多植保无人机协同路径规划

doi:10.7527/S1000-6893.2019.23610

电子电气工程与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

多植保无人机协同路径规划

阚平^1,2, 姜兆亮^1,2, 刘玉浩^1,2, 王振武^1,2

1. 山东大学机械工程学院高效洁净机械制造教育部重点实验室, 济南 250061;
2. 山东大学日照智能制造研究院, 日照 276800

收稿日期:2019-10-25 修回日期:2019-12-30 出版日期:2020-04-15 发布日期:2019-12-26
通讯作者: 姜兆亮 E-mail:jiangzhaoliang@sdu.edu.cn
基金资助:
山东省自然科学基金（ZR2017MEE052）；日照市科技创新专项（2019CXZX1110）

Cooperative path planning for multi-sprayer-UAVs

KAN Ping^1,2, JIANG Zhaoliang^1,2, LIU Yuhao^1,2, WANG Zhenwu^1,2

1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Ji'nan 250061, China;
2. Rizhao Intelligent Manufacturing Institute, Shandong University, Rizhao 276800, China

Received:2019-10-25 Revised:2019-12-30 Online:2020-04-15 Published:2019-12-26
Supported by:
Shandong Provincial Natural Science Foundation (ZR2017MEE052); Rizhao Science and Technology Innovation Project (2019CXZX1110)

摘要/Abstract

摘要： 为实现多植保无人机（UAVs）协同作业，并提高作业效率，提出了一种基于改进粒子群优化（PSO）的多植保无人机协同路径规划算法。根据作业区域的形状面积和植保UAV的作业参数划分各架UAV作业区域，采用栅格法生成各区域全覆盖作业航线。以各架植保UAV各架次植保作业距离为算法寻优变量，在确保各架UAV补给时间满足间隔分布约束条件下，综合考虑补给总次数、返航补给总时间、总耗时和最小补给时间间隔4项因素，并构成目标函数，通过采用改进PSO算法，实现了对各UAV返航顺序和返航点位置的寻优。仿真分析结果表明，相较于最大作业距离规划和最小返航距离规划，本文提出的规划算法表现出了较优的性能和较好的作业区域适应性，证实了其有效性和实用性。

关键词: 路径规划, 多植保无人机, 补给点, 粒子群优化(PSO), 返航点

Abstract: In order to achieve collaborative work and improve operation efficiency of multi-sprayer-Unmanned Aerial Vehicles(UAVs), a cooperative path planning algorithm for multi-sprayer-UAVs based on the improved Particle Swarm Optimization(PSO) is proposed. Considering the working area’s shape and size and the operating parameters of sprayer-UAV, the working area of each UAV is divided. The full coverage route in each area is generated by grid method. The operation distance during one trip of each sprayer-UAV is used as the algorithm optimization variable. Under the condition that the replenishment time of each UAV meets the interval distribution constraint, four factors of replenishment frequency, total replenishment time, total operation time and minimum replenishment interval are comprehensively considered, constituting the objective function. The improved PSO algorithm is applied to optimize the position of return points and return sequence of UAVs. The simulation results show that compared with the maximum operating distance planning and the minimum return distance planning, the proposed planning alqorithm show better performance and better operation area adaptability, which proved its effectiveness and practicability.

Key words: path planning, multi-sprayer-Unmanned Aerial Vehicle(UAV), supply point, Particle Swarm Optimization(PSO), return points

中图分类号:

V279
S252

阚平, 姜兆亮, 刘玉浩, 王振武. 多植保无人机协同路径规划[J]. 航空学报, 2020, 41(4): 323610-323610.

KAN Ping, JIANG Zhaoliang, LIU Yuhao, WANG Zhenwu. Cooperative path planning for multi-sprayer-UAVs[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(4): 323610-323610.

参考文献 21

[1]	ZHAO Y, ZHENG Z, YANG L. Survey on computational-intelligence-based UAV path planning[J]. Knowledge-Based Systems, 2018, 158:54-64.
[2]	MINAEIAN S, JIAN L, SON Y J. Vision-based target detection and localization via a team of cooperative UAV and UGVs[J]. IEEE Transactions on Systems Man&Cybernetics Systems, 2017, 46(7):1005-1016.
[3]	CONESA-MU OZ J, PAJARES G, RIBEIRO A. Mix-opt:A new route operator for optimal coverage path planning for a fleet in an agricultural environment[J]. Expert Systems with Applications, 2016, 54:364-378.
[4]	BEN-GHORBEL M, RODRIGUEZ-DUARTE D, GHAZZAI H, et al. Joint position and travel path optimization for energy efficient wireless data gathering using unmanned aerial vehicles[J]. IEEE Transactions on Vehicular Technology, 2019, 68(3):2165-2175.
[5]	PRIMICERIO J, FIORILLO E, GENESIO L, et al. A flexible unmanned aerial vehicle for precision agriculture[J]. Precision Agriculture, 2012, 13(4):517-523.
[6]	刘鑫,杨霄鹏,刘雨帆,等.基于GA-OCPA学习系统的无人机路径规划方法[J].航空学报, 2017, 38(11):321275. LIU X, YANG X P, LIU Y F, et al. UAV path planning based on GA-OCPA learning system[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(11):321275(in Chinese).
[7]	LIN Y, SARIPALLI S. Sampling-based path planning for UAV collision avoidance[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11):1-14.
[8]	周志艳,臧英,罗锡文,等.中国农业航空植保产业技术创新发展战略[J].农业工程学报, 2013, 29(24):1-10. ZHOU Z Y, ZANG Y, LUO X W, et al. Technology innovation development strategy on agricultural aviation industry for plant protection in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(24):1-10(in Chinese).
[9]	娄尚易,薛新宇,顾伟,等.农用植保无人机的研究现状及趋势[J].农机化研究, 2017, 39(12):1-6, 31. LOU S Y, XUE X Y, GU W, et al. Current status and trends of agricultural plant protection unmanned aerial vehicle[J]. Journal of Agricultural Mechanization Research, 2017, 39(12):1-6, 31(in Chinese).
[10]	陈海,何开锋,钱炜祺.多无人机协同覆盖路径规划[J].航空学报, 2016, 37(3):928-935. CHEN H, HE K F, QIAN W Q. Cooperative coverage path planning for multiple UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(3):928-935(in Chinese).
[11]	徐博.植保无人机航线规划方法研究[D].北京:中国农业大学, 2017. XU B. Research on route planning for plant protection unmanned aerial vehicles[D]. Beijing:China Agricultural University, 2017(in Chinese).
[12]	徐博,陈立平,谭彧,等.多架次作业植保无人机最小能耗航迹规划算法研究[J].农业机械学报, 2015, 46(11):36-42. XU B, CHEN L P, TAN Y, et al. Path planning based on minimum energy consumption for plant protection UAVs in sorties[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(11):36-42(in Chinese).
[13]	王宇,陈海涛,李煜,等.基于Grid-GSA算法的植保无人机路径规划方法[J].农业机械学报, 2017, 48(7):29-37. WANG Y, CHEN H T, LI Y, et al. Path planning method based on Grid-GSA for plant protection UAV[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(7):29-37(in Chinese).
[14]	董胜,袁朝辉,谷超,等.基于多学科技术融合的智能农机控制平台研究综述[J].农业工程学报, 2017, 33(8):1-11. DONG S, YUAN Z H, GU C, et al. Research on intelligent agricultural machinery control platform based on multi-discipline technology integration[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(8):1-11(in Chinese).
[15]	KENNEDY J, EBERHART R. Particle swarm optimization[C]//International Conference on Neural Networks. Piscataway, NJ:IEEE Press, 1995:1942-1948.
[16]	EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]//Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Piscataway, NJ:IEEE Press, 1995:39-43.
[17]	杨维,李歧强.粒子群优化算法综述[J].中国工程科学, 2004, 6(5):87-94. YANG W, LI Q Q. Survey on particle swarm optimization algorithm[J]. Strategic Study of CAE, 2004, 6(5):87-94(in Chinese).
[18]	ESMIN A A A, COELHO R A, MATWIN S. A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data[J]. Artificial Intelligence Review, 2015, 44(1):23-45.
[19]	SHI Y H, EBERHART R. Monitoring of particle swarm optimization[J]. Frontiers of Computer Science, 2009, 3(1):31-37.
[20]	JIAO B, LIAN Z, GU X. A dynamic inertia weight particle swarm optimization algorithm[J]. Chaos Solitons&Fractals, 2008, 37(3):698-705.
[21]	方群,徐青.基于改进粒子群算法的无人机三维航迹规划[J].西北工业大学学报, 2017, 35(1):66-73. FANG Q, XU Q. 3D route planning for UAV based on improved PSO algorithm[J]. Journal of Northwestern Polytechnical University, 2017, 35(1):66-73(in Chinese).

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

多植保无人机协同路径规划

Cooperative path planning for multi-sprayer-UAVs

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价

[1]	薛镇涛, 陈建, 张自超, 刘旭赞, 苗宪盛, 胡贵. 基于复杂地块凸划分优化的多无人机覆盖路径规划[J]. 航空学报, 2022, 43(12): 325990-325990.
[2]	周彬, 郭艳, 李宁, 钟锡健. 基于导向强化Q学习的无人机路径规划[J]. 航空学报, 2021, 42(9): 325109-325109.
[3]	胡莘婷, 吴宇. 面向城市飞行安全的无人机离散型多路径规划方法[J]. 航空学报, 2021, 42(6): 324383-324383.
[4]	庞磊, 曹志强, 喻俊志. 基于A^*和TEB融合的行人感知无碰跟随方法[J]. 航空学报, 2021, 42(4): 524909-524909.
[5]	戴健, 许菲, 陈琪锋. 多无人机协同搜索区域划分与路径规划[J]. 航空学报, 2020, 41(S1): 723770-723770.
[6]	李安醍, 李诚龙, 武丁杰, 卫鹏. 结合跳点引导的无人机随机搜索避撞决策方法[J]. 航空学报, 2020, 41(8): 323726-323726.
[7]	卢燕梅, 宗群, 张秀云, 鲁瀚辰, 张睿隆. 集群无人机队形重构及虚拟仿真验证[J]. 航空学报, 2020, 41(4): 323580-323580.
[8]	王志伟, 蔺小军, 史耀耀, 高源, 张允. 基于压缩量偏差约束的整体叶盘砂布轮数控抛光路径规划[J]. 航空学报, 2020, 41(2): 623330-623330.
[9]	张菁, 何友, 彭应宁, 李刚. 基于神经网络和人工势场的协同博弈路径规划[J]. 航空学报, 2019, 40(3): 322493-322493.
[10]	贾正荣, 王航宇, 卢发兴. 基于障碍凸化的改进环流APF路径规划[J]. 航空学报, 2019, 40(10): 323189-323189.
[11]	贾庆轩, 王宣, 陈钢, 孙汉旭, 郭雯. 基于位姿可达空间的太空机械臂容错路径规划[J]. 航空学报, 2018, 39(8): 422000-422000.
[12]	樊伟, 郑联语, 王亚辉, 刘新玉. 管路组件可重构装配工装系统的定位器自动配置与性能分析[J]. 航空学报, 2018, 39(5): 421793-421793.
[13]	刘鑫, 杨霄鹏, 刘雨帆, 姚昆. 基于GA-OCPA学习系统的无人机路径规划方法[J]. 航空学报, 2017, 38(11): 321275-321275.
[14]	陈海, 何开锋, 钱炜祺. 多无人机协同覆盖路径规划[J]. 航空学报, 2016, 37(3): 928-935.
[15]	李宝磊, 施心陵, 李敬敬, 吕丹桔. 基于改进多元优化算法的动态路径规划[J]. 航空学报, 2015, 36(7): 2319-2328.