Compression based distributed dynamic task assignment algorithms for heterogeneous multiple unmanned aerial vehicles
Li Wang, Qiao Guo · 2017
For the dynamic mission scenarios with task deadline constraints, we present two online task assignment algorithms for multiple unmanned aerial vehicles: the distributed deep compression algorithm (DDCA) and the distributed quick compression algorithm (DQCA). The two methods based on a compression strategy aim at directly optimizing the mission span as their objective by considering the long-term benefits and the current results, respectively. These algorithms all include a task calculation phase, a consensus and compression phase and a task update phase, running on each UAV in an iterative fashion. The methods are simple, efficient and anytime, which reach good solution in a relatively short time. Numerical results show that the proposed algorithms perform better in various conditions when compared with the classic SSIA algorithm.