Risk-aware Meta-level Decision Making for Exploration Under Uncertainty
This work addresses the challenge of autonomous robotic exploration under uncertainty, which is incremental as it builds upon classical hierarchical coverage planners.
The paper tackled the problem of robotic exploration in unknown environments by proposing a risk-aware meta-level decision making framework to balance local and global exploration, resulting in significantly more efficient exploration of large-scale environments as validated in simulation and real-world hardware tests.
Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, localization, action execution, as well as many other factors. For large-scale exploration applications, autonomous systems must overcome the challenges of sequentially deciding which areas of the environment are valuable to explore while safely evaluating the risks associated with obstacles and hazardous terrain. In this work, we propose a risk-aware meta-level decision making framework to balance the tradeoffs associated with local and global exploration. Meta-level decision making builds upon classical hierarchical coverage planners by switching between local and global policies with the overall objective of selecting the policy that is most likely to maximize reward in a stochastic environment. We use information about the environment history, traversability risk, and kinodynamic constraints to reason about the probability of successful policy execution to switch between local and global policies. We have validated our solution in both simulation and on a variety of large-scale real world hardware tests. Our results show that by balancing local and global exploration we are able to significantly explore large-scale environments more efficiently.