多作业区域植保无人机航线规划算法

摘要

针对植保施药多个作业区域的情况,研究了一种植保无人机全局航线规划算法,将整个算法分为单个区域航线规划、区域间作业顺序和区域间调度航线规划3部分.从作业路程、多余覆盖和遗漏覆盖的角度,分析了多种覆盖作业方式的优劣,确定了无人机在单区域内的覆盖方式.基于遗传算法与TSP问题得到区域间的优化作业顺序,并基于改进的二进制编码遗传算法进行区域间调度航线的规划,最终实现无人机多作业区域航线的全局规划.仿真结果表明,规划算法可以有效地实现全局航线的规划,缩短了无人机的作业距离与区域间调度飞行的距离,达到了能耗与工作时间的优化,节省了航线规划所需的人力成本,使作业管理更加便利.%According to multi-area operations,a kind of overall route planning algorithm for plant protection UAVs was developed in order to reduce flight distance in multi-area operations and operating sequence of each area was reasonable allocation to improve operational efficiency and reduce energy consumption of the UAVs.The algorithm was divided into three parts,namely,single area route planning,operating sequences of areas and dispatching route planning among areas.After analyzing a variety of covering operation modes in aspects of operation distance,extra coverage and missed coverage,the UAVs operation mode in single area was determined.Optimized operation sequences of areas were planned based on genetic algorithm and traveling salesman problem (TSP).Dispatching routes among areas were planned based on improved genetic algorithm with binary coding,finally the overall route planning algorithm was achieved.The simulation was performed in an operation of five different irregular areas,numbers of each area were set as A,B,C,D and E.Operation route of each area was planned by using the previously proposed algorithm of route planning algori.thm based on operation path angle in irregular,achieving operation start point,end point and node point coordinates of each area.Operation sequences of areas were achieved based on genetic algorithm and TSP,dispatching routes among areas were planned based on the improved genetic algorithm,of which the code was a random five-digit binary sequence,each digit represented arrangement of start point and end point of each area.The simulation result proved feasibility of the multi-area route planning algorithm.Nowadays,unmanned operations becomes trend,this multi-area route planning algorithm not only saves manpower required by route planning,but also makes operation management easier,and it is suitable for autonomous unmanned aerial vehicles and can be widely used in the area of precision agriculture.