MAV/UAV task coalition phased-formation method
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摘要
The formation of the manned aerial vehicle/unmanned aerial vehicle(MAV/UAV) task coalition is considered. To reduce the scale of the problem, the formation progress is divided into three phases. For the task clustering phase, the geographical position of tasks is taken into consideration and a cluster method is proposed. For the UAV allocation phase, the UAV requirement for both constrained and unconstrained resources is introduced, and a multi-objective optimal algorithm is proposed to solve the allocation problem. For the MAV allocation phase, the optimal model is firstly constructed and it is decomposed according to the ideal of greed to reduce the time complexity of the algorithm. Based on the above phases, the MAV/UAV task coalition formation method is proposed and the effectiveness and practicability are demonstrated by simulation examples.
The formation of the manned aerial vehicle/unmanned aerial vehicle(MAV/UAV) task coalition is considered. To reduce the scale of the problem, the formation progress is divided into three phases. For the task clustering phase, the geographical position of tasks is taken into consideration and a cluster method is proposed. For the UAV allocation phase, the UAV requirement for both constrained and unconstrained resources is introduced, and a multi-objective optimal algorithm is proposed to solve the allocation problem. For the MAV allocation phase, the optimal model is firstly constructed and it is decomposed according to the ideal of greed to reduce the time complexity of the algorithm. Based on the above phases, the MAV/UAV task coalition formation method is proposed and the effectiveness and practicability are demonstrated by simulation examples.
The formation of the manned aerial vehicle/unmanned aerial vehicle(MAV/UAV) task coalition is considered. To reduce the scale of the problem, the formation progress is divided into three phases. For the task clustering phase, the geographical position of tasks is taken into consideration and a cluster method is proposed. For the UAV allocation phase, the UAV requirement for both constrained and unconstrained resources is introduced, and a multi-objective optimal algorithm is proposed to solve the allocation problem. For the MAV allocation phase, the optimal model is firstly constructed and it is decomposed according to the ideal of greed to reduce the time complexity of the algorithm. Based on the above phases, the MAV/UAV task coalition formation method is proposed and the effectiveness and practicability are demonstrated by simulation examples.
引文
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[2]ARSLAN O,ARMAGAN B,INALHAN G.Development of a mission simulator for design and testing of C2 algorithms and HMI concepts across real and virtual manned-unmanned fleets.Lecture Notes in Control and Information Sciences,2009,381(1):431-458.
[3]GARCIA R D,BARNES L,FIELDS M.Unmanned aircraft system as wingmen.Journal of Defense Modeling and Simulation,2012,9(1):5-15.
[4]HU X,YANG L Y,ZHANG J.The design and analysis of hierarchical decision-making for manned/unmanned cooperative engagement.Proc.of the 34th Chinese Control Conference,2015:2698-2703.
[5]CHEN C,ZHANG X W,XU J,et al.Human/unmanned-aerialvehicle team collaborative decision-making with limited intervention.Acta Aeronautica et Astronautica Sinica,2015,69(11):3652-3665.
[6]LIU Y F,ZHANG A.Cooperative task assignment method of manned/unmanned aerial vehicle formation.Systems Engineering and Electronics,2010,32(3):584-588.(in Chinese)
[7]CHOI H,BRUNET L,HOW J P.Consensus-based decentralized auctions for robust task allocation.IEEE Trans.on Robotics,2009,25(4):912-926.
[8]ZHONG Y,YAO P Y,SUN Y,et al.Cooperative task allocation method of MCAV/UCAV formation.Mathematical Problems in Engineering,2016,1(1):1-9.
[9]ZHANG Y,PARKER L E.Considering inter-task resource constraints in task allocation.Autonomous Agents and MultiAgent Systems,2013,26(3):389-419.
[10]HU X X,MA H W,YE Q S,et al.Hierarchical method of task assignment for multiple cooperating UAV teams.Journal of Systems Engineering and Electronics,2015,26(5):1000-1009.
[11]ZHANG Y Z,XIE S Y,ZHANG L,et al.Optimal task decision-making for heterogeneous multi-UAV cooperation reconnaissance.Journal of Northwestern Polytechnical University,2017,35(3):386-392.(in Chinese)
[12]SERVICE T C,ADAMS J A.Coalition formation for task allocation:theory and algorithms.Auton Agent and Multi-Agent Systerms,2011,22(2):225-248.
[13]HAN B W,YAO P Y.Coalition formation of manned/unmanned aerial vehicle cluster based on Holon organization.Systems Engineering and Electronics,2018,40(1):91-97.(in Chinese)
[14]ZHONG Y,YAO P Y,SUN Y,et al.Research on phasedforming method of manned/unmanned aerial vehicle task coalition.Systems Engineering and Electronics,2017,39(9):2031-2038.(in Chinese)
[15]CUELL C,BONSAL B.An assessment of climatological synoptic typing by principal component analysis and k-means clustering.Theoretical and Applied Climatology,2009,98(3-4):361-373.
[16]HONDA S,IGARASHI T,NARITA Y.Multi-objective optimization of curvilinear fiber shapes for laminated composite plates by using NSGA-II.Composites Part B:Engineering,2013,45(1):1071-1078.
[17]GHOLAMI M H,AZIZI M R.Constrained grinding optimization for time,cost,and surface roughness using NSGA-II.The International Journal of Advanced Manufacturing Technology,2014,73(5-8):981-988.
[18]ARUNACHALAM A,NAGARAJAN N P,MOHAN V,et al.Resolving team selection in agile development using NSGA-IIalgorithm.CSI Trans.on ICT,2016,4(2-4):83-86.
[19]COELLO C,LECHUNGA S.MOPSO:a proposal for multiple objective particles warm optimization.Proc.of the IEEECongress on Evolutionary Computation,2002:1050-1056.
[20]LI M Q,ZHENG J H.An indicator for assessing the spread of solutions in multi-objective evolutionary algorithm.Chinese Journal of Computers,2011,34(4):647-664.(in Chinese)