🤖 AI Summary
To address two key bottlenecks in multi-robot autonomous exploration under unknown environments—insufficient spatial structural modeling of unexplored regions and high computational complexity (NP-hard) of global task allocation—this paper proposes an efficient collaborative framework based on region modeling and hierarchical planning. We introduce a novel RegionGraph weighted graph model that explicitly encodes the spatial topology and explorability of unknown regions. A hierarchical asynchronous exploration mechanism is designed to dynamically decompose global tasks into local subtasks, significantly reducing re-planning frequency. The framework integrates region segmentation, graph-based modeling, hierarchical task assignment, and frontier-point-driven exploration. Evaluated through both simulation and real-robot experiments, it achieves a 20% improvement in exploration efficiency over state-of-the-art methods, reduces mission completion time, and enhances system scalability.
📝 Abstract
Multi-robot autonomous exploration in an unknown environment is an important application in robotics.Traditional exploration methods only use information around frontier points or viewpoints, ignoring spatial information of unknown areas. Moreover, finding the exact optimal solution for multi-robot task allocation is NP-hard, resulting in significant computational time consumption. To address these issues, we present a hierarchical multi-robot exploration framework using a new modeling method called RegionGraph. The proposed approach makes two main contributions: 1) A new modeling method for unexplored areas that preserves their spatial information across the entire space in a weighted graph called RegionGraph. 2) A hierarchical multi-robot exploration framework that decomposes the global exploration task into smaller subtasks, reducing the frequency of global planning and enabling asynchronous exploration. The proposed method is validated through both simulation and real-world experiments, demonstrating a 20% improvement in efficiency compared to existing methods.