Drones are intelligent devices that offer solutions for a continuously expanding variety of applications. Therefore, there would be a significant improvement if these systems could explore space automatically and without human-supervision. This work integrates cutting-edge artificial intelligence techniques that allow drones to travel independently. Following an overview of reinforcement learning methods built for discrete action space settings, a multilayer Perceptron model is constructed for feature extraction along with Hybrid neural networks. The agent employed in the experiments is a Rainbow DQN agent trained on the AirSim simulator. The experimental results are encouraging as the agent was tested for 16 missions and the accuracy was higher than 93%. In particular, the success for action selection was 97% and 93 for mission success. Finally, future work related to the navigation of autonomous drones is discussed including current concepts and methods of integration with more sophisticated algorithmic approaches.
CITATION STYLE
Karatzas, A., Karras, A., Karras, C., Giotopoulos, K. C., Oikonomou, K., & Sioutas, S. (2022). On Autonomous Drone Navigation Using Deep Learning and an Intelligent Rainbow DQN Agent. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13756 LNCS, pp. 134–145). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21753-1_14
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