The ability to traverse an unknown environment is crucial for autonomous robot operations. However, due to the limited sensing capabilities and system constraints, approaching this problem with a single robot agent can be slow, costly, and unsafe. For example, in planetary exploration missions, the wear on the wheels of a rover from abrasive terrain should be minimized at all costs as reparations are infeasible. On the other hand, utilizing a scouting robot such as a micro aerial vehicle (MAV) has the potential to reduce wear and time costs and increasing safety of a follower robot. This work proposes a novel cooperative IPP framework that allows a scout (e.g., an MAV) to efficiently explore the minimum-cost-path for a follower (e.g., a rover) to reach the goal. We derive theoretic guarantees for our algorithm, and prove that the algorithm always terminates, always finds the optimal path if it exists, and terminates early when the found path is shown to be optimal or infeasible. We show in thorough experimental evaluation that the guarantees hold in practice, and that our algorithm is 22.5% quicker to find the optimal path and 15% quicker to terminate compared to existing methods.

翻译：在未知环境中自主导航的能力对于机器人自主操作至关重要。然而，由于感知能力有限和系统约束，使用单一机器人智能体处理此问题通常效率低下、成本高昂且存在安全隐患。例如，在行星探测任务中，应不惜一切代价减少探测车车轮在磨蚀性地形上的磨损，因为维修并不可行。另一方面，利用微型飞行器（MAV）等侦察机器人，有望降低跟随机器人的磨损与时间成本，并提升其安全性。本研究提出了一种新颖的协同信息路径规划（IPP）框架，使侦察机器人（如MAV）能够高效探索出供跟随机器人（如探测车）抵达目标的最小成本路径。我们为该算法推导了理论保证，证明了算法始终会终止，若最优路径存在则必能找到，并在已发现路径被证明为最优或不可行时提前终止。通过全面的实验评估，我们验证了这些保证在实践中成立，且本算法相较于现有方法，寻找最优路径的速度快22.5%，终止速度快15%。