基于相邻争夺算法的无人机多架次植保作业路径规划  

Path planning for the multiple drones plant protection operations of UAV based on adjacent contention algorithm

作　　者：沈跃[1] 张凌飞 沈亚运 储金城 刘慧[1] SHEN Yue;ZHANG Lingfei;SHEN Yayun;CHU Jincheng;LIU Hui(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,镇江212013

出　　处：《农业工程学报》2024年第16期44-51,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(51975260);中国高校产学研创新基金项目(2021ZYB02002)。

摘　　要：为了提高植保无人机作业效率,减少无人机作业损耗,该研究针对传统粒子群优化(particle swarm optimization,PSO)算法在规划植保作业路径时容易陷入局部最优,能耗最优方案的搜索能力低下等问题,该研究提出一种基于相邻争夺(adjacent competition, AC)算法的植保无人机作业路径规划算法。首先,对所有粒子设置作业距离范围,以防止单次作业距离过长或过短的极端情况;其次,在作业距离范围内随机分配每个粒子的作业距离,作为搜索的初始值;最后,相邻粒子相互争夺作业距离间接改变各架次作业距离,搜索出最优路径。相邻争夺算法保证了植保无人机作业总距离一定,对搜索方向进行先验且保证特殊点不被遗漏,避免算法陷入局部最优解。使用Matlab软件对420 m×200 m的模拟植保场地进行算法仿真验证,传统粒子群算法常陷入局部最优解,在10次规划中相较于遍历出的能耗最优规划方案增加了16.16%~38.14%的能耗,本文提出的相邻争夺算法规划结果的能耗远低于传统粒子群算法,算法具有更强的搜索能力。使用RflySim仿真平台搭建植保无人机模型和420 m×200 m的作业场地,在虚拟环境下比较传统粒子群算法与相邻争夺算法规划结果的模拟跟踪情况,相邻争夺算法规划结果的能耗相较传统粒子群算法减少了25.15%。420 m×200 m作业场地实际飞行试验中,相邻争夺算法规划结果的能耗相较传统粒子群算法减少了34.48%,更适应多架次植保作业。Traditional particle swarm optimization(PSO)is prone to getting stuck in local optima and low search ability for energy consumption optimal solutions when planning plant protection operation paths.The study focuses on improving the PSO algorithm to improve the efficiency of crop protection drone operations and reduce drone losses.An adjacent competition(AC)algorithm for crop protection operation path planning based on a crop protection UAV model was proposed to solve the problem of poor effectiveness of traditional particle swarm optimization algorithms applied to UAV plant protection operations.The AC algorithm ensures that the total distance of crop protection UAV operations is constant,provides prior knowledge of the search direction,and ensures that special points are not missed,preventing the algorithm from getting stuck in local optimal solutions:Firstly,all particles initialize the homework distance range to prevent extreme situations where the single homework distance is too long or too short.Secondly,each drone is randomly assigned the operating distance for each particle within the operating range.Finally,adjacent particles compete with each other for the operating distance,indirectly changing the operating distance of each flight and searching for the optimal path.Matlab was used to conduct algorithm simulation verification on a simulated plant protection site of 420 m×200 m.Traditional PSO algorithm often gets stuck in local optima and the final planning results fluctuate greatly,while the AC algorithm obtains the optimal energy consumption solution 9 times out of 10 planning iterations,and the planning energy consumption of non-optimal solutions is similar to the optimal energy consumption,still better than all planning results of the traditional PSO algorithm,proving that the neighboring contention algorithm has stronger searchability.The results show that traditional PSO algorithms often got stuck in local optima and increased energy consumption by 16.16%to 38.14%compared to neighboring contention

关 键 词：植保 无人机 粒子群算法 相邻争夺算法 搜索能力 能耗最优 

分 类 号：S494[农业科学—植物保护] S252.3