A distributed strategy for the multi-target rescue using a UAV swarm under communication constraints

Abstract

The current designs of the cooperative decision-making of an unmanned aerial vehicle (UAV) swarm usually adopt unreasonable assumptions on the communication ability between UAVs.Focusing on a multi-target rescue problem of a UAV swarm under constraints of energy, load and path, the limitation on the information sharing due to the communication constraints and the flight path of UAVs were taken into account.Firstly, the problem was formulated as a partially observable Markov decision process (POMDP).Then, a recurrent neural network was used to propose a deep-reinforcement-learning-based distributed rescue strategy, which is able to adapt to the changeable communication topology.Simulation results show that the proposed strategy outperforms other strategies under communication constraints, and further show that a careful joint setting of the size and communication ability of a UAV swarm is needed to achieve the best compromise between the UAV swarm rescue performance and the cost.

Keywords

• unmanned aerial vehicle;multi-target rescue;Markov decision process;distributed strategy;reinforcement learning