Distributed Recursive Hungarian-based Approaches to Fast Task Allocation for Unmanned Aircraft Systems
Arezoo Samiei, Liang Quan Sun · AIAA Scitech 2020 Forum · 2020
In this paper, we present a novel distributed Hungarian-based approach to multi-task allocation problems, in which the number of agents is smaller than the number of the tasks. By introducing the dummy agents and dummy tasks in each iteration, we can build a squared cost matrix so that the Hungarian algorithm can be applied. We then remove the assigned tasks from the pool of tasks and rebuild a squared cost matrix by adding necessary dummy agents and tasks in an iteration. This process is recursively applied until all tasks are assigned. The proposed distributed recursive Hungarian-based algorithm (DRHBA) does not need any path-planning algorithm as what other existing algorithms do. We compare the proposed DRHBA with the consensus-Based Bundle Algorithm (CBBA) in Monti-Carlo simulation with different number of agents and tasks. The result shows that the proposed DRHBA outperforms CBBA on the basis of computational time, while performs equivalently to CBBA on the basis of overall cost.