Abstract We explore a navigation problem for a simple robot with extremely noisy sensing and significant movement uncertainty. We are particularly interested in environments containing large regions in which relatively little distinguishing sensor information is available to assist with localization. This paper proposes a navigation algorithm for this setting that strategically directs the robot through such regions when possible, but with a careful view of the need to regain relatively accurate localization at certain points in the execution. Reasoning directly about the robot's uncertainty, the approach utilizes a local entropy metric to identify regions where sensors have strong informative value. This metric informs the selection of coarse global paths that guide a more precise local planner. We discuss an implementation of this algorithm, and provide simulation results demonstrating its effectiveness in spite of large errors in both sensing and actuation.
@inproceedings{CarOKa26,
author = {Bennett A. Carley and Jason M. O'Kane},
booktitle = {Proc. IEEE International Conference on Robotics and
Automation},
note = {To appear},
title = {A entropy-based hybrid local-global algorithm to navigate
information-sparse environments},
year = {2026}
}