多无人机协同覆盖路径规划

doi:10.7527/S1000-6893.2015.0174

电子与控制

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多无人机协同覆盖路径规划

陈海^1,2, 何开锋^1,2, 钱炜祺^1,2

1. 中国空气动力研究与发展中心计算空气动力研究所, 绵阳 621000;
2. 空气动力学国家重点实验室, 绵阳 621000

收稿日期:2015-04-20 修回日期:2015-06-07 出版日期:2016-03-15 发布日期:2015-06-12
通讯作者: 陈海,Tel.:0816-2463147,E-mail:chenhai@mail.nwpu.edu.cn E-mail:chenhai@mail.nwpu.edu.cn
作者简介:陈海,男,博士,讲师。主要研究方向:无人机航迹规划,飞行控制。Tel:0816-2463147,E-mail:chenhai@mail.nwpu.edu.cn;何开锋,男,博士,研究员,博士生导师。主要研究方向:无人机航迹规划,飞行力学。Tel:0816-2463148,E-mail:hekf@vip.sina.com;钱炜祺,男,博士,研究员。主要研究方向:无人机航迹规划,飞行力学。Tel:0816-2463075,E-mail:qwqhyy@sina.com

Cooperative coverage path planning for multiple UAVs

CHEN Hai^1,2, HE Kaifeng^1,2, QIAN Weiqi^1,2

1. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China;
2. State Key Laboratory of Aerodynamics, Mianyang 621000, China

Received:2015-04-20 Revised:2015-06-07 Online:2016-03-15 Published:2015-06-12

摘要/Abstract

摘要：

多无人机协同覆盖路径规划(CPP)由于其并行性和容错能力,对于提高无人机完成侦察、监视、搜索等任务的效率具有重要意义。提出了一种基于无人机任务性能评价和任务区域划分的多无人机协同CPP算法。定量分析了无人机执行覆盖任务的能力,根据无人机及携带成像传感器的性能给出了计算无人机任务性能指数的数学公式;提出了一种基于任务性能和子区域宽度的任务区域划分算法,使无人机的总转弯次数达到最少。仿真结果表明,所提出的CPP算法能够规划出全局最优的多无人机协同覆盖路径。

关键词: 多无人机, 覆盖路径规划, 任务性能指数, 子区域宽度, 任务区域划分, 转弯次数

Abstract:

Because of its parallelism and fault tolerant capability, the cooperative coverage path planning(CPP) for UAVs is very important in enhancing UAV's abilities for reconnaissance, surveillance, search and other missions. An algorithm for cooperative CPP for multiple UAVs is proposed based on mission performance evaluation and mission region decomposition. The UAV's capabilities for coverage mission implementation are analyzed quantitatively. The formulas for calculating the mission performance index are given based on the performances of the UAV and the onboard imaging sensor. A subregion decomposition algorithm based on the mission performance of the UAV and the width of the subregion is proposed to get the least total number of turns. The simulation results show that we can obtain the global optimal cooperative paths for multiple UAVs by using the proposed algorithm.

Key words: multiple UAVs, coverage path planning(CPP), mission performance index, subregion width, mission region decomposition, number of turns

中图分类号:

V279

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 Hai, HE Kaifeng, QIAN Weiqi. Cooperative coverage path planning for multiple UAVs[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(3): 928-935.

参考文献

[1] AMIT A, LIM M H, ER M J, et al. ACO for a new TSP in region coverage[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ:IEEE Press, 2005:1717-1722.
[2] DILLE M. Search and pursuit with unmanned aerial vehicles in road networks:CMU-RI-TR-13-30[R]. Pittsburgh:Michael Dille, 2013.
[3] ELMALIACH Y, AGMON N, KAMINKA G A. Multi-robot area patrol under frequency constraints[J]. Annals of Mathematics and Artificial Intelligence, 2009, 57(3):293-320.
[4] LAZARUS S B, TSOURDOS A, WHITE B A, et al. Cooperative unmanned aerial vehicle searching and mapping of complex obstacles using two-dimensional splinegon[J]. Proceedings of the Institution of Mechanical Engineers. Part G:Journal of Aerospace Engineering, 2010, 224(2):149-170.
[5] MADJIDI H, NEGAHDARIPOUR S, BANDARI E. Vision-based positioning and terrain mapping by global alignment for UAVs[C]//Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance. Piscataway, NJ:IEEE Press, 2003:305-312.
[6] PARK S J, YEU T K, YOON S M, et al. A study of sweeping coverage path planning method for deep-sea manganese nodule mining robot[C]//Proceedings of OCEANS2011. Piscataway, NJ:IEEE Press, 2011:1-5.
[7] HAMEED I A. Intelligent coverage path planning for agricultural robots and autonomous machines on three-dimensional terrain[J]. Journal of Intelligent and Robotic Systems, 2014, 74(3):965-983
[8] 陈海, 王新民, 焦裕松, 等. 一种凸多边形区域的无人机覆盖航迹规划算法[J]. 航空学报, 2010, 31(9):1802-1808. CHEN H, WANG X M, JIAO Y S, et al. An algorithm of coverage flight path planning for UAVs in convex polygon areas[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(9):1802-1808(in Chinese).
[9] LI Y, CHEN H, ER M J, et al. Coverage path planning for UAVs based on enhanced exact cellular decomposition method[J]. Mechatronics, 2011, 21(2):876-885.
[10] DIAS M B, STENTZ A T. Traderbots:A market-based approach for resource, role, and task allocation in multirobot coordination:CMU-RI-TR-03-19[R]. Pittsburgh:Robotics Institute, 2003.
[11] ZLOT R, STENTZ A. Market-based multirobot coordination for complex tasks[J]. International Journal of Robotics Research, 2006, 25(1):73-101.
[12] GERKEY B P, MATARIC M J. Sold:Auction methods for multirobot coordination[J]. IEEE Transactions on Robotics and Automation, 2002, 18(5):758-768.
[13] MOSTEO A R, MONTANO L. Comparative experiments on optimization criteria and algorithms for auction based multi-robot task allocation[C]//Proceedings of IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE Press, 2007:3345-3350.
[14] NIGAM N, KROO I. Persistent surveillance using multiple unmanned air vehicles[C]//Proceedings of IEEE Aerospace Conference. Piscataway, NJ:IEEE Press, 2008:1-14.
[15] AGARWAL A, HIOT L M, NGHIA N T, et al. Parallel region coverage using multiple UAVs[C]//Proceedings of IEEE Aerospace Conference. Piscataway, NJ:IEEE Press, 2006:1-8.
[16] JANCHIV A, BATSAIKHAN D, KIM B S, et al. Time-efficient and complete coverage path planning based on flow networks for multi-robots[J]. International Journal of Control, Automation, and Systems, 2013, 11(2):369-376.
[17] CHOSET H, PIGNON P. Coverage path planning:The Boustrophedon cellular decomposition[C]//International Conference on Field and Service Robotics. Berlin:Springer, 1997:203-209.
[18] REKLEITIS I, NEW A P, RANKIN E S, et al. Efficient boustrophedon multi-robot coverage:An algorithmic approach[J]. Annals of Mathematics and Artificial Intelligence, 2008, 52(2):109-142.
[19] 崔益安. 多机器人协同覆盖技术研究[D]. 长沙:中南大学, 2008. CUI Y A. Research on multi-robot coverage based on cooperation[D]. Changsha:Central South University, 2008(in Chinese).
[20] MAZA I, OLLERO A. Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms[J]. Distributed Autonomous Robotics Systems, 2007, 46(6):221-230.
[21] 彭辉, 沈林成, 霍霄华. 多UAV协同区域覆盖搜索研究[J]. 系统仿真学报, 2007, 19(11):2472-2476. PENG H, SHEN L C, HUO X H. Research on multiple UAV cooperative area coverage searching[J]. Journal of System Simulation, 2007, 19(11):2472-2476(in Chinese).
[22] CHEN H, WANG X M, JIAO Y S, et al. Research on search probability and camera footprint of region coverage for UAVs[C]//Proceedings of 2009 IEEE International Conference on Control and Automation. Piscataway, NJ:IEEE Press, 2009:1920-1924.

E-mail：hkxb@buaa.edu.cn

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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

多无人机协同覆盖路径规划

Cooperative coverage path planning for multiple UAVs

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