不对称侦察员-工人侦察用于未知环境中的路线验证
Asymmetric Scout-Worker Reconnaissance for Route Validation in Unknown Environments
- Worcester Polytechnic Institute（伍斯特理工学院）
- Democritus University of Thrace（色雷斯德谟克利特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种共生侦察员框架，利用小型侦察员探索未知环境，为大型工人机器人验证并修复可行路线，显著减少侦察行程。
AI中文摘要：
本文研究了未知环境中的不对称侦察员-工人侦察问题，其中一个小型、灵活的自主侦察员为较大的工人机器人探索路线，该工人机器人必须访问一系列有序的目标位置。由于侦察员具有较小的占地面积和更高的机动性，侦察员可通行的路线可能对工人机器人不可行；因此，必须从侦察员的观察中推断工人的可行性。现有探索和重新规划方法通常假设单一的可通行性模型，并为执行探索的同一机器人寻求最优路径，未明确处理此设置。我们引入了一种共生侦察员框架，利用侦察员优越的机动性，仅探索未知环境中需要识别连接有序目标的工人可行路径段的部分。在模拟和真实世界设置中的评估表明，所提出的方法验证了可行路线，修复了阻塞段并带有经过验证的工人可行绕行，并且与基线探索和规划方法相比，显著减少了侦察员的行程。真实世界的室内部署进一步展示了侦察员在狭窄走廊中导航以识别工人可行路线。
英文摘要：
This paper studies asymmetric scout-worker reconnaissance in unknown environments, where a small, agile autonomous scout explores routes for a larger worker robot that must visit an ordered sequence of goal locations. Because the scout has a smaller footprint and greater mobility, a scout-traversable route may be infeasible for the worker; worker feasibility must therefore be inferred from scout observations. This setting is not explicitly addressed by existing exploration and replanning methods, which typically assume a single traversability model and seek optimal paths for the same robot performing the exploration. We introduce a symbiotic scout-based framework that exploits the scout's superior mobility to explore only the portions of the unknown environment needed to identify worker-feasible path segments connecting the ordered goals. Evaluations in simulated and real-world settings demonstrate that the proposed approach validates feasible routes, repairs blocked segments with validated worker-feasible detours, and substantially reduces scout travel compared to baseline exploration and planning methods. A real-world indoor deployment further demonstrates the scout navigating narrow corridors to identify a worker-feasible route.
