This paper presents a deep reinforcement learning-based 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-the-art 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.
Deep Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles
09.11.2020
3042685 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Unmanned Aerial Vehicles Path Planning Based on Deep Reinforcement Learning
Springer Verlag | 2019
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