Autonomous Aerial Robotic Exploration of Subterranean Environments relying on Morphology–aware Path Planning

In this work the challenge of autonomous navigation, exploration and mapping in underground mines using aerial robots is considered. Despite the paramount importance of underground mine accessing, the relevant challenges of sensor degradation (darkness, dust, smoke) and broadly stringent navigation conditions due to particularly narrow geometries across very long drifts render typical navigation and planning methods insufficient. Towards a comprehensive solution, we present and extensively field test a variety of robot realizations implementing different sensor fusion and path planning strategies inside underground mine settings. We conclude and propose an optimized multi-modal sensor fusion approach combined with a local environment morphology-aware exploration path planning strategy that in their combination provide superior results in terms of navigation resourcefulness and resilience, exploration efficiency and mapping accuracy despite the large set of challenging conditions encountered.