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Autonomous task allocation for multi-UAV systems based on the locust elastic behavior

机译:基于蝗虫弹性行为的多UAV系统自主任务分配

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Task allocation is a grand challenge facing researches and practitioners in multiple unmanned aerial vehicles (multi-UAVs) missions. This paper proposes a new autonomous bio-inspired approach for efficiently allocating tasks among multiple UAVs during a mission. Task assignments are dynamically adjusted by each UAV on the basis of criteria related to the individual UAV operational status and mission parameters, without direct communication between the UAVs actively taking part in the mission. The proposed approach was inspired by the nature of locust species and their autonomous and elastic behavior in response to inside and outside impetus. Four long-standing multi-UAVs task allocation paradigms, including the auction-based, max-sum, ant colony optimization and opportunistic coordination schemes were used to benchmark the performance of the proposed approach. Experimental results demonstrated that the new approach substantially improves the net throughput and the mean task completion time while maintaining a linear running time when compared to all benchmarks under different scales of fleet size and number of tasks, demonstrating better scalability and sustainability. (C) 2018 Published by Elsevier B.V.
机译:任务分配是多个无人驾驶飞行器(多UVS)任务的研究和从业者面临的大挑战。本文提出了一种新的自主生物启发方法,可在任务期间有效地分配多个无人机之间的任务。任务分配根据与个人无人机运营状态和任务参数相关的标准,每个无人机都是由每个UAV调整的,而无人机之间的直接通信积极参与任务。拟议的方法受到蝗虫物种的性质及其自主和弹性行为的启发,响应于内外动力。四个长期的多无人机任务分配范例,包括基于拍卖,最大和蚂蚁殖民地优化和机会协调方案用于基准建议方法的性能。实验结果表明,新方法大大提高了净吞吐量和平均任务完成时间,同时保持线性运行时间,与不同队列规模和任务数量的不同比例下的所有基准相比,展示了更好的可扩展性和可持续性。 (c)2018由elsevier b.v发布。

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