Graph based Path Planning for Autonomous Robotic Exploration in Subterranean Environments
This paper presents a novel strategy for autonomous graph based exploration path planning in subterranean environments. Attuned 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 planner employs a rapidly exploring random graph to reliably and efficiently identify collision free 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 dead end (e.g. a mine heading) 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 in a collection of deployments inside both active and abandoned underground mines in the U.S. and in Switzerland.
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)