Impact of Uncertain Flight Time on Heterogeneous UAVs’ Task Planning with Temporal Constraints

Jianfeng Wang, Gaowei Jia, Zheng Guo, Zhongxi Hou
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Abstract

Heterogeneous multi-UAV systems offer distinct advantages through their complementary and coordinated use of their diverse capabilities. However, this complexity poses significant challenges in task planning, particularly in considering temporal constraints among tasks. As task dependencies evolve from simple linear chains to complex networked associations, uncertainties in flight times can have a substantial impact on the overall schedule. To address these challenges, this study introduces a rapid estimation method that recursively calculates task completion times, derives their probability distributions, and assesses the robustness of the plan. Furthermore, a neighborhood search algorithm guided by dynamic time windows is designed to effectively evaluate the consequences of task insertions, precisely to adjust high-risk tasks, and reduce blindness in enumerative neighborhood exploration. Simulation results demonstrate that the proposed approach effectively accounts for inherent randomness in the problem and exhibits strong adaptability to changes in the problem scale, flight time fluctuations, and variations in time window constraints.
飞行时间不确定对具有时间约束条件的异构无人机任务规划的影响
异构多无人机系统通过互补和协调使用不同的能力,具有明显的优势。然而,这种复杂性给任务规划带来了巨大挑战,尤其是在考虑任务之间的时间限制时。随着任务依赖关系从简单的线性链演变为复杂的网络关联,飞行时间的不确定性会对整体计划产生重大影响。为应对这些挑战,本研究引入了一种快速估算方法,该方法可递归计算任务完成时间、推导其概率分布并评估计划的稳健性。此外,还设计了一种由动态时间窗引导的邻域搜索算法,以有效评估任务插入的后果,精确调整高风险任务,并减少枚举邻域探索的盲目性。仿真结果表明,所提出的方法有效地考虑了问题中固有的随机性,并对问题规模的变化、飞行时间的波动和时间窗限制的变化表现出很强的适应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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