In this work we present a new methodology on learning-based path planning for autonomous exploration of subterranean environments using aerial robots. Utilizing a recently proposed graph-based path planner as a ``training expert'' and following an approach relying on the concepts of imitation learning, we derive a trained policy capable of guiding the robot to autonomously explore underground mine drifts and tunnels. The algorithm utilizes only a short window of range data sampled from the onboard LiDAR and achieves an exploratory behavior similar to that of the training expert with a more than an order of magnitude reduction in computational cost, while simultaneously relaxing the need to maintain a consistent and online reconstructed map of the environment. The trained path planning policy is extensively evaluated both in simulation and experimentally within field tests relating to the autonomous exploration of underground mines.
Russell Reinhart, Tung Dang, Emily Hand, Christos Papachristos, and Kostas Alexis, "Learning-based Path Planning for Autonomous Exploration of Subterranean Environments", IEEE International Conference on Robotics and Automation (ICRA) 2020, May 31 - June 4 2020, Paris, France.
R. Reinart, T. Dang, and K. Alexis are with the Autonomous Robots Lab of the University of Nevada, Reno
E. Hand is with the Machine Perception Lab of the University of Nevada, Reno
C. Papachristos is with CoRoboWork of the University of Nevada, Reno