Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel). 2019 Feb 12;19(3):734. doi: 10.3390/s19030734.
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.
本文考虑了具有不同传感器能力的多架无人机（UAV）的侦察任务分配问题。采用改进的多目标共生生物搜索算法（MOSOS）来优化无人机的任务序列。为异构目标建立了基于时间窗的任务模型。然后，将基本任务分配问题表述为基于多时间窗的 Dubins 旅行商问题（MTWDTSP）。在逻辑和物理约束下，为任务分配问题建立了双链编码规则和多个标准。引入了 Pareto 优势确定和全局自适应缩放因子，以提高原始 MOSOS 的性能。本文还提出了任务分配问题的数值模拟和蒙特卡罗模拟结果，并与非支配排序遗传算法（NSGA-II）和原始 MOSOS 进行了比较，以验证所提出方法的优越性。模拟结果表明，在 MTWDTSP 中，改进的 SOS 在分配结果的最优性和效率方面优于原始 MOSOS 和 NSGA-II。
