Deep Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles - Grando, Ricardo; de Jesus, Junior; Drews-Jr, Paulo (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS), 2020)
This paper presents a deep reinforcement learningbased system for goal-oriented mapless navigation for Unmanned Aerial Vehicles (UAVs). In this context, image-based sensing approaches are the most common. However, they demand high processing power hardware which are heavy and difficult to embed into a small-autonomous UAV. Our approach is based on localization data and simple sparse range data to train the intelligent agent. We based our approach in two state-of-theart Deep-RL techniques for terrestrial robot: Deep Deterministic Policy Gradient (DDPG) and Soft Actor Critic (SAC). We compare the performance with a classic geometric-based tracking controller for mapless navigation of UAVs. Based on experimental results, we conclude that Deep-RL algorithms are effective to perform mapless navigation and obstacle avoidance for UAVs. Our vehicle successfully performed two proposed tasks, reaching the desired goal and outperforming the geometric-based tracking controller on the obstacle avoiding capability.