Traversability Aware graph-based Subterranean Exploration with Unmanned Aerial Vehicles*

Subterranean exploration and mapping for search and rescue robotics have become an emerging research direction since the DARPA organized Subterranean Challenge. As part of development efforts within the team CoSTAR (Collaborative SubTerranean Autonomous Robots) in the Sub-T challenge, this work establishes a novel traversable graph-based exploration strategy that utilizes frontiers for local navigation and a fast collision risk-aware graph building for global navigation. The exploration strategy extracts frontiers in an unknown area that contribute to safe navigation while maximizing information gain for the robot. The exploration problem is further bifurcated into local and global exploration for faster decision-making at junctions with the goal of rapidly exploring the area. The local exploration guarantees collision-free straight-line paths to informative frontiers for rapid forward navigation, while global re-positioning utilizes a traversable graph subject to geometrical collision checks within the occupancy map. The pathfinding in a graph is addressed using a heuristic, which combines risk margins and travel costs to assist in short yet safe paths to the global frontier in case of a dead end in local exploration. The presented exploration strategy is developed with the goal of making exploration algorithms platform agnostic in order to be able to use it with aerial, as well as ground robots. The proposed method is also evaluated against different state-of-the-art exploration planners in simulated fixed-time budget-based exploration missions on an Unmanned Aerial Vehicle (UAV) in order to benchmark the capabilities and highlight the novelty.