Paper ID: 2210.08107

Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy

Shamak Dutta, Nils Wilde, Pratap Tokekar, Stephen L. Smith

We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the resources used (samples or tour length) while guaranteeing estimation accuracy. We give approximation algorithms for both problems in convex environments. These improve previously known results, both in terms of theoretical guarantees and in simulations. In addition, we disprove an existing claim in the literature on a lower bound for a solution to the sample placement problem.

Submitted: Oct 14, 2022