Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed

Abstract

Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in multitarget environment poses huge challenges for a multirobot team with limited coverage and limited communication. In particular, the robot team needs to cover multiple targets while preserving connectivity. In this article, a novel deep-reinforcement-learning-based approach is proposed to take both multitarget coverage and connectivity preservation into account simultaneously, which consists of four parts: a hierarchical observation attention representation, an interaction attention representation, a two-stage policy learning, and a connectivity-guaranteed policy filtering. The hierarchical observation attention representation is designed for each robot to extract the latent features of the relations from its neighboring robots and the targets. To promote the cooperation behavior among the robots, the interaction attention representation is designed for each robot to aggregate information from its neighboring robots. Moreover, to speed up the training process and improve the performance of the learned policy, the two-stage policy learning is presented using two reward functions based on algebraic connectivity and coverage rate. Furthermore, the learned policy is filtered to strictly guarantee the connectivity based on a model of connectivity maintenance. Finally, the effectiveness of the proposed method is validated by numerous simulations. Besides, our method is further deployed to an experimental platform based on quadrotor unmanned aerial vehicles and omnidirectional vehicles. The experiments illustrate the practicability of the proposed method.