视距约束下的多机器人无图导航：基于多边形可见区域
Line-of-Sight-Constrained Multi-Robot Mapless Navigation via Polygonal Visible Regions
AI总结：
本文提出一种基于多边形可见区域的多机器人无图导航方法，通过分布式通信建立视距约束，提升在障碍物密集环境下的连接稳定性与导航效率。
摘要：
多机器人系统依赖底层连接性以确保可靠通信和及时协调。本文研究了在未知障碍物环境下多机器人导航中的视距（LoS）连接性维护问题。现有方法通常假设已知环境地图来建立机器人间的视距约束，限制了其实际应用。为克服这一限制，本文提出一种内在分布式方法，每个机器人仅基于实时LiDAR扫描构建以自身为中心的可见区域，而非构建全局地图。通过分布式通信共享个体可见区域以建立机器人间的视距约束，并将其纳入多机器人导航框架以确保视距连接性。此外，本文提出更准确的视距距离度量方法，进一步实现灵活的拓扑优化，消除冗余和费力的连接。所提框架通过大量多机器人导航和探索任务在仿真和现实实验中进行评估。结果表明，该框架在充满障碍物的挑战性环境中可靠地维持机器人间的视距连接性，即使在大可视范围和脆弱最小拓扑下，现有方法也持续失败。消融研究还显示，拓扑优化使导航效率提高约20%，证明了该框架在连接性约束下的导航潜力。
英文摘要：
Multi-robot systems rely on underlying connectivity to ensure reliable communication and timely coordination. This paper studies the line-of-sight (LoS) connectivity maintenance problem in multi-robot navigation with unknown obstacles. Prior works typically assume known environment maps to formulate LoS constraints between robots, which hinders their practical deployment. To overcome this limitation, we propose an inherently distributed approach where each robot only constructs an egocentric visible region based on its real-time LiDAR scans, instead of endeavoring to build a global map online. The individual visible regions are shared through distributed communication to establish inter-robot LoS constraints, which are then incorporated into a multi-robot navigation framework to ensure LoS-connectivity. Moreover, we enhance the robustness of connectivity maintenance by proposing a more accurate LoS-distance metric, which further enables flexible topology optimization that eliminates redundant and effort-demanding connections. The proposed framework is evaluated through extensive multi-robot navigation and exploration tasks in both simulation and real-world experiments. Results show that it reliably maintains LoS-connectivity between robots in challenging environments cluttered with obstacles, even under large visible ranges and fragile minimal topologies, where existing methods consistently fail. Ablation studies also reveal that topology optimization boosts navigation efficiency by around $20\%$, demonstrating the framework's potential for efficient navigation under connectivity constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
