提出双层拓扑结构，让机器人更高效探索未知环境。
DART: Dual-level Autonomous Robotic Topology for Efficient Exploration in Unknown Environments
- 构建低层拓扑图捕捉几何细节，再生成高层拓扑图表征凸区域布局。
- 相比传统方法，探索时间减少37%，旅行距离降低41%。
- 适合在地图质量差或障碍多的复杂环境中使用的自主探索系统。
传统自主探索算法难以实时准确识别真实地图中凸区域的空间分布，常优先向最近或信息丰富的前沿（已知与未知区域边界）导航，导致凸区域探索不完整，需大量回溯。为此，本文提出一种创新的双层拓扑分析方法：首先通过均匀采样原始地图数据生成低层拓扑图（LTG），捕捉关键几何与连通性信息；随后将LTG转换为高层拓扑图（HTG），表征凸区域的空间布局与探索完备性，优先探索未完全覆盖的凸区域并最小化无效回溯。最后引入新型局部人工势场（LAPF）进行运动控制，替代传统路径规划，显著提升整体效率。实验结果表明，仿真测试中本框架显著缩短探索时间与旅行距离，在速度与效率上优于现有方法。消融实验证明各模块至关重要。真实场景测试显示，该方法在地图质量差的环境下仍具鲁棒性，对映射误差和不可达区域具有更强适应能力。
原文摘要 · Abstract (English)
Conventional algorithms in autonomous exploration face challenges due to their inability to accurately and efficiently identify the spatial distribution of convex regions in the real-time map. These methods often prioritize navigation toward the nearest or information-rich frontiers -- the boundaries between known and unknown areas -- resulting in incomplete convex region exploration and requiring excessive backtracking to revisit these missed areas. To address these limitations, this paper introduces an innovative dual-level topological analysis approach. First, we introduce a Low-level Topological Graph (LTG), generated through uniform sampling of the original map data, which captures essential geometric and connectivity details. Next, the LTG is transformed into a High-level Topological Graph (HTG), representing the spatial layout and exploration completeness of convex regions, prioritizing the exploration of convex regions that are not fully explored and minimizing unnecessary backtracking. Finally, an novel Local Artificial Potential Field (LAPF) method is employed for motion control, replacing conventional path planning and boosting overall efficiency. Experimental results highlight the effectiveness of our approach. Simulation tests reveal that our framework significantly reduces exploration time and travel distance, outperforming existing methods in both speed and efficiency. Ablation studies confirm the critical role of each framework component. Real-world tests demonstrate the robustness of our method in environments with poor mapping quality, surpassing other approaches in adaptability to mapping inaccuracies and inaccessible areas.
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