WO2021213540A1 - Procédé de planification d'itinéraire sûr en trois dimensions pour véhicule aérien sans pilote - Google Patents

Procédé de planification d'itinéraire sûr en trois dimensions pour véhicule aérien sans pilote Download PDF

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WO2021213540A1
WO2021213540A1 PCT/CN2021/095252 CN2021095252W WO2021213540A1 WO 2021213540 A1 WO2021213540 A1 WO 2021213540A1 CN 2021095252 W CN2021095252 W CN 2021095252W WO 2021213540 A1 WO2021213540 A1 WO 2021213540A1
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uav
grid
ground
route planning
dimensional
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韩鹏
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中国民航大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the present disclosure belongs to the technical field of UAV route planning, and particularly relates to a three-dimensional UAV safe route planning method.
  • UAV route planning refers to finding the optimal or feasible route from the starting point to the target point and meeting the UAV's performance indicators under certain constraints.
  • UAV route planning technologies There are two types of existing UAV route planning technologies. One is the study of UAV path planning based on unfolding numerical algorithms, such as intelligent algorithms such as bionics or particle swarms; the other is the study of path planning based on graphics algorithms, such as Voronoi diagrams. And Laguerre diagrams and so on.
  • Existing technologies include research on UAV path planning based on intelligent bionic algorithms such as ant colony and genetic algorithm, UAV path planning based on different algorithms such as gravitational search algorithm and particle swarm, and various types of UAV static and dynamic real-time obstacle avoidance route plan.
  • UAV route planning methods there is not yet a route planning method that can effectively take into account the safety risks caused by UAV flight to ground personnel, so that the flight path of UAVs has safety attributes. Reduce ground casualties caused by drone flights.
  • the three-dimensional UAV safe route planning method provided by the present disclosure includes the following steps in sequence:
  • step 1) the three-dimensional rasterization of the drone flight airspace space is to divide the three-dimensional space formed by the flight space into multiple cube-shaped grids; the side length of the grid is determined by the type of the drone. , The design size is determined;
  • UAVs The types of UAVs are divided into fixed-wing UAVs and multi-rotor UAVs;
  • L grid max(L UAV +2R person ,W UAV +2R person ), where L UAV is the wingspan of the fixed-wing UAV, and W UAV is the fixed-wing
  • R person is the average radius of the human body
  • the grid safety factor is defined as the product s of the probability of occurrence of UAV ground impact accidents in the grid and the severity of UAV ground impact accidents;
  • the quantitative index selected for the probability of UAV ground impact accidents is the probability of UAV ground impact accidents per flight hour P U ;
  • the quantitative index selected for the severity of UAV ground impact accidents is the number of UAV ground impact accident casualties per flight hour N f ;
  • the number of UAV ground impact accident casualties per flight hour N f can be expressed as the number of people on the ground affected by the accident
  • the product of N e and the casualty rate P f in the ground impact accident of the UAV per flight hour, the calculation formula is:
  • N f P f ⁇ N e (1)
  • the formula 1 for calculating the casualty rate P f (j) of the grid j in the ground impact accident of the drone per flight hour is:
  • P S (j) is the protection coefficient of ground shelters in grid j, and its value is related to the types of ground shelters in the grid and the grid area.
  • the calculation formula is shown in formula (5); n is the correction factor, take
  • h is the type of ground shelter in Table 1; Protection factor of the ground shield h; h, S h in the surface area of the shield grid j; S j, j is a raster area;
  • Table 1 shows the types of different ground shelters and their protection coefficients
  • the impact area of the UAV ground impact accident as the maximum range of the human cylinder being violated by the UAV cylinder; when only the UAV is falling vertically, the calculation formula of the area A g of the impact area of the ground impact accident is as (6) ), where ru is the equivalent wingspan radius of the UAV, and r p is the radius of the human body;
  • a g 2 ⁇ (r u +2r p ) 2 +( ru +2r p )d (8).
  • step 3 the method for constructing a total route planning cost estimate expectation function based on the grid safety factor obtained in step 2) and the UAV route distance is:
  • the expectation function of the total cost of route planning is composed of two parts, namely the safety evaluation and the distance evaluation;
  • the safety evaluation refers to the sum of the grid safety factors of the drone flight path;
  • the distance evaluation takes the flight path of the drone As the evaluation index;
  • f j is the estimated value of the total cost of route planning from point j to the end point
  • d j is the distance from point j to the end point
  • s j is the grid risk factor of point j
  • ⁇ is the distance heuristic factor, which is used for A coefficient that characterizes the importance of distance
  • ⁇ is a safety heuristic factor, which is a coefficient used to characterize the importance of safety.
  • step 4 the above-mentioned route planning total cost estimate expectation function is used as the objective function of the A* algorithm to improve the A* algorithm, and the improved A* algorithm is used for iterative calculation, and finally the double optimization of route safety and route cost is obtained.
  • the following three-dimensional desired flight path method is:
  • the A* algorithm is improved by taking the above-mentioned route planning total cost evaluation expectation function as the objective function of the A* algorithm. Using the improved A* algorithm, starting from the starting point grid, searching for its barrier-free neighborhood grid, and using the above-mentioned route The estimated total cost of the planning function calculates the reasonable value of the grid in each neighborhood, and selects the most reasonable grid until it reaches the end; after several cycles, the three-dimensional expected flight path is finally obtained after double optimization of airway safety and airway cost. .
  • f k is the evaluation function from the initial node to the target node via node k
  • g k is the actual cost from the initial node to node k in the state space
  • Sk is the acceptable risk from node k to the target node and Estimated cost of distance total cost.
  • Certain implementations of the safe route planning method for the three-dimensional UAV provided by the present disclosure have the following beneficial effects: the probability and severity of the UAV ground impact accident are evaluated, and the safety factor of the three-dimensional space grid of the flight airspace is determined. Construct a total route evaluation function under the dual constraints of grid safety factor and route distance, and carry out safe route planning by improving the A* algorithm. So that the planned three-dimensional UAV route has the function of a safety barrier for ground personnel. In the strategic stage, the serious consequences of drone crashes and injuries were further mitigated, so as to move forward in risk mitigation.
  • Figure 1 is a schematic diagram of the grid safety factor structure in this disclosure.
  • Figure 2 is an analysis diagram of the area affected by the UAV ground impact accident in this disclosure.
  • the safe route planning method for a three-dimensional UAV includes the following steps 1), 2), 3) and 4) in order:
  • the flight airspace of the UAV is determined according to the mission scope of its flight operations.
  • the flight airspace is represented by the latitude and longitude coordinates and the height from the ground on the map.
  • the three-dimensional rasterization of the airspace space of the UAV is to divide the three-dimensional space formed by the flight space into multiple cube-shaped grids.
  • the side length of the grid is determined by the type and design size of the drone.
  • UAVs The types of UAVs are divided into fixed-wing UAVs and multi-rotor UAVs;
  • L grid max(L UAV +2R person ,W UAV +2R person ), where L UAV is the wingspan of the fixed-wing UAV, and W UAV is the fixed-wing
  • R person is the average radius of the human body.
  • the grid safety factor is defined as the product s of the probability of occurrence of a UAV ground impact accident in the grid and the severity of the UAV ground impact accident.
  • the quantitative index selected for the probability of UAV ground impact accidents is the probability of UAV ground impact accidents per flight hour P U ;
  • the quantitative index selected for the severity of UAV ground impact accidents is the number of UAV ground impact accident casualties per flight hour N f ;
  • the number of UAV ground impact accident casualties per flight hour N f can be expressed as the number of people on the ground affected by the accident
  • the product of N e and the casualty rate P f in the ground impact accident of the UAV per flight hour, the calculation formula is:
  • N f P f ⁇ N e (1)
  • the personal injury rate P f in the ground impact accident of the drone per flight hour is related to many factors, among which the factors related to the drone are mainly the altitude and flight speed of the drone, and the factors related to the grid are mainly in the grid.
  • the formula 1 for calculating the casualty rate P f (j) of the grid j in the ground impact accident of the drone per flight hour is:
  • P S (j) is the protection coefficient of ground shelters in grid j, and its value is related to the types of ground shelters in the grid and the grid area.
  • the calculation formula is shown in formula (5); n is the correction factor, take
  • h is the type of ground shelter in Table 1; To protect the coefficient h of the ground shield; S j, h is a grid of the area of the ground shield h; j S j raster area.
  • Table 1 shows the types of different ground shelters and their protection coefficients.
  • Figure 2 is the analysis diagram of the area affected by the UAV ground impact accident. As shown in Figure 2, the area affected by the UAV ground impact accident is defined as the maximum range of the human cylinder being violated by the UAV cylinder. Effects area of formula A g when considering only the UAV vertical fall ground impact accidents the formula (6), where r u UAVs wingspan equivalent radius, r p is the radius of the body.
  • a g 2 ⁇ (r u +2r p ) 2 +(r u +2r p )d (8)
  • the expectation function of total cost evaluation of route planning is composed of two parts, namely safety evaluation and distance evaluation.
  • the safety evaluation refers to the sum of the grid safety factors of the drone's flight path. The greater the safety evaluation, the worse the flight safety of the drone; the distance evaluation refers to the distance of the flight path of the drone, and the greater the distance evaluation is. Large means the longer the UAV flight path.
  • the distance evaluation takes the length of the UAV flight path as the evaluation index.
  • h j is the estimated value of the total cost of route planning from point j to the end point; d j is the distance from point j to the end point; s j is the raster risk factor of point j; ⁇ is the distance heuristic factor for A coefficient that characterizes the importance of distance; ⁇ is a safety heuristic factor, which is a coefficient used to characterize the importance of safety.
  • the A* algorithm is improved by taking the above-mentioned route planning total cost evaluation expectation function as the objective function of the A* algorithm. Using the improved A* algorithm, starting from the starting point grid, searching for its barrier-free neighborhood grid, and using the above-mentioned route The estimated total cost of the planning function calculates the reasonable value of the grid in each neighborhood, and selects the most reasonable grid until it reaches the end; after several cycles, the three-dimensional expected flight path is finally obtained after double optimization of airway safety and airway cost. .
  • f k is the evaluation function from the initial node to the target node via node k
  • g k is the actual cost from the initial node to node k in the state space
  • Sk is the acceptable risk from node k to the target node and Estimated cost of distance total cost.

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Abstract

L'invention concerne un procédé de planification d'itinéraire sûr en trois dimensions pour un véhicule aérien sans pilote. Le procédé comprend les étapes consistant à: effectuer une rastérisation tridimensionnelle sur un espace aérien de vol d'un véhicule aérien sans pilote de façon à obtenir une pluralité de grilles cubiques; décrire quantitativement le risque de vol du véhicule aérien sans pilote dans les grilles au moyen d'un facteur de sécurité de grille; construire une fonction de probabilité d'estimation de coût total de planification d'itinéraire sur la base du facteur de sécurité de grille et d'une distance d'itinéraire du véhicule aérien sans pilote; et considérer la fonction de probabilité d'estimation de coût total de planification d'itinéraire en tant que fonction d'objectif d'un algorithme A* pour l'amélioration, utiliser l'algorithme A* amélioré pour effectuer un calcul itératif, et enfin obtenir un trajet de vol prédit tridimensionnel après une double optimisation de la sécurité de route et du coût d'acheminement, etc.
PCT/CN2021/095252 2020-09-23 2021-05-21 Procédé de planification d'itinéraire sûr en trois dimensions pour véhicule aérien sans pilote WO2021213540A1 (fr)

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CN113985892A (zh) * 2021-11-17 2022-01-28 江苏科技大学 一种基于改进a＊算法的智能船舶路径规划方法
CN114199255A (zh) * 2021-12-08 2022-03-18 南京航空航天大学 一种用于城区物流无人机终端配送航路网络的规划方法
CN114265431A (zh) * 2021-12-22 2022-04-01 中国地质大学（武汉） 基于分布式覆盖控制的地灾监测方法及系统
CN114460972A (zh) * 2022-04-13 2022-05-10 中国民航大学 一种无人机城区运行管控方法
CN114489111A (zh) * 2021-12-08 2022-05-13 南京航空航天大学 一种基于随机航向的多旋翼无人机安全间隔标定方法
CN114489149A (zh) * 2022-02-23 2022-05-13 中山大学 一种面向三维重建的多无人机覆盖路径规划方法
CN115167459A (zh) * 2022-08-05 2022-10-11 江苏海洋大学 一种融合空洞修复和信息采集的水下机器人路径规划方法
CN115810087A (zh) * 2023-01-31 2023-03-17 中国电子科技集团公司第二十八研究所 一种基于多尺度空间栅格的低空空域使用冲突检测方法
CN117109597A (zh) * 2023-10-23 2023-11-24 北京卓翼智能科技有限公司 基于改进a星算法的无人机路径规划方法和装置

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CN112215407B (zh) * 2020-09-23 2022-10-04 中国民航大学 一种三维无人机安全航路规划方法
CN113405552A (zh) * 2021-06-17 2021-09-17 北京理工大学 一种飞行器路径规划方法及装置
CN115204466B (zh) * 2022-06-20 2024-02-20 中国南方航空股份有限公司 一种带通行限制的国际航线航路规划方法
CN114812564B (zh) * 2022-06-22 2022-09-20 北京航空航天大学杭州创新研究院 基于城市动态时空风险分析的多目标无人机路径规划方法

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Publication number Priority date Publication date Assignee Title
CN113985892B (zh) * 2021-11-17 2023-09-22 江苏科技大学 一种基于改进a＊算法的智能船舶路径规划方法
CN113985892A (zh) * 2021-11-17 2022-01-28 江苏科技大学 一种基于改进a＊算法的智能船舶路径规划方法
CN114199255A (zh) * 2021-12-08 2022-03-18 南京航空航天大学 一种用于城区物流无人机终端配送航路网络的规划方法
CN114489111A (zh) * 2021-12-08 2022-05-13 南京航空航天大学 一种基于随机航向的多旋翼无人机安全间隔标定方法
CN114199255B (zh) * 2021-12-08 2024-05-03 南京航空航天大学 一种用于城区物流无人机终端配送航路网络的规划方法
CN114489111B (zh) * 2021-12-08 2023-09-29 南京航空航天大学 一种基于随机航向的多旋翼无人机安全间隔标定方法
CN114265431A (zh) * 2021-12-22 2022-04-01 中国地质大学（武汉） 基于分布式覆盖控制的地灾监测方法及系统
CN114489149A (zh) * 2022-02-23 2022-05-13 中山大学 一种面向三维重建的多无人机覆盖路径规划方法
CN114460972A (zh) * 2022-04-13 2022-05-10 中国民航大学 一种无人机城区运行管控方法
CN114460972B (zh) * 2022-04-13 2022-06-07 中国民航大学 一种无人机城区运行管控方法
CN115167459A (zh) * 2022-08-05 2022-10-11 江苏海洋大学 一种融合空洞修复和信息采集的水下机器人路径规划方法
CN115167459B (zh) * 2022-08-05 2024-05-07 江苏海洋大学 一种融合空洞修复和信息采集的水下机器人路径规划方法
CN115810087A (zh) * 2023-01-31 2023-03-17 中国电子科技集团公司第二十八研究所 一种基于多尺度空间栅格的低空空域使用冲突检测方法
CN117109597A (zh) * 2023-10-23 2023-11-24 北京卓翼智能科技有限公司 基于改进a星算法的无人机路径规划方法和装置
CN117109597B (zh) * 2023-10-23 2023-12-29 北京卓翼智能科技有限公司 基于改进a星算法的无人机路径规划方法和装置

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