Intelligent multi-robot exploration in non-exposed spaces: methods and challenges

Abstract

Multi-robot exploration in non-exposed spaces, such as underground tunnels, disaster-stricken areas, and planetary subsurface environments (e.g., lunar lava tubes, Martian caves), presents significant challenges due to limited perception, complex terrain, and the absence of Global Navigation Satellite System signals. Overcoming these challenges requires a combination of advanced perception techniques, intelligent path planning algorithms, and multi-robot coordination strategies. This paper systematically categorizes state-of-the-art methods into three key areas: environmental perception, path planning, and multi-agent coordination. We review the progression from traditional exploration methods to artificial intelligence-based techniques, including deep reinforcement learning and graph neural networks, which have demonstrated improved multi-robot performance. Moreover, we examine sensor fusion techniques, uncertainty modeling, and adaptive decision-making frameworks. Furthermore, we analyze major challenges, including limited communication, real-time adaptability, and decentralized coordination in multi-robot systems. Addressing these challenges requires more robust task allocation mechanisms, enhanced map representation strategies, and efficient learning-based control frameworks. Finally, we discuss future research directions, emphasizing scalable multi-robot intelligence, enhanced perception models, and resilient collaboration frameworks to improve exploration efficiency in extreme environments.