WO2023019890A1 - 一种多无人机自动作业调度系统及调度方法 - Google Patents

一种多无人机自动作业调度系统及调度方法 Download PDF

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WO2023019890A1
WO2023019890A1 PCT/CN2022/075579 CN2022075579W WO2023019890A1 WO 2023019890 A1 WO2023019890 A1 WO 2023019890A1 CN 2022075579 W CN2022075579 W CN 2022075579W WO 2023019890 A1 WO2023019890 A1 WO 2023019890A1
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grid
safety
flight
route
uav
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French (fr)
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尹彦卿
刘胜君
罗伟
陈梦云
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中航金城无人系统有限公司
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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
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/003—Flight plan management
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft

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  • the invention relates to a dispatching method, in particular to a multi-UAV automatic operation dispatching system and a dispatching method; it belongs to the technical field related to UAV control.
  • Drones are emerging technological products of the information age. By virtue of its fast and flexible time and space, it can effectively assist relevant departments to grasp the scene of the incident in a comprehensive, timely, in-depth and all-weather manner, and even handle it by itself, which can effectively improve work efficiency and reduce economic losses and casualties.
  • the domestic and foreign research on route planning in complex environments is still relatively preliminary, and a complete and systematic UAV dynamic flight scheduling system and planning method have not yet been formed.
  • the purpose of the present invention is to provide a multi-UAV automatic operation scheduling system and scheduling method.
  • the present invention firstly discloses a multi-UAV automatic operation dispatching system, including: dispatching server, several regional dispatching centers, multiple UAVs, UAV field and communication station;
  • the dispatching server accepts the demand input of the terminal user and forwards it to the regional dispatching center through a public or private network;
  • the regional dispatch center generates a mission plan and sends it to the UAV field through a public or private network and 4G/5G, and the UAV field then sends it to the UAV through a 4G/5G network or a dedicated microwave data link to perform specific flight tasks ;
  • the communication station is a dedicated microwave data link relay device within a certain area, which is used to supplement the communication blind area in the urban occlusion environment.
  • the aforesaid unmanned airport includes an unmanned aerial vehicle automatic airport and an alternate landing field.
  • the scheduling method based on the aforementioned multi-unmanned aerial vehicle automatic operation scheduling system includes the following steps:
  • the dispatching server receives the expected flight waypoints input by the user, and the regional dispatching center selects the most suitable flying UAV according to different configuration strategies, and automatically generates them according to the flight energy requirements and the values of Grid, Safety and Familiar grids Multiple flight mission plans based on different strategy configurations, and alternate landing sites for different flight segments;
  • the regional dispatch center and the dispatch server return the automatically generated flight mission plans to the user, and the user decides which route to use, and finally sends the flight mission plan selected by the user to the selected UAV field and UAV. Start the task execution.
  • the aforementioned basic information includes: the deployment location of the UAV and the UAV field, the location of the alternate landing site, the deployment location of the communication station, and the three-dimensional geographic model of the area. necessary basic data.
  • the cost function for defining a route segment Waypoints with a length of L grids is as follows:
  • the Cost function is a comprehensive cost function, which is the sum of the terrain cost function Cost Grid , the energy cost function Cost energy , the safety cost function Cost Safety , and the familiar road cost function Cost familiar , and the energy cost, safety cost, and familiar road cost have corresponding Weight coefficients k energy , k safety and k familiar ;
  • the terrain cost, the safety cost and the familiar road cost all adopt static values, and the energy cost is dynamically calculated according to the relationship between Waypoint i and Waypoint i-1 :
  • the aforementioned no-fly zone is obtained by the following method:
  • the terrain obstacles in the entire area can be represented by the terrain three-dimensional grid Grid[i,j,k], where The value stored in the grid is its no-fly zone;
  • Grid[i, j, k] 0, then the judgment point (i, j, k) belongs to the three-dimensional available flight space.
  • a qualitative judgment which is used to judge whether the grid is reachable or not. It mainly considers the flight distance and terrain factors, and the value can only be infinite or 0.
  • the grid Safety here is a quantitative grid obtained by comprehensively considering the distance of the alternate landing field and the distance of the communication station. It is a continuous value from zero to infinity, and the relative safety between the two grids can be judged. Setting the two grids of Safety and Backup is convenient for adjusting the strategy for subsequent task planning. Different strategies refer to different weights of the two grids.
  • familiar road grids Familiar[i,j,k] are also set in the scheduling method, and all Familiar grids are initialized to a certain value (according to the expected self-learning speed value), if in Familiar[i ,j,k] has executed a successful flight route at the corresponding area point, then the fixed value will be reduced by 1 until it is 0.
  • the familiar route grid is used to calculate the cost of the familiar route. This grid is mainly the number of times the grid points have been passed. The grid with more times has a lower cost. When planning missions, it will give priority to flying from the grid. Because the grid that has actually flown has higher security.
  • the aforementioned inbound and outbound routes are standardized as: (m, Waypoint m, n , P m, n ), wherein m represents that it is the inbound and outbound route of the mth airport, and Waypoint m, n represents that it is the inbound and outbound route of the mth airport.
  • the waypoint set of the nth entry and departure route of m airports, P m, n is the entry and departure point guided by the route, that is, the three-dimensional available flight space point where the UAV is guided from the airport, starting from this point It can fly freely in any direction.
  • the aforementioned grid initialization method is:
  • weight coefficients of the aforementioned three strategies are as follows:
  • k energy 1.0.
  • the cost function is mainly determined by terrain influence and energy consumption. The route with the lowest cost will have the lowest energy consumption, that is, the fastest flight speed;
  • the cost function is determined by terrain influence, energy consumption, safety and familiar route.
  • the familiar route has a higher weight, and the route with the lowest cost will tend to Areas that have been flown through, either manually defined or learned by the system.
  • the multi-UAV dispatching system of the present invention includes a dispatching server, a regional dispatching center, a UAV, an UAV field and a communication station.
  • the system first accepts the initial information input by the user, specifies a general flight route, and dispatches the UAV.
  • the system can automatically dispatch UAVs in an area, and select the most suitable flying UAV, and then calculate the optimal mission plan by the mission plan generation algorithm, including main waypoints, and related entry and departure routes , safety alternate landing points, etc., can realize the automatic task scheduling of multiple drones in a certain size area, and the system will automatically select the drone deployed in the most suitable position according to the route to fly the route, overcoming the existing
  • the dispatching system can only target a specific drone's technical drawbacks;
  • the dispatching method of the present invention can generate multiple routes for the user to choose from multiple perspectives such as time priority, safety priority, and familiarity priority according to different route generation strategies to meet the customer's requirements for automatic dispatching of multiple drones. Diverse needs.
  • Fig. 1 is the overall block diagram of the multi-unmanned aerial vehicle automatic operation scheduling system of the present invention
  • Fig. 2 is a schematic diagram of the maximum safe flight area considering the alternate landing mileage in the present invention
  • Fig. 3 is a grid schematic diagram of the three-dimensional available flight space in the present invention.
  • Fig. 4 is a schematic diagram of the Safety grid in the present invention.
  • Fig. 5 is a schematic diagram of the route generated by the present invention under the safety priority strategy
  • Fig. 6 is a schematic diagram of the routes generated by the present invention under the time priority strategy.
  • the present embodiment first announces a kind of multi-UAV automatic operation dispatching system, including: dispatching server, some regional dispatching centers, multiple UAVs, UAV field and communication station;
  • the dispatching server accepts the demand input of the terminal user and forwards it to the regional dispatching center through the public or private network;
  • the regional dispatch center generates the mission plan and sends it to the UAV field through the public or private network and 4G/5G, and the UAV field then sends it to the UAV through the 4G/5G network or dedicated microwave data link to perform specific flight tasks, here UAV airports include UAV automatic airports and alternate landing sites;
  • the communication station is a dedicated microwave data link relay device within a certain area, which is used to supplement the communication blind area in the urban occlusion environment.
  • the dispatch server receives the expected flight waypoint ⁇ W 1 , W 2 , W 3 ...W n ⁇ input by the user, and the regional dispatch center selects the most suitable flying UAV according to different configuration strategies, according to the flight energy Based on the requirements and values of Grid, Safety and Familiar grids, multiple flight mission plans based on different strategy configurations are automatically generated, and alternate landing sites are set for different flight segments;
  • the regional dispatch center and the dispatch server return the automatically generated flight mission plans to the user, and the user decides which route to use, and finally sends the flight mission plan selected by the user to the selected UAV field and UAV. Start the task execution.
  • the generation of the flight mission plan in step S2 is the core of the scheduling method.
  • the system receives the necessary waypoints ⁇ W 1 , W 2 , W 3 ... W n ⁇ input by the user (that is, the flight path of the UAV expected by the user)
  • the generation method of the flight mission plan includes the following sub-steps:
  • the cost function for defining a route segment Waypoints with a length of L grids is as follows:
  • the Cost function is a comprehensive cost function, which is the sum of the terrain cost function Cost Grid , the energy cost function Cost energy , the safety cost function Cost Safety , and the familiar road cost function Cost familiar , and the energy cost, safety cost, and familiar road cost have corresponding Weight coefficients k energy , k safety and k familiar ;
  • the terrain cost, the safety cost and the familiar road cost all adopt static values, and the energy cost is dynamically calculated according to the relationship between Waypoint i and Waypoint i-1 :
  • the non-flying zone in step S201 fully considers various factors such as high-rise buildings, crowd gathering areas, communication occlusion and communication interference in the city, unmanned airport entry and departure routes, emergency backup points, etc., which is important for the efficient and accurate execution of the scheduling method. meaning, which is defined by:
  • the terrain obstacles in the entire area can be represented by the terrain three-dimensional grid Grid[i,j,k], where The value stored in the grid is its no-fly zone;
  • Grid grid initialization method is:
  • the flight safety grid Safety[i,j,k] and the alternate landing field selection grid are calculated according to the emergency flight mileage of the UAV. Backup[i,j,k], specifically:
  • the Safety here is a quantitative grid obtained by mainly considering the distance of the alternate landing field and the distance of the communication station. It is a continuous value from zero to infinity, and the relative safety between the two grids can be judged. Setting the two grids of Safety and Backup is convenient for adjusting the strategy for subsequent task planning. Different strategies refer to different weights of the two grids.
  • Familiar[i,j,k] grids are also set, and all Familiar grids are initialized to a certain value (according to the expected self-learning speed value), if Familiar[i,j ,k] has executed a successful route on the corresponding area point, then the fixed value will be reduced by 1 until it is 0.
  • the familiar route grid is used to calculate the cost of the familiar route. This grid is mainly the number of times the grid points have been passed. The grid with more times has a lower cost. When planning missions, it will give priority to flying from the grid. Because the grid that has actually flown has higher security.
  • step S203 the weight coefficients of the three strategies are as follows:
  • k energy 1.0.
  • the cost function is mainly determined by terrain influence and energy consumption. The route with the lowest cost will have the lowest energy consumption, that is, the fastest flight speed;
  • the cost function is determined by terrain influence, energy consumption and safety. Safety has a higher weight.
  • the route with the lowest cost will tend to start from the area with high safety. Flying, avoiding areas with dense population and poor communication, the route generated under the safety priority strategy in this embodiment is shown in Figure 5. It can be seen that the route is actively approaching the communication station and the alternate landing field, and the grid values used is the safety grid;
  • the cost function is determined by terrain influence, energy consumption, safety and familiar route.
  • the familiar route has a higher weight, and the route with the lowest cost will tend to From the areas that have been flown through manually defined or learned by the system, the route generated under the time priority strategy in this embodiment is shown in Figure 6. It can be seen that the route has selected the shortest path in space, and the grid used The value is the Grid grid.
  • the arrival and departure route obtained in this embodiment is standardized as: (m, Waypoint m, n , P m, n ), where m indicates that it is the arrival and departure route of the m-th airport, and Waypoint m, n indicates its is the waypoint set of the nth entry-departure route of the m-th airport, P m, n is the entry-departure point guided by the route, that is, the three-dimensional available flight space point where the UAV is guided from the airport to, from which From the point, it can fly freely in any direction.
  • the multi-UAV dispatching system of the present invention first accepts the initial information input by the user, and indicates the general flight route.
  • the UAV dispatching system can automatically dispatch the UAVs in an area, and select the most suitable execution Fly the UAV, and then calculate the optimal mission plan by the mission plan generation algorithm, including the main waypoints, as well as related entry and departure routes, safety backup points, etc., which can achieve multiple UAVs in a certain size area
  • Automatic task scheduling the system will automatically select the UAV deployed in the most suitable position according to the route to fly the route, overcoming the technical drawbacks of existing technologies that can only target a specific UAV; in the scheduling method Fully consider various factors such as high-rise buildings, crowd gathering areas, communication occlusion and interference in the city, unmanned airport entry and departure routes, emergency backup points, etc., and configure and generate simultaneously from multiple perspectives such as time priority, safety priority, and familiar road priority Multiple routes are available for users to choose, to meet the diverse needs of customers for multi-UAV automatic scheduling solutions.

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Abstract

提供了一种多无人机自动作业调度系统和调度方法，其中，调度系统包括：调度服务器、若干区域调度中心、多台无人机、无人机场及通讯站；系统首先接受用户输入的初始信息，根据航线自动选择部署在最合适的位置的无人机执飞该航线，从时间优先、安全优先、熟路优先等多种角度配置，同时生成多条航线供用户选择。

Description

一种多无人机自动作业调度系统及调度方法 技术领域
本发明涉及一种调度方法，具体涉及一种多无人机自动作业调度系统和调度方法；属于无人机操控相关技术领域。
背景技术
无人机是信息时代的新兴科技产物。它凭借其在时间和空间上快速、灵活等优点，能有效地协助相关部门全面、及时、深入、全天候地掌握事件现场情况甚至自行进行处理，可以有效提高工作效率，减少经济损失和人员伤亡。国内外在复杂环境下航线规划方面的研究还比较初步，尚没有形成完整而系统的无人机动态飞行调度系统和规划方法。
现有的多旋翼无人机航线规划方法普遍还是依赖人工规划，将无人机必须经过的航点以直线形式连接起来，没有充分考虑到航线上的障碍物干扰、避开人群密集区域、通讯控制范围、安全备降点等多方面因素。鉴于上述原因，如何对多无人机进行自动作业调度是亟待解决的问题，此问题的解决无论对军用还是民用都具有极其重要的应用价值。
发明内容
为解决现有技术的不足，本发明的目的在于提供一种多无人机自动作业调度系统及调度方法。
为了实现上述目标，本发明采用如下的技术方案：
本发明首先公布了一种多无人机自动作业调度系统，包括：调度服务器、若干区域调度中心、多台无人机、无人机场及通讯站；
所述调度服务器接受终端用户的需求输入并通过公共或专用网络转发至区域调度中心；
所述区域调度中心生成任务计划并通过公共或专用网络以及4G/5G下发至无人机场，无人机场再通过4G/5G网络或专用微波数据链路发送给无人机，执行具体飞行任务；
所述通讯站是一定区域范围内的专用微波数据链路中继设备，用于补充城市遮挡环境下的通讯盲区。
优选地，前述无人机场包括无人机自动机场和备降场。
基于前述的多无人机自动作业调度系统的调度方法，包括如下步骤：
S1、获取调度区域内的基础信息，并根据以上基础信息准备出本区域内的可用飞行区域网格Grid、飞行安全性评价网格Safety、备降场选取网格Backup以及在系统刚开始运行时初始化的熟路网格Familiar，并对网格进行初始化操作；
S2、调度服务器接收用户输入的期望飞行航路点，区域调度中心根据不同的配置策略，选择最合适的执飞无人机，依照飞行能量需求以及Grid、Safety和Familiar网格的取值，自动生成多条基于不同策略配置的飞行任务计划，并为不同的航段设置备降场；
S3、区域调度中心和调度服务器将自动生成的多条飞行任务计划返回给用户，由用户决策使用哪一条航线，最终将用户选取的飞行任务计划发送到选定的无人机场和无人机，启动执行任务。
优选地，前述基础信息包括：无人机和无人机场部署位置、备降场位置、通讯站部署位置和本区域的三维地理模型，这些数据的准备为网格建立和飞行任务计划的生成提供了必要的基础数据。
优选地，系统接收到用户输入的必经航路点{W 1，W 2，W 3...W n}(即用户期望的无人机飞行路径)后，飞行任务计划的生成方式如下：
S201、通过Grid网格，检查用户输入的航路点或航线是否在不可飞行区内，若处于不可飞行区，则报错并返回，请用户重新检查输入条件；
S202、根据用户设置的航线起点，将所有的进离场航线集合按照进离场点距离该起点的距离排序，选取距离最近的航线作为本次任务使用的进离场航线；若该机场内的无人机处于不可用状态，则按照距离依次向下查询，直到选中一台可用的无人机及其进离场航线，将选中的进离场点加入必经航点集合{W 0＝P，W 1，W 2，W 3...W n}，这里的W是waypoints的简写；
S203、按照从起点到各个必经航路点的顺序，逐个生成任务航线段：
定义一段长度为L个网格的航线段Waypoints的代价函数如下：
Figure PCTCN2022075579-appb-000001
式中，Cost函数为综合代价函数，其为地形代价函数Cost Grid、能量代价函数Cost energy、安全代价函数Cost Safety、熟路代价函数Cost familiar之和，能量代价、安全代价、熟路代价分别有对应的权重系数k energy、k safety与k familiar；
所述地形代价、安全代价及熟路代价均采用静态数值，所述能量代价根据Waypoint i和Waypoint i-1之间的关系动态计算：
Figure PCTCN2022075579-appb-000002
按照不同的策略配置调节综合代价函数各权重之间的关系，分别生成时间优先、安全优先及熟路优先的不同策略，对每个必经航路点对(W i，W i+1)∈{W 0，W 1，W 2，W 3...W n}分别按照时间优先策略、安全优先策略及熟路优先策略，使用Dijkstra算法搜索出从W i到W i+1的加权最短路径，点与点之间的权重即为综合代价函数Cost；对航线中的每个网格，从backup网格中查询出对应的备降点，最终生成多条飞行任务计划。
优选地，前述不可飞行区通过如下方法获得：
(1)获取可用服务区域：
根据各机场位置及无人机最大飞行半径，获取由无人机性能限制的最大服务区：
Figure PCTCN2022075579-appb-000003
根据各备降点位置及无人机应急续航里程限制得到的最大安全飞行区域：
Figure PCTCN2022075579-appb-000004
取两者交集，记为可用服务区S available＝S uav∩S safety；
(2)取本区域的三维测绘模型并将其网格化，将网格初始化，获取本区域的立体可用飞行空间：
记整个区域的宽度为width，长度为length，高度为height，取合适的空间分辨率resolution，整个区域的地形障碍可用地形三维网格Grid[i,j,k]表示，其中
Figure PCTCN2022075579-appb-000005
网格中存储的值为其不可飞行区；
根据飞行器的定位性能及网格划分精度，设定一个最小安全间距，及无人机与最近的障碍物所必须保持的最小距离δ并体现在网格中：
Figure PCTCN2022075579-appb-000006
若Grid[i,j,k]＝0,则判断点(i,j,k)属于立体可用飞行空间。此处为一种定性判断，用于判断网格可不可达，主要考虑飞行距离和地形因素，取值只有无穷或0 两种。
更优选地，对每个备降场，根据无人机的应急飞行里程计算飞行安全性网格Safety[i,j,k]和备降场选取网格Backup[i,j,k]，具体为：
(a)将整个Safety和Backup网格全部初始化为∞；
(b)对每个备降场(Xbackup i，Ybackup i)所在的Safety网格，将其值置为0；对其所在的Backup网格，将数值置为i；
(c)取r＝resolution，对每个备降场(Xbackup i，Ybackup i)，将与其距离为r的Safety网格值设置为
Figure PCTCN2022075579-appb-000007
Backup网格值置为i；
(d)取r＝r+resolution，再进行一次步骤(c)操作，直到r值大于r emergency为止。
此处的网格Safety是综合考虑备降场的距离和通讯站的距离得到的一个定量网格，是从零到无穷的连续值，可以判断出两个网格之间的相对安全性。设置Safety和Backup两个网格是便于后续进行任务规划时调整策略使用，不同的策略参考两个网格的权重不一样。
优选地，对每个机场和通讯站，我们还根据基站增益进一步调整飞行安全性网格Safety[i,j,k]，在上一步的基础上进一步叠加：
1)对每个机场(Xairport i，Yairport i)和通讯站(Xbasestation i，Ybasestation i)所在的网格，其值不变；
2)取r＝resolution，对每个机场(Xairport i，Yairport i)或通讯站(Xbasestation i，Ybasestation i)，将与其距离为r的网格值增加20log(r)；
3)取r＝r+resolution，再进行一次步骤2)操作，直到r值大于distance radio为止；
4)对其他通讯基站未覆盖到的safety网格，将其值增加20log(distance radio)。
进一步优选地，在该调度方法中还设定有熟路网格Familiar[i,j,k]，将所有Familiar网格初始化为一定值(根据期望的自学习速度取值)，若在Familiar[i,j,k]对应的区域点上执行过一次成功的航线，则将该定值减1，直到为0为止。该熟路网格用于计算熟路代价，这个网格主要是网格点的途经次数，途经次数越多的网格代价越低，任务规划的时候就会优先考虑从已经飞行过的网格走，因为已经实际飞行过的网格具有更高的安全性。
进一步优选地，前述进离场航线规化为：(m，Waypoint m，n，P m，n)，其中m表示其为第m个机场的进离场航线，Waypoint m，n表示其为第m个机场的第n条进离场航线的航路点集，P m，n为该航线引导到的进离场点，即将无人机从机场引导到的立体可用飞行空间点，从该点开始可向任意方向自由飞行。
再进一步优选地，前述网格初始化方式为：
Figure PCTCN2022075579-appb-000008
再进一步优选地，前述三种策略的权重系数分别如下：
时间优先：k energy＝1.0，此时代价函数主要由地形影响和能耗决定，代价最低的航线将拥有最低的能耗，即最快的飞行速度；
安全优先：k energy＝0.3，k safety＝0.7，此时代价函数由地形影响、能耗和安全性综合决定，安全性拥有较高的权重，代价最低的航线将倾向于从安全性高的区域飞行，避开人员密集、通讯不佳的区域；
熟路优先：k energy＝0.3，k safety＝0.3，k familiar＝0.4，此时代价函数由地形影响、能耗、安全性和熟路综合决定，熟路拥有较高的权重，代价最低的航线将倾向于从人工定义或系统学习到的、已经飞行过的区域经过。
本发明的有益之处在于：
(1)本发明的多无人机调度系统包括调度服务器、区域调度中心、无人机、 无人机场及通讯站，系统首先接受用户输入的初始信息，指明大体的飞行路线，无人机调度系统可自动调度一个区域内的无人机，并从中选取出最合适的执飞无人机，然后由任务计划生成算法计算出最优任务计划，包括主要航路点，以及相关的进离场航线、安全备降点等，能够实现一定大小的区域内多台无人机的自动任务调度，系统将根据航线自动选择部署在最合适的位置的无人机执飞该航线，克服了现有的调度系统仅能针对某一台特定的无人机的技术弊端；
(2)在调度方法的任务计划规划阶段，针对城市应用场景，首先判断用户输入的航路点或航线是否处于本发明的可用飞行区内，充分考虑了高楼、人群聚集区、城市内通讯遮挡与通讯干扰、无人机场进离场航线、应急备降点等多种因素，规划出多条合理的飞行线路供用户选择；
(3)本发明的调度方法能够根据不同的航线生成策略，从时间优先、安全优先、熟路优先等多种角度配置同时生成多条航线供用户选择，满足客户对多无人机自动调度方案的多样化需求。
附图说明
图1是本发明的多无人机自动作业调度系统的总体框图；
图2是本发明中考虑了备降里程的最大安全飞行区域示意图；
图3是本发明中立体可用飞行空间的网格示意图；
图4是本发明中的Safety网格示意图；
图5是本发明在安全优先策略下生成的航线示意图；
图6是本发明在时间优先策略下生成的航线示意图。
具体实施方式
以下结合附图和具体实施例对本发明作具体的介绍。
参见图1，本实施例首先公布了一种多无人机自动作业调度系统，包括：调 度服务器、若干区域调度中心、多台无人机、无人机场及通讯站；
其中，调度服务器接受终端用户的需求输入并通过公共或专用网络转发至区域调度中心；
区域调度中心生成任务计划并通过公共或专用网络以及4G/5G下发至无人机场，无人机场再通过4G/5G网络或专用微波数据链路发送给无人机，执行具体飞行任务，这里的无人机场包括无人机自动机场和备降场；
通讯站则是一定区域范围内的专用微波数据链路中继设备，用于补充城市遮挡环境下的通讯盲区。
为了更好地理解和实施本发明，下面对基于该多无人机自动作业调度系统的调度方法进行说明，其包括如下步骤：
S1、获取调度区域内的基础信息：无人机和无人机场部署位置、备降场位置、通讯站部署位置和本区域的三维地理模型，这些数据为网格建立和后续飞行任务计划的生成提供了必要的基础信息。然后，系统根据以上基础信息准备出本区域内的可用飞行区域网格Grid、飞行安全性评价网格Safety、备降场选取网格Backup以及在系统刚开始运行时初始化的熟路网格Familiar，并对网格进行初始化操作；
S2、调度服务器接收用户输入的期望飞行航路点{W 1，W 2，W 3...W n}，区域调度中心根据不同的配置策略，选择最合适的执飞无人机，依照飞行能量需求以及Grid、Safety和Familiar网格的取值，自动生成多条基于不同策略配置的飞行任务计划，并为不同的航段设置备降场；
S3、区域调度中心和调度服务器将自动生成的多条飞行任务计划返回给用户，由用户决策使用哪一条航线，最终将用户选取的飞行任务计划发送到选定的无人机场和无人机，启动执行任务。
步骤S2中生成飞行任务计划是该调度方法的核心，系统接收到用户输入的必经航路点{W 1，W 2，W 3...W n}(即用户期望的无人机飞行路径)后，飞行任务计划的生成方式包括如下子步骤：
S201、通过建立的Grid网格，检查用户输入的航路点或航线是否在不可飞行区内，若处于不可飞行区，则报错并返回，请用户重新检查输入条件；
S202、根据用户设置的航线起点，将所有的进离场航线集合按照进离场点距离该起点的距离排序，选取距离最近的航线作为本次任务使用的进离场航线；若该机场内的无人机处于不可用状态，则按照距离依次向下查询，直到选中一台可用的无人机及其进离场航线，将选中的进离场点加入必经航点集合{W 0＝P，W 1，W 2，W 3...W n}，这里的W是waypoints的简写；
S203、按照从起点到各个必经航路点的顺序，逐个生成任务航线段：
定义一段长度为L个网格的航线段Waypoints的代价函数如下：
Figure PCTCN2022075579-appb-000009
式中，Cost函数为综合代价函数，其为地形代价函数Cost Grid、能量代价函数Cost energy、安全代价函数Cost Safety、熟路代价函数Cost familiar之和，能量代价、安全代价、熟路代价分别有对应的权重系数k energy、k safety与k familiar；
所述地形代价、安全代价及熟路代价均采用静态数值，所述能量代价根据Waypoint i和Waypoint i-1之间的关系动态计算：
Figure PCTCN2022075579-appb-000010
按照不同的策略配置调节综合代价函数各权重之间的关系，分别生成时间优先、安全优先及熟路优先的不同策略，对每个必经航路点对(W i，W i+1)∈{W 0，W 1，W 2，W 3...W n}分别按照时间优先策略、安全优先策略及熟路优先策略，使用Dijkstra算法搜索出从W i到W i+1的加权最短路径，点与点之间的权重即为综合代价函数Cost；对航线中的每个网格，从backup网格中查询出对应的备降点，最终生成多条飞行任务计划。
步骤S201中的不可飞行区充分考虑了高楼、人群聚集区、城市内通讯遮挡与通讯干扰、无人机场进离场航线、应急备降点等多种因素，对于调度方法的高效准确执行具有重要意义，其通过如下方法定义：
(1)获取可用服务区域：
根据各机场位置及无人机最大飞行半径，获取由无人机性能限制的最大服务区：
Figure PCTCN2022075579-appb-000011
根据各备降点位置及无人机应急续航里程限制得到的最大安全飞行区域：
Figure PCTCN2022075579-appb-000012
如图2所示，取两者交集，记为可用服务区S available＝S uav∩S safety；
(2)取本区域的三维测绘模型并将其网格化，将网格初始化，获取本区域的立体可用飞行空间：
记整个区域的宽度为width，长度为length，高度为height，取合适的空间分辨率resolution，整个区域的地形障碍可用地形三维网格Grid[i,j,k]表示，其中
Figure PCTCN2022075579-appb-000013
网格中存储的值为其不可飞行区；
根据飞行器的定位性能及网格划分精度，设定一个最小安全间距，及无人 机与最近的障碍物所必须保持的最小距离δ并体现在网格中：
Figure PCTCN2022075579-appb-000014
若Grid[i,j,k]＝0,则判断点(i,j,k)属于立体可用飞行空间，如图3所示，需要说明的是图3中仅取了立体方格中的一层作为示意。此处为一种定性判断，用于判断网格可不可达，主要考虑飞行距离和地形因素，取值只有无穷或0两种。
其中，Grid网格初始化方式为：
Figure PCTCN2022075579-appb-000015
为便于后续进行任务规划时调整策略，本实施例中对每个备降场，根据无人机的应急飞行里程计算飞行安全性网格Safety[i,j,k]和备降场选取网格Backup[i,j,k]，具体为：
(a)将整个Safety和Backup网格全部初始化为∞；
(b)对每个备降场(Xbackup i，Ybackup i)所在的Safety网格，将其值置为0；对其所在的Backup网格，将数值置为i；
(c)取r＝resolution，对每个备降场(Xbackup i，Ybackup i)，将与其距离为r的Safety网格值设置为
Figure PCTCN2022075579-appb-000016
Backup网格值置为i；
(d)取r＝r+resolution，再进行一次步骤(c)操作，直到r值大于r emergency为止。
此处的Safety是主要综合考虑备降场的距离和通讯站的距离，得到的一个定量网格，是从零到无穷的连续值，可以判断出两个网格之间的相对安全性。设置Safety和Backup两个网格是便于后续进行任务规划时调整策略使用，不同的策略参考两个网格的权重不一样。
为进一步优化策略，如图4所示，对每个机场和通讯站，我们还根据基站 增益进一步调整飞行安全性网格Safety[i,j,k]：
1)对每个机场(Xairport i，Yairport i)和通讯站(Xbasestation i，Ybasestation i)所在的网格，其值不变；
2)取r＝resolution，对每个机场(Xairport i，Yairport i)或通讯站(Xbasestation i，Ybasestation i)，将与其距离为r的网格值增加20log(r)；
3)取r＝r+resolution，再进行一次步骤2)操作，直到r值大于distance radio为止；
4)对其他通讯基站未覆盖到的safety网格，将其值增加20log(distance radio)。
此外，在该调度方法中还设定有熟路网格Familiar[i,j,k]，将所有Familiar网格初始化为一定值(根据期望的自学习速度取值)，若在Familiar[i,j,k]对应的区域点上执行过一次成功的航线，则将该定值减1，直到为0为止。该熟路网格用于计算熟路代价，这个网格主要是网格点的途经次数，途经次数越多的网格代价越低，任务规划的时候就会优先考虑从已经飞行过的网格走，因为已经实际飞行过的网格具有更高的安全性。
在步骤S203中，三种策略的权重系数分别如下：
时间优先：k energy＝1.0，此时代价函数主要由地形影响和能耗决定，代价最低的航线将拥有最低的能耗，即最快的飞行速度；
安全优先：k energy＝0.3，k safety＝0.7，此时代价函数由地形影响、能耗和安全性综合决定，安全性拥有较高的权重，代价最低的航线将倾向于从安全性高的区域飞行，避开人员密集、通讯不佳的区域，本实施例在安全优先策略下生成的航线如图5所示，可见该航线主动靠近了通讯站和备降场，所取用的网格数值为safety网格；
熟路优先：k energy＝0.3，k safety＝0.3，k familiar＝0.4，此时代价函数由 地形影响、能耗、安全性和熟路综合决定，熟路拥有较高的权重，代价最低的航线将倾向于从人工定义或系统学习到的、已经飞行过的区域经过，本实施例在时间优先策略下生成的航线如图6所示，可见该航线选取了空间上的最短路径，所取用的网格数值为Grid网格。
最终，本实施例得到的进离场航线规化为：(m，Waypoint m，n，P m，n)，其中m表示其为第m个机场的进离场航线，Waypoint m，n表示其为第m个机场的第n条进离场航线的航路点集，P m，n为该航线引导到的进离场点，即将无人机从机场引导到的立体可用飞行空间点，从该点开始可向任意方向自由飞行。
综上，本发明的多无人机调度系统首先接受用户输入的初始信息，指明大体的飞行路线，无人机调度系统可自动调度一个区域内的无人机，并从中选取出最合适的执飞无人机，然后由任务计划生成算法计算出最优任务计划，包括主要航路点，以及相关的进离场航线、安全备降点等，能够实现一定大小的区域内多台无人机的自动任务调度，系统将根据航线自动选择部署在最合适的位置的无人机执飞该航线，克服了现有的技术仅能针对某一台特定的无人机的技术弊端；在调度方法中充分考虑了高楼、人群聚集区、城市内通讯遮挡与通讯干扰、无人机场进离场航线、应急备降点等多种因素，从时间优先、安全优先、熟路优先等多种角度配置同时生成多条航线供用户选择，满足客户对多无人机自动调度方案的多样化需求。
以上显示和描述了本发明的基本原理、主要特征和优点。本行业的技术人员应该了解，上述实施例不以任何形式限制本发明，凡采用等同替换或等效变换的方式所获得的技术方案，均落在本发明的保护范围内。

Claims (10)

  1. 一种多无人机自动作业调度系统，其特征在于，包括：调度服务器、若干区域调度中心、多台无人机、无人机场及通讯站；
    所述调度服务器接受终端用户的需求输入并通过公共或专用网络转发至区域调度中心；
    所述区域调度中心生成任务计划并通过公共或专用网络以及4G/5G下发至无人机场，无人机场再通过4G/5G网络或专用微波数据链路发送给无人机，执行具体飞行任务，所述无人机场包括无人机自动机场和备降场；
    所述通讯站是一定区域范围内的专用微波数据链路中继设备，用于补充城市遮挡环境下的通讯盲区。
  2. 基于权利要求1所述的多无人机自动作业调度系统的调度方法，其特征在于，包括如下步骤：
    S1、获取调度区域内的基础信息，并根据以上基础信息准备出本区域内的可用飞行区域网格Grid、飞行安全性评价网格Safety、备降场选取网格Backup以及在系统刚开始运行时初始化的熟路网格Familiar；
    S2、调度服务器接收用户输入的期望飞行航路点，区域调度中心根据不同的配置策略，选择最合适的执飞无人机，依照飞行能量需求以及Grid、Safety和Familiar网格的取值，自动生成多条基于不同策略配置的飞行任务计划，并为不同的航段设置备降场；
    S3、区域调度中心和调度服务器将自动生成的多条飞行任务计划返回给用户，由用户决策使用哪一条航线，最后将用户选取的飞行任务计划发送到选定的无人机场和无人机，启动执行任务。
  3. 根据权利要求2所述的基于多无人机自动作业调度系统的调度方法，其特征在于，所述基础信息包括：无人机和无人机场部署位置、备降场位置、 通讯站部署位置和本区域的三维地理模型。
  4. 根据权利要求2所述的基于多无人机自动作业调度系统的调度方法，其特征在于，系统接收到用户输入的期望飞行航路点{W 1，W 2，W 3…W n}后，所述飞行任务计划的生成方式如下：
    S201、通过Grid网格，检查用户输入的航路点或航线是否在不可飞行区内，若处于不可飞行区，则报错并返回，请用户重新检查输入条件；
    S202、根据用户设置的航线起点，将所有的进离场航线集合按照进离场点距离该起点的距离排序，选取距离最近的航线作为本次任务使用的进离场航线；若该机场内的无人机处于不可用状态，则按照距离依次向下查询，直到选中一台可用的无人机及其进离场航线，将选中的进离场点加入飞行航点集合{W 0＝P，W 1，W 2，W 3…W n}；
    S203、按照从起点到各个飞行航路点的顺序，逐个生成任务航线段：
    定义一段长度为L个网格的航线段Waypoints的代价函数如下：
    Figure PCTCN2022075579-appb-100001
    式中，Cost函数为综合代价函数，其为地形代价函数Cost Grid、能量代价函数Cost energy、安全代价函数Cost Safety、熟路代价函数Cost familiar之和，能量代价、安全代价、熟路代价分别有对应的权重系数k energy、k safety与 k familiar；
    所述地形代价、安全代价及熟路代价均采用静态数值，所述能量代价根据Waypoint i和Waypoint i-1之间的关系动态计算：
    Figure PCTCN2022075579-appb-100002
    按照不同的策略配置调节综合代价函数各权重之间的关系，分别生成时间优先、安全优先及熟路优先的不同策略，对每个必经航路点对(W i，W i+1)∈{W 0，W 1，W 2，W 3…W n}分别按照时间优先策略、安全优先策略及熟路优先策略，使用Dijkstra算法搜索出从W i到W i+1的加权最短路径，点与点之间的权重即为综合代价函数Cost；对航线中的每个网格，从backup网格中查询出对应的备降点，最终生成多条飞行任务计划。
  5. 根据权利要求4所述的基于多无人机自动作业调度系统的调度方法，其特征在于，所述不可飞行区通过如下方法获得：
    (1)获取可用服务区域：
    根据各机场位置及无人机最大飞行半径，获取由无人机性能限制的最大服务区：
    Figure PCTCN2022075579-appb-100003
    根据各备降点位置及无人机应急续航里程限制得到的最大安全飞行区域：
    Figure PCTCN2022075579-appb-100004
    取两者交集，记为可用服务区S available＝S uav∩S safety；
    (2)取本区域的三维测绘模型并将其网格化，将网格初始化，获取本区域的立体可用飞行空间：
    记整个区域的宽度为width，长度为length，高度为height，取合适的空间分辨率resolution，整个区域的地形障碍可用地形三维网格Grid[i,j,k]表示，其中
    Figure PCTCN2022075579-appb-100005
    网格中存储的值为其不可飞行区；
    根据飞行器的定位性能及网格划分精度，设定一个最小安全间距，及无人机与最近的障碍物所必须保持的最小距离δ并体现在网格中：
    Figure PCTCN2022075579-appb-100006
    若Grid[i,j,k]＝0，则判断点(i,j,k)属于立体可用飞行空间。
  6. 根据权利要求5所述的基于多无人机自动作业调度系统的调度方法，其特征在于，所述网格初始化操作方式为：
    Figure PCTCN2022075579-appb-100007
  7. 根据权利要求4所述的基于多无人机自动作业调度系统的调度方法，其特征在于，对每个备降场，根据无人机的应急飞行里程计算飞行安全性网格Safety[i,j,k]和备降场选取网格Backup[i,j,k]，具体为：
    (a)将整个Safety和Backup网格全部初始化为∞；
    (b)对每个备降场(Xbackup i，Ybackup i)所在的Safety网格，将其值置为0；对其所在的Backup网格，将数值置为i；
    (c)取r＝resolution，对每个备降场(Xbackup i，Ybackup i)，将与其距离为r的Safety网格值设置为
    Figure PCTCN2022075579-appb-100008
    Backup网格值置为i；
    (d)取r＝r+resolution，再进行一次步骤(c)操作，直到r值大于r emergency为止。
  8. 根据权利要求7所述的基于多无人机自动作业调度系统的调度方法，其特征在于，对每个机场和通讯站，根据基站增益调整飞行安全性网格Safety[i,j,k]：
    1)对每个机场(Xairport i，Yairport i)和通讯站(Xbasestation i，Ybasestation i) 所在的网格，其值不变；
    2)取r＝resolution，对每个机场(Xairport i，Yairport i)或通讯站(Xbasestation i，Ybasestation i)，将与其距离为r的网格值增加20log(r)；
    3)取r＝r+resolution，再进行一次步骤2)操作，直到r值大于distance radio为止；
    4)对其他通讯基站未覆盖到的safety网格，将其值增加20log(distance radio)。
  9. 根据权利要求5所述的基于多无人机自动作业调度系统的调度方法，其特征在于，设定有熟路网格Familiar[i,j,k]，将所有Familiar网格初始化为一定值，若在Familiar[i,j,k]对应的区域点上执行过一次成功的航线，则将该定值减1，直到为0为止。
  10. 根据权利要求5所述的基于多无人机自动作业调度系统的调度方法，其特征在于，进离场航线规化为：(m，Waypoint m，n，P m，n)，其中m表示其为第m个机场的进离场航线，Waypoint m，n表示其为第m个机场的第n条进离场航线的航路点集，P m，n为该航线引导到的进离场点，即将无人机从机场引导到的立体可用飞行空间点，从该点开始可向任意方向自由飞行；
    所述三种策略的权重系数分别为：
    时间优先：k energy＝1.0
    安全优先：k energy＝0.3，k safety＝0.7
    熟路优先：k energy＝0.3，k safety＝0.3，k familiar＝0.4。
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