用分层区域图提升多机器人探索效率，省时20%。
A Hierarchical Region-Based Approach for Efficient Multi-Robot Exploration
- 构建区域图保留未知区域空间信息，替代传统前沿点建模
- 分层任务分解降低全局规划频率，支持异步探索
- 仿真与实测均验证效率提升20%，适合大规模机器人协同
在未知环境中进行多机器人自主探索是机器人学的重要应用。传统方法仅依赖前沿点或视点信息，忽略了未知区域的空间结构；同时，多机器人任务分配的最优解求解属于NP难问题，导致计算耗时严重。为此，本文提出一种基于区域图（RegionGraph）的层次化多机器人探索框架。该方法有两个主要贡献：1）提出一种新的未知区域建模方式，通过加权图形式的区域图，在整个空间中保持未知区域的空间信息；2）设计了一种层次化探索框架，将全局探索任务分解为更小的子任务，减少全局规划频率，实现异步探索。该方法在仿真和真实实验中均得到验证，相比现有方法效率提升20%。
原文摘要 · Abstract (English)
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.
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