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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm
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Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

机译：基于改进共生生物搜索算法的异构目标多无人机侦察任务分配

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摘要

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.
机译：本文考虑了具有不同传感器容量的多架无人机的侦察任务分配问题。修改后的多目标共生生物搜索算法（MOSOS）被用于优化无人机的任务序列。为异构目标建立了基于时间窗口的任务模型。然后，将基本任务分配问题表述为基于多个时间窗口的杜宾斯旅行商问题（MTWDTSP）。针对逻辑和物理约束条件下的任务分配问题，建立了双链编码规则和几个标准。引入了帕累托优势控制和全局自适应缩放因子，以提高原始MOSOS的性能。本文还给出了任务分配问题的数值模拟和蒙特卡洛模拟结果，并与非主导排序遗传算法（NSGA-II）和原始MOSOS进行了比较，以验证该方法的优越性。仿真结果表明，改进后的SOS在MTWDTSP中分配结果的最优性和效率方面优于原始MOSOS和NSGA-II。

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