, device and equipment, and relates to the technical field of aircraft airspace route planning. The method comprises the following steps: determining a plurality of target candidate nodes corresponding to a city based on ground traffic topological characteristics of the city corresponding to a low airspace and spatial distribution characteristics of low-risk ground features; performing triangulation on the plurality of target candidate nodes to obtain an initial edge set; wherein the initial edge set comprises a plurality of initial edges; performing sparse processing on the plurality of initial edges based on a greedy tensor graph algorithm, and determining a plurality of target edges from the plurality of initial edges; wherein the length of the shortest path between the two nodes of the target edge is greater than the greedy tensor coefficient times of the Euclidean distance between the two nodes; and constructing a low-airspace public route based on the plurality of target edges. By adopting the technical scheme provided by the invention, the utilization efficiency and safety of the low airspace can be effectively improved by constructing the unified low airspace public route.

The invention discloses an e-commerce storage order real-time picking path planning method and system, and relates to the technical field of intelligent storage and logistics automation, and the method comprises the following steps: training a behavior prediction model based on historical picking behavior data, and outputting a path candidate region of a picker; constructing a path game model according to the path candidate area in combination with the current order task; based on a Stackelberg mechanism, solving a game equilibrium path distribution result; according to a solving result, evaluating the candidate path scheme through a multi-objective optimization algorithm, and outputting an optimal path scheduling strategy; according to the method, by introducing path candidate region extraction based on behavior prediction, fusion graph construction, a Stackelberg hierarchical game solving mechanism and multi-target optimization strategy output, real-time performance and global collaborative optimization of order picking path selection are realized.

The invention relates to the technical field of unmanned aerial vehicle anti-collision path planning, in particular to an unmanned aerial vehicle ad hoc network anti-collision path planning method based on a graph neural network, and the method comprises the steps: obtaining the topographic data of a task airspace, recording the initial positions, target positions and motion constraint conditions of all unmanned aerial vehicles, and constructing unmanned aerial vehicle graph structure data; according to the unmanned aerial vehicle graph structure data, taking each unmanned aerial vehicle as a unit node, and representing a potential collision relationship between two adjacent unmanned aerial vehicles by a side between the nodes; initializing features of the nodes and the edges, and completing training of an unmanned aerial vehicle graph neural network model based on the input features of the nodes and the edges as input of the preset unmanned aerial vehicle graph neural network model; and performing collision risk prediction based on the unmanned aerial vehicle graph neural network model, and generating and outputting an optimal flight path. According to the invention, through the graph neural network and multi-objective optimization, intelligent cooperative flight of the unmanned aerial vehicle group is realized, the collision risk and energy consumption are significantly reduced, and the network connectivity and task efficiency are ensured at the same time.

The invention discloses an urban low-altitude-oriented grid dynamic evaluation and intelligent route planning system, and belongs to the field of urban low-altitude airspace and routes. And the data processing module is used for inputting static data including the task number, the task time, the unmanned aerial vehicle number, the take-off and landing field number and the total airspace, the continuous airspace is divided into grid units by a project, and a quantitative evaluation model with seven factors including the underlying surface, the communication quality, the navigation quality, the meteorological environment, the electromagnetic environment, the noise and the countering as the core is constructed. The airworthiness score of each grid is calculated through normalization processing and weighted summation, a real-time and scientific decision basis is provided for path planning of the unmanned aerial vehicle, crossing from static planning to dynamic evaluation is achieved, and a set of airspace gridding dynamic evaluation and task planning system is provided and implemented. The system aims to cope with flight risks caused by multiple factors such as complex underlying surfaces, dynamic weather and communication navigation interference in urban low-altitude flight, and the safety and reliability of unmanned aerial vehicle logistics are improved.

The invention discloses a path planning method and system for corner optimization. The method and system are used for cutter radius compensation and machining instruction smooth transition of a three-dimensional path of a numerical control machine tool. The method comprises the steps that a machining instruction is pre-read, and a path point location is analyzed; calculating a path direction vector and a normal vector, and determining a path plane equation; calculating a plane included angle and an intersection direction vector through a normal vector; generating a compensation curve by translating along the perpendicular direction of the intersecting line by taking the radius of the cutter as a constraint; and generating a smooth control instruction through curve subdivision and inserting the instruction into an instruction queue. The problem that three-dimensional path corner transition is not smooth is solved, the machining precision and efficiency are remarkably improved, and the method is suitable for a numerical control machine tool for complex curved surface machining.

The invention discloses a power grid inspection method, system and equipment. The method comprises the following steps: acquiring multi-dimensional data of power equipment by constructing a multi-source heterogeneous sensor array; according to the obtained multi-dimensional data of the power equipment, the to-be-inspected power monitoring points are connected in series to generate an initial inspection line; calculating and optimizing line configuration based on the electric power key data identifier and the inspection duration; and dynamically adjusting the inspection path through the reinforcement learning model, and generating an intelligent inspection decision scheme fusing the equipment health degree. According to the method, the power grid inspection efficiency is optimized through technology fusion, the detection precision is improved, and deep cooperation is formed between inspection path planning and task scheduling.

The invention relates to the technical field of ship navigation, and discloses a ship navigation route optimization method and system. The method comprises a meteorological change sequence, an ocean current fluctuation sequence and channel obstacle distribution data. Then carrying out two-dimensional route topology construction processing to generate route distribution characteristics; calling a pre-trained path optimization model to carry out nonlinear path analysis processing to obtain an optimal route prediction value and a key risk area identifier; performing navigation environment compensation correction processing on the optimal route prediction value based on the association relationship between the meteorological change sequence and the ship control sensitivity to generate a corrected optimal route prediction value; and finally, generating a course maintenance strategy set including a course adjustment path optimization scheme and a speed dynamic regulation and control processing scheme according to the key risk area identifier. According to the method, dynamic environmental factors can be comprehensively processed, the accuracy and adaptability of route planning are improved, risks are effectively identified and avoided, and high efficiency and safety of ship navigation are guaranteed.

The invention relates to the technical field of smart park integrated management, and discloses a smart park comprehensive management and control system and method based on multi-modal data analysis, and the method comprises the steps: obtaining a park multi-dimensional real-time data flow, and extracting a current situation feature vector and a personnel distribution thermodynamic diagram; analyzing the historical emergency event data sequence by using a space-time convolutional neural network, and outputting an event type identification vector; and generating a dynamic evacuation path planning and resource scheduling scheme based on a graph optimization algorithm, constructing a minimum cost flow optimization problem taking the weighted sum of time cost and waiting time as a target function, and solving to obtain an optimal people flow allocation scheme. The problem that emergency response cannot adapt to dynamic environment changes is solved. Experiential knowledge cannot be effectively inherited and utilized. And the emergency response system is slow in response and improper in resource allocation.

The invention discloses a low-drift laser scanning control method and system based on dynamic error compensation, relates to the technical field of laser scanning control, and aims to solve the drift problem in the laser scanning process and improve the scanning precision. The system comprises a system initialization and calibration module, a data acquisition and storage module, an error modeling module, a route planning and correction module and a real-time compensation and closed-loop optimization module, wherein the error modeling module and the route planning and correction module are key modules. The error modeling module comprises a key route screening unit and a data preprocessing unit, and is responsible for constructing a space-time joint error correction model; the route planning and correction module comprises an initial route generation unit, an error prediction unit and the like, and dynamic correction of a pre-planned route is achieved. According to the system, real-time compensation and closed-loop optimization are achieved through cooperation of all the modules in combination with a dynamic error compensation algorithm, scanning drift is effectively reduced, low-drift and high-precision laser scanning is guaranteed, and meanwhile follow-up scanning precision is optimized.

Apparatuses, systems, and techniques for enhancing autonomous driving systems. In at least one embodiment, visual input corresponding to an observable environment is tokenized into object-level knowledge and provided to a large language model (LLM). Object-level tokens are processed by the LLM to enhance autonomous vehicle route-planning, reducing trajectory error and decreasing collision rates.

The invention discloses a ship route planning method and system in a polar region sea ice environment and a program product. The ship route planning method comprises the steps of preprocessing satellite remote sensing radiation intensity data, performing image reconstruction of inter-ice water channel detection through an improved Vision Transform model, planning and visualizing a ship route and the like. The deep features of the image blocks are extracted through a Transformer encoder which comprises a multi-head self-attention mechanism and is processed by a multi-layer perceptron; and the Transform encoder firstly performs layer normalization on the input, and then performs multi-head attention mechanism and multiple linear transformation and regularization processing. And realizing path planning in an inter-ice water channel environment by fusing a dynamic planning cost function through a fast marching method. Compared with a traditional algorithm, the method has the advantages that the calculation precision is higher in the same scene, the calculation efficiency is greatly improved, and the planning result obtained after multiple costs are comprehensively considered is given instead of pursuing the shortest distance.