未知环境下异构多无人机协同搜索打击中的联盟组建

doi:10.3969/j.issn.1000-1093.2015.12.011

摘要/Abstract

摘要： 为了提高多架异构无人机在未知环境下协同执行搜索打击任务时的效能，提出了一种未知环境下的异构多无人机协同搜索打击中的联盟组建方法，研究了实时性较高且适应于未知环境下的任务分配机制。以最小化目标打击时间和最小化联盟规模为优化指标，以满足同时打击和资源需求为约束条件，建立了联盟组建模型；为了提高联盟组建的实时性，提出了一种分阶次优联盟快速组建算法(MSOCFA)。算法复杂度分析说明了该算法是一个多项式时间算法，并且通过与粒子群优化算法进行仿真对比，验证了该算法具有较低的计算复杂度，满足实时性要求。为了使得多架无人机能自主协同完成搜索打击任务，设计了基于有限状态机(FSM)的多无人机分布式自主协同控制策略。仿真验证了未知环境下的异构多无人机协同搜索打击中的联盟组建方法的合理性和可行性。使用蒙特卡洛法验证了无人机数量和目标数量对联盟组建的影响，即无人机数量越多，目标数量越少，其平均任务完成时间越短。为了提高多架异构无人机在未知环境下协同执行搜索打击任务时的效能，提出了一种未知环境下的异构多无人机协同搜索打击中的联盟组建方法，研究了实时性较高且适应于未知环境下的任务分配机制。以最小化目标打击时间和最小化联盟规模为优化指标，以满足同时打击和资源需求为约束条件，建立了联盟组建模型；为了提高联盟组建的实时性，提出了一种分阶次优联盟快速组建算法(MSOCFA)。算法复杂度分析说明了该算法是一个多项式时间算法，并且通过与粒子群优化算法进行仿真对比，验证了该算法具有较低的计算复杂度，满足实时性要求。为了使得多架无人机能自主协同完成搜索打击任务，设计了基于有限状态机(FSM)的多无人机分布式自主协同控制策略。仿真验证了未知环境下的异构多无人机协同搜索打击中的联盟组建方法的合理性和可行性。使用蒙特卡洛法验证了无人机数量和目标数量对联盟组建的影响，即无人机数量越多，目标数量越少，其平均任务完成时间越短。

关键词: 控制科学与技术, 多无人机, 协同搜索打击, 联盟组建, 有限状态机, 蒙特卡洛方法

Abstract: A novel method for coalition formation of multiple heterogeneous unmanned aerial vehicles in cooperative search and attack in unknown environment is presented to improve the cooperative search and attack effectivenesses of multiple heterogeneous unmanned aerial vehicles. A coalition formation model is established based on the minimum target attack time and the minimum coalition scale with the constraints of required resources and simultaneous strike. A multistage sub-optimal coalition formation algorithm (MSOCFA) that has low computational complexity is proposed to solve the optimization problem of coalition formation. The performances of MSOCFA and partical swarm optimization algorithms are compared in terms of mission performance, complexity of algorithm and time taken to form the coalitions. In order to enable the multiple cooperative unmanned aerial vehicles to accomplish the search and attack missions autonomously, a distributed autonomous control strategy is proposed, which is based on the finite-state machine (FSM). The simulation results show the rationality, validity and high real-time performance of the proposed method for the coalition formation of multiple heterogeneous unmanned aerial vehicles in cooperative search and attack in the unknown environment. Monte Carlo method is employed to validate the impact of the number of unmanned aerial vehicles and targets on coalition formation. The reduced average mission completion time relates to the decreased number of targets and the increased the number of unmanned aerial vehicles.

Key words: control science and technology, multi-unmanned aerial vehicle, cooperative search and prosecute, coalition formation, finite-state machine, Monte Carlo method

中图分类号:

V 279

刘重，高晓光，符小卫，牟之英. 未知环境下异构多无人机协同搜索打击中的联盟组建[J]. 兵工学报, 2015, 36(12): 2284-2297.

LIU Zhong, GAO Xiao-guang, FU Xiao-wei, MOU Zhi-ying. Coalition Formation of Multiple Heterogeneous Unmanned Aerial Vehicles in Cooperative Search and Attack in UnknownEnvironment[J]. Acta Armamentarii, 2015, 36(12): 2284-2297.

参考文献

［1］ Shima T, Rasmussen S. UAV cooperative decision and control challenges and practical approaches ［M］. Philadelphia: Society for industrial and Applied Mathematics, 2009.
［2］ Schumacher C, Chandler P R, Rasmussen S R. Task allocation for wide area search munitions via network flow optimization ［C］∥AIAA Guidance, Navigation, and Control Conference and Exhibit. Montreal, QC: AIAA, 2001.
［3］ Schumacher C, Chandler P R, Rasmussen S R. Task allocation for wide area search munitions via iterative network flow ［C］ ∥AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, CA: AIAA, 2002.
［4］ Schumacher C, Chandler P R, Pachter M, et al. UAV task assignment with timing constrains ［C］ ∥AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas: AIAA,2003.
［5］ Schumacher C, Chandler P R, Pachter M, et al. UAV task assignment with timing constrains via mixed integer linear programming ［C］∥AIAA 3rd Unmanned Unlimited Technical Conference, Workshop and Exhibit. Chicago, Illinois: AIAA, 2004.
［6］ Schumacher C, Chandler P R, Pachter M, et al. Constrained optimization for UAV task assignment ［C］∥AIAA Guidance, Navigation, and Control Conference and Exhibit. Providence, RI: AIAA, 2004.
［7］ Darrah M A, Niland W M, Stolarik B M. Multiple UAV dynamic task allocation using mixed integer linear programming in a SEAD mission ［C］∥AIAA InfoTech at Aerospace: Advancing Contemporary Aerospace Technologies and Their Integration. Arlington, VA: AIAA, 2005.
［8］ Alighanbari M, How J P. Decentralized task assignment for unmanned aerial vehicles ［C］∥ Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain:IEEE, 2005: 5668-5673.
［9］ Shima T, Rasmussen S J, Sparks A G, et al. Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms ［J］. Computers & Operations Research, 2006, 33(11): 3252-3269.
［10］钱艳平, 夏洁, 刘天宇. 基于合同网的无人机协同目标分配方法［J］. 系统仿真学报, 2011, 23(8): 1672-1676.
QIAN Yan-ping, XIA jie, LIU Tian-yu. Task assignment scheme based on contract net ［J］. Journal of System Simulation, 2011, 23(8): 1672-1676. (in Chinese)
［11］廖沫, 陈宗基. 基于满意决策的多机协同目标分配算法［J］. 北京航空航天大学学报, 2007, 33(1): 81-85.
LIAO Mo, CHEN Zong-ji. Coordinated target assignment in multi-UAV based on satisficing decision theory ［J］. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(1): 81-85. (in Chinese)
［12］ Shehory O, Kraus S. Methods for task allocation via agent coalition formation ［J］. Artificial Intelligence, 1998, 101(1/2): 165-200.
［13］ Service T C, Adams J A. Coalition formation for task allocation: theory and algorithms ［J］. Autonomous Agents and Multi-Agent Systems, 2011, 22(2):225-248.
［14］ Vig L, Adams J A. Multi-robot coalition formation ［J］. IEEE Transactions on Robotics, 2006, 22(4):637-649.
［15］ Chalkiadakis G, Markakis E, Boutilier C. Coalition formation under uncertainty: bargaining equilibria and the Bayesian core stability concept ［C］∥ Proceedings of the 6th International Joint Conference on Autonomous Agents & Multiagent System. Honolulu, HI: the Association for Computing Machinery, 2007: 412-419.
［16］ Cormen T H, Leiserson C E, Rivest R L. Introduction to algorithms［M］. 3rd ed. New York: MIT Press, 2009.
［17］ Nelson D R, Barber D B, McLain T W, et al. Vector field path following for miniature air vehicles ［J］. IEEE Transactions on Robotics, 2007, 23(3): 519-529.
［18］ Tsourdos A, White B, Shanmugavel M. Cooperative path planning of unmanned aerial vehicles ［M］.Bognor Regis: John Wiley and Sons Ltd, 2011.
［19］ Ambrosino G, Ariola M, Ciniglio F, et al. Path generation and tracking in 3-D for UAVs ［J］. IEEE Transactions on Control Systems Technology, 2009, 17(4):980-988.
［20］符小卫，李建，高晓光. 带通信约束的多无人机协同搜索中的目标分配［J］. 航空学报, 2014, 35(5): 1347-1356.
FU Xiao-wei, LI Jian, GAO Xiao-guang. Target allocation in multi-UAV cooperative search with communication constraints ［J］. Acta Aeronautica et Astronautica Sinica, 2014, 35(5): 1347-1356. (in Chinese)
［21］ Sujit P B, Georget J M, Beard R. Multiple UAV task allocation using particle swarm optimization ［C］∥AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, HI: AIAA, 2008.

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