Safe Learning for Multi-Robot Mapless Exploration

Abstract

When using deep reinforcement learning (DRL) to perform multi-robot exploration in unknown environments, the training model may produce actions that lead to unpredictable system behaviors due to the complexity and unpredictability of the surroundings. Therefore, ensuring safe exploration with DRL becomes critical. To tackle this issue, we propose a multi-agent framework that utilizes the formation scheme based on intermediate estimator compensation (IEC) to address the uncertainties introduced by DRL to ensure safe exploration. The convergence of the proposed scheme is verified via the Lyapunov method in the presence of tracking errors. An actor-critic-based DRL method is proposed for each mobile robot to deal with collision avoidance tasks. To enhance the efficiency of obtaining the DRL training model, a consensus-based training policy is introduced. The proposed safe learning framework successfully addresses uncertainties introduced by DRL while ensuring mapless exploration in both simulations and real-world experiments. The experimental video is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/ce99FxKFFtY</uri>.