Graphbased Path Planning for Autonomous Robotic Exploration in Subterranean Environments This paper presents a novel strategy for autonomous graphbased exploration path planning in subterranean environments. Attuned to the fact that subterranean settings, such as underground mines, are often largescale networks of narrow tunnellike and multibranched topologies, the proposed planner is structured around a bifurcated localand globalplanner architecture. The local planner employs a rapidlyexploring random graph to reliably and efficiently identify collisionfree paths that optimize an exploration gain within a local subspace. Accounting for the robot endurance limitations and the possibility that the local planner reaches a deadend (e.g. a mine heading), the global planner is engaged when a returntohome path must be derived or when the robot should be repositioned towards an edge of the exploration space. The proposed planner is field evaluated in a collection of deployments inside both active and abandoned underground mines in the U.S. and in Switzerland.

Explore Locally, Plan Globally: A Path Planning Framework for Autonomous Robotic Exploration in Subterranean Environments This paper presents a path planning strategy for the autonomous exploration of subterranean environments. Tailored to the specific challenges and particularities of underground settings, and especially the fact that they often are extremely large in scale, tunnellike, narrow and multibranched, the proposed method employs a bifurcated local and globalplanning design to enable exploration efficiency and path planning solution resourcefulness. The local planner builds on top of minimumlength random trees and efficiently identifies collisionfree paths that optimize for exploration within a local subspace, while simultaneously ensuring enhanced obstacle clearance and thus safety. Accounting for the robot endurance limitations and the possibility that the local planner reaches a deadend (e.g. a mine heading), the global planner utilizes an incrementally built graph to search within the full range of explored space and is engaged when the robot should be repositioned towards a frontier of the exploration space or when a returntohome path must be derived. The proposed approach is field evaluated in a set of deployments in an exploratory underground mine drift in Northern Nevada.