Multi-Objective Autonomous Exploration on Real-Time Continuous Occupancy Maps
Robotics
2021-11-02 v1
Abstract
Autonomous exploration in unknown environments using mobile robots is the pillar of many robotic applications. Existing exploration frameworks either select the nearest geometric frontier or the nearest information-theoretic frontier. However, just because a frontier itself is informative does not necessarily mean that the robot will be in an informative area after reaching that frontier. To fill this gap, we propose to use a multi-objective variant of Monte-Carlo tree search that provides a non-myopic Pareto optimal action sequence leading the robot to a frontier with the greatest extent of unknown area uncovering. We also adopted Bayesian Hilbert Map (BHM) for continuous occupancy mapping and made it more applicable to real-time tasks.
Cite
@article{arxiv.2111.00067,
title = {Multi-Objective Autonomous Exploration on Real-Time Continuous Occupancy Maps},
author = {Zheng Chen and Weizhe Chen and Shi Bai and Lantao Liu},
journal= {arXiv preprint arXiv:2111.00067},
year = {2021}
}