Multi-Objective Autonomous Exploration on Real-Time Continuous Occupancy Maps
This work addresses a specific bottleneck in robotic exploration for mobile robots, offering an incremental improvement over existing frontier-based methods.
The paper tackled the problem of autonomous robot exploration by addressing the limitation that informative frontiers may not lead to informative robot positions, proposing a multi-objective Monte-Carlo tree search method to find Pareto optimal action sequences, resulting in improved unknown area uncovering.
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.