Graph-based Path Planning for Autonomous Robotic Exploration in Subterranean Environments
This work presents a new strategy for autonomous graph-based exploration path planning in subterranean environments. Tailored to the fact that subterranean settings such as underground mines are often large-scale networks of narrow tunnel-like and multi-branched topologies, the proposed planner is structured around a bifurcated local- and global-planner architecture. The local planer employs a rapidly-exploring random graph to reliably and efficiently identify collision-free paths that optimize for exploration gain in a local subspace. Accounting for the robot endurance limitations and the possibility that the local planner reaches a dead-end, the global planner is engaged when a return-to-home path must be derived or when the robot should be re-positioned towards an edge of the exploration space. The proposed planner is field evaluated inside real underground metal mines such as the Gonzen Mine in Switzerland.
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