Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments
This work addresses the unsolved challenge of autonomous UAV navigation for applications in outdoor exploration, though it appears incremental as it builds upon existing deep reinforcement learning methods with modifications.
The paper tackles autonomous UAV navigation in varying outdoor environments and weather conditions by proposing an Extended Double Deep Q-Network (EDDQN) that uses a double state-input strategy, and it demonstrates that this approach consistently outperforms adapted Deep Q-Network, Double Deep Q-Network, and Deep Recurrent Q-Network in unseen environments and severe weather conditions.
With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying environments and weather conditions remains a highly desirable but as-of-yet unsolved challenge. In this work, we use Deep Reinforcement Learning to continuously improve the learning and understanding of a UAV agent while exploring a partially observable environment, which simulates the challenges faced in a real-life scenario. Our innovative approach uses a double state-input strategy that combines the acquired knowledge from the raw image and a map containing positional information. This positional data aids the network understanding of where the UAV has been and how far it is from the target position, while the feature map from the current scene highlights cluttered areas that are to be avoided. Our approach is extensively tested using variants of Deep Q-Network adapted to cope with double state input data. Further, we demonstrate that by altering the reward and the Q-value function, the agent is capable of consistently outperforming the adapted Deep Q-Network, Double Deep Q- Network and Deep Recurrent Q-Network. Our results demonstrate that our proposed Extended Double Deep Q-Network (EDDQN) approach is capable of navigating through multiple unseen environments and under severe weather conditions.