Multi-Objective Autonomous Exploration on Real-Time Continuous Occupancy Maps

TL;DR

This paper introduces a multi-objective Monte-Carlo tree search approach for autonomous exploration, optimizing for informative frontiers in real-time continuous occupancy maps, enhancing exploration efficiency.

Contribution

It presents a novel multi-objective Monte-Carlo tree search method combined with Bayesian Hilbert Maps for improved autonomous exploration in unknown environments.

Findings

Effective identification of informative frontiers

Enhanced real-time occupancy mapping

Improved exploration efficiency

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.