多无人机协同任务规划，实时高效且能应对突发情况。
Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
- 基于杜宾路径耦合任务分配与路径规划，提升整体效率。
- 单次规划仅需0.0003秒，比模拟退火快4-5个数量级。
- 可动态处理新任务或无人机减少等紧急情况，适合实战应用。
随着无人机普及，其任务规划问题日益突出。传统方法存在任务分配与路径规划分离、实时性差、适应性弱等问题。本文提出一种基于杜宾路径的分布式编队结构下多无人机协同实时任务规划算法。杜宾路径有效衔接任务分配与路径规划，实现联合求解。通过任务聚类预处理、高效距离代价函数及低复杂度迭代少的任务分配策略，保障算法实时性。针对突发任务或可用无人机减少等应急情况，支持实时新增任务规划与任务重规划。仿真验证表明，该方法仅牺牲9.57%路径长度，单次规划耗时约0.0003秒，较模拟退火提速4-5个数量级。
原文摘要 · Abstract (English)
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.
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