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arXiv · 2311.12698

Informative Path Planning with Limited Adaptivity

Rayen Tan·Rohan Ghuge·Viswanath Nagarajan
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Abstract

We consider the informative path planning ($\mathtt{IPP}$) problem in which a robot interacts with an uncertain environment and gathers information by visiting locations. The goal is to minimize its expected travel cost to cover a given submodular function. Adaptive solutions, where the robot incorporates all available information to select the next location to visit, achieve the best objective. However, such a solution is resource-intensive as it entails recomputing after every visited location. A more practical approach is to design solutions with a small number of adaptive "rounds", where the robot recomputes only once at the start of each round. In this paper, we design an algorithm for $\mathtt{IPP}$ parameterized by the number $k$ of adaptive rounds, and prove a smooth trade-off between $k$ and the solution quality (relative to fully adaptive solutions). We validate our theoretical results by experiments on a real road network, where we observe that a few rounds of adaptivity suffice to obtain solutions of cost almost as good as fully-adaptive ones.

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Rayen Tan, Rohan Ghuge, Viswanath Nagarajan. 2023-11-21. Informative Path Planning with Limited Adaptivity. https://arxiv.org/abs/2311.12698

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