We present an algorithm to explore an orthogonal polygon using a team of p robots. This algorithm combines ideas from information-theoretic exploration algorithms and computational geometry based exploration algorithms. We show that the exploration time of our algorithm is competitive (as a function of p) with respect to the offline optimal exploration algorithm. The algorithm is based on a single-robot polygon exploration algorithm, a tree exploration algorithm for higher level planning and a submodular orienteering algorithm for lower level planning. We discuss how this strategy can be adapted to real-world settings to deal with noisy sensors. In addition to theoretical analysis, we investigate the performance of our algorithm through simulations for multiple robots and experiments with a single robot.
@article{arxiv.2004.06856,
title = {Combining Geometric and Information-Theoretic Approaches for Multi-Robot Exploration},
author = {Aravind Preshant Premkumar and Kevin Yu and Pratap Tokekar},
journal= {arXiv preprint arXiv:2004.06856},
year = {2020}
}