Compression based distributed dynamic task assignment algorithms for heterogeneous multiple unmanned aerial vehicles

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.