Map-less navigation of an hybrid aero-acquatic vehicle using reinforcement learning - Grando, Ricardo B.; Drews-Jr, Paulo L. J. (Companion Proceedings of the Brazilian Symposium of Robotics & Latin American Robotics Symposium (SBR/LARS); 2021: Companion Proceedings of the 13th Brazilian Symposium of Robotics & 18th Latin American Robotics SymposiumDO - 10.5753/wtdr_ctdr.2021.18682, 2021)
Performing navigation between environments is a challenging task for hybrid mobile robots, especially in scenarios with obstacles. This work presents an approach based on deep reinforcement learning (Deep-RL) for autonomous navigation of a specific type of hybrid mobile robot: a Hybrid Unmanned Aerial Underwater Vehicle (HUAUV). Our approach uses only information from range findings sensors and the vehicle’s relative localization data to perform the navigation. Results show that it is possible to perform mapless navigation from start to finish without any manual operation, only using Deep-RL-based agents. The navigation performed by the agents is compared with the mapless navigation performed by a classic algorithm - BUG2. We based our approach in two stateof-art Deep-RL techniques for mapless navigation of terrestrial robots: Deep Deterministic Policy Gradient (DDPG) and Soft Actor Critic (SAC).