Autonomous and Adaptive Role Selection for Multi-Robot Collaborative Area Search Based on Deep Reinforcement Learning

In the tasks of multi-robot collaborative area search, we propose the unified approach for simultaneous mapping for sensing more targets (exploration) while searching and locating the targets (coverage). Specifically, we implement a hierarchical multi-agent reinforcement learning algorithm to decouple task planning from task execution. The role concept is integrated into the upper-level task planning for role selection, which enables robots to learn the role based on the state status from the upper-view. Besides, an intelligent role switching mechanism enables the role selection module to function between two timesteps, promoting both exploration and coverage interchangeably. Then, the primitive policy learns how to plan based on their assigned roles and local observation for sub-task execution. The well-designed experiments show the scalability and generalization of our method compared with state-of-the-art approaches in scenes with varying complexity and numbers of robots. Our code is released at https://github.com/linaug/Role_selection Note to Practitioners—In the realm of area search tasks, the collaboration among multiple robots in a multi-robot system brings about a reduction in labor costs and an enhancement in task completion efficiency. Recognizing this, the development of an intelligent multi-robot collaborative decision-making algorithm becomes crucial-especially within complex and diverse environments, ensuring better adaptability to various real-world scenarios. This paper introduces a hierarchical framework designed for the simultaneous mapping and searching of targets in multi-robot area search tasks. The upper-level role selection framework employs adaptive role learning through deep reinforcement learning, while the low-level primitive policy focuses on planning actions based on the designated roles. As a result, the proposed approach exhibits notable advantages in terms of both the percentage and efficiency of task completion, particularly evident in well-designed scenes with varying complexities. We believe this framework holds significant potential for real-world applications, addressing challenges in multi-robot collaboration across diverse environments.