多约束条件下的动态实时多无人机协同任务规划方法
Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
AI总结：
本文针对多无人机任务分配与路径规划解耦、实时性差等问题，提出基于Dubins路径和分布式编队的动态实时协同任务规划算法，并通过任务聚类、高效代价函数和低复杂度分配策略，在仅损失9.57%路径长度的条件下实现4至5个数量级的速度提升。
摘要：
随着无人机普及程度迅速提高，与之相关的任务规划需求也在不断增长。经典方法存在任务分配与路径规划相互解耦、实时性差和适应性有限这三重问题。针对这些挑战，本文在分布式编队结构下提出了一种基于Dubins路径的动态实时多无人机协同任务规划算法。具有多重优势的Dubins路径弥合了任务分配与路径规划之间的鸿沟，从而实现了任务规划的耦合求解。随后，采用一系列加速技术，包括任务聚类预处理、高效距离代价函数以及低复杂度且迭代次数更少的任务分配策略，以保证算法的实时性能。为应对不同紧急情况及其同时出现的极端情形，本文对突发任务的实时规划以及因可用无人机数量减少而进行的任务重规划进行了合理处理。最后，通过仿真对所开发的算法进行了全面展示和研究，结果表明，所提方法仅牺牲9.57%的路径长度，却相比模拟退火方法实现了4至5个数量级的速度提升，单次任务规划时间约为0.0003秒。
英文摘要：
As UAV popularity soars, so does the mission planning associated with it. The classical approaches suffer from the triple problems of decoupled of task assignment and path planning, poor real-time performance and limited adaptability. Aiming at these challenges, this paper proposes a dynamic real-time multi-UAV collaborative mission planning algorithm based on Dubins paths under a distributed formation structure. Dubins path with multiple advantages bridges the gap between task assignment and path planning, leading to a coupled solution for mission planning. Then, a series of acceleration techniques, task clustering preprocessing, highly efficient distance cost functions, low-complexity and less iterative task allocation strategies, are employed to guarantee the real-time performance of the algorithms. To cope with different emergencies and their simultaneous extremes, real-time planning of emerging tasks and mission replanning due to the reduction of available UAVs are appropriately handled. Finally, the developed algorithm is comprehensively exemplified and studied through simulations, highlighting that the proposed method only sacrifices 9.57% of the path length, while achieving a speed improvement of 4-5 orders of magnitude over the simulated annealing method, with a single mission planning of about 0.0003s.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
