An Interrelated Imitation Learning Method for Heterogeneous Drone Swarm Coordination

The proliferation of small drones has boosted diverse intelligent services, in which the effective swarm coordination plays a vital role in enhancing execution efficiency. However, owing to unreliable air communication and heterogeneous computation capabilities, it is difficult to achieve coordinated actions particularly in distributed scenarios with incomplete observations. In this article, we utilize the generative adversarial imitation learning (GAIL) model to coordinate the drones' maneuvers by imitating the peer's demonstrations. However, incomplete observations will lead to inaccurate imitation policies. In order to recover true environment states, we encode historical observation-action trajectories into latent belief representations, which are trained in correlation to imitation policies. Moreover, by merging the trace of historical contexts, the prediction of future states and the action-assisted guidance information, we gain robust belief representations, which lead to more accurate imitation policies. We evaluate the algorithm performance via the drones' formation control task. Experiment results display the superiorities on imitation accuracy, execution time and energy cost.