Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding
Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding
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DOI：
10.1016/j.cja.2017.09.005
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发表时间：
2018-02-01
影响因子：
5.7
通讯作者：
Wen, Yonglu
中科院分区：
文献类型：
--
作者：
Wang, Zhu;Liu, Li;Wen, Yonglu
提出了一种针对异构目标的多架无人机侦察任务分配模型，并提出了一种优化无人机任务序列的有效遗传算法。根据目标几何形状和传感器视场的特点，将异质目标分为点目标、线目标和面积目标。将每个无人机视为杜宾飞行器，考虑其运动学约束。任务分配的目标是最小化任务执行时间和无人机的总消耗。然后，将多无人机侦察任务分配形式化为扩展的多杜宾旅行推销员问题（MDTSP），其中由于目标的特征，到异构目标的访问路径必须满足特定的约束；MDTSP作为一个复杂的组合优化问题，由于目标的异质性，其维度进一步增加。为了有效地解决这一计算量大的问题，提出了基于双染色体编码和多突变算子的基于对立的遗传算法（OGA-DEMMO），以改善种群多样性，提高全局搜索能力。仿真结果表明，OGA-DEMMO算法在分配结果的最优性方面优于普通遗传算法、蚁群优化算法和随机搜索算法，尤其适用于大规模侦察任务分配问题。(C) 2017中国航空航天学会。由爱思唯尔有限公司制作和托管
This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs' task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor's field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs' total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets' feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems. (C) 2017 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.