中央研究院 資訊科學研究所

This talk describes the architecture and implementation of a heterogeneous team comprising unmanned ground vehicles and blimp robots capable of navigating unknown subterranean environments for search and rescue missions. The ground vehicles are equipped with a range of sensors for accurate perception, localization, and mapping. The blimps feature a long flight duration and collision tolerance when traversing uneven terrain. The design of the system was meant to satisfy the requirements of the DARPA Subterranean Challenge in terms of perception capability and autonomy. To facilitate navigation through smoke-filled spaces, we employed novel millimeter wave radar to enable cross-modal representations for integration via deep reinforcement learning. The autonomy of the proposed scheme was augmented using simulations to train deep neural networks, thereby allowing the system to perform sequential decision-making for collision avoidance and navigation toward a specific goal. The navigation system was evaluated in the DARPA SubT Urban Circuit, and quantitative localization results and recovery strategy from failures was discussed. We also discuss the lessons learned during this project and reflect on future plans.

Dr. Wang is an associate professor in the Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Taiwan. He was a postdoctoral associate in the Marine Robotics Group (MRG) in the Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT from 2013 to 2016. Dr. Wang received Ph.D. from the Department of Computer Science, UMass Boston, and his B.S. and M.S. in National Taiwan University.