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.
[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).