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Consensus-based bundle algorithm with local replanning for heterogeneous multi-UAV system in the time-sensitive and dynamic environment
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2021-06-16 , DOI: 10.1007/s11227-021-03940-z
Jie Chen , Xianguo Qing , Fang Ye , Kai Xiao , Kai You , Qian Sun

Consensus-based bundle algorithm (CBBA) is a decentralized task allocation algorithm that can produce feasible and conflict-free task assignment solution for multi-UAV system in the search and rescue scenarios. Further considering the new emerging tasks, this paper studies how to realize task reasssignment in the time-sensitive and dynamic environment. Effective task replanning algorithm aims to maximize the score value of task replanning solution when ensuring the timely allocation of the new task. Thus, an extension of CBBA called CBBA with local replanning (CBBA-LR) is proposed to produce reliable task replanning solution with quick response to the new task. Firstly, the capable matrix is adopted in CBBA-LR to denote the capable relationship between UAVs and tasks. Only capable UAVs for the new task are included in the task replanning. Then, the performing time list is introduced to the bid lists. For each UAV, CBBA-LR selects the assigned tasks whose performing times overlap the time window of the new task as the potential reset tasks. The setting of potential reset tasks effectively reduces the number of tasks included in the replanning process. After that, each UAV selects the nearest task to the new task from the potential reset task set as the reset task. Hence, CBBA-LR resets the most likely insert position of the new task from each UAV’s path. Finally, CBBA runs based on the reset task schedules to get the task replanning solution. Numerical simulations demonstrate the solution quality and convergence time of CBBA-LR from four perspectives: different time windows of the new task, different locations of the new task, continuous appearance of new tasks and different scales of search and rescue scenarios. The simulation results verify the feasibility and superiority of CBBA-LR compared with other replanning strategies.



中文翻译:

时效动态环境下异构多无人机系统局部重新规划的基于共识的捆绑算法

基于共识的捆绑算法(CBBA)是一种分散的任务分配算法,可以在搜索和救援场景中为多无人机系统产生可行且无冲突的任务分配解决方案。进一步考虑到新出现的任务,本文研究了如何在时间敏感和动态环境下实现任务重新分配。有效的任务重新规划算法旨在在保证新任务及时分配的情况下最大化任务重新规划方案的分值。因此,提出了一种名为 CBBA with local replanning (CBBA-LR) 的 CBBA 扩展,以产生可靠的任务重新规划解决方案,并对新任务做出快速响应。首先,CBBA-LR采用能力矩阵来表示无人机与任务之间的能力关系。只有能够完成新任务的无人机才包含在任务重新规划中。然后,在投标清单中引入了执行时间清单。对于每架无人机,CBBA-LR 选择执行时间与新任务时间窗口重叠的分配任务作为潜在的重置任务。潜在重置任务的设置有效减少了重新规划过程中包含的任务数量。之后,每架无人机从潜在重置任务集中选择离新任务最近的任务作为重置任务。因此,CBBA-LR 从每个 UAV 的路径重置新任务最可能的插入位置。最后,CBBA根据重置的任务调度运行,得到任务重新规划方案。数值模拟从四个角度展示了CBBA-LR的求解质量和收敛时间:新任务的不同时间窗口、新任务的不同位置、新任务的不断出现和不同规模的搜救场景。仿真结果验证了CBBA-LR与其他重新规划策略相比的可行性和优越性。

更新日期:2021-06-17
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