Publication Type : Conference Paper
Publisher : Elsevier
Source : Proceedings of 22nd IFAC Symposium on Automatic Control in Aerospace
Url : https://www.sciencedirect.com/science/article/pii/S2405896323003051
Campus : Coimbatore
School : Department of Aerospace Engineering, School of Engineering
Department : Aerospace
Year : 2022
Abstract : This paper proposes a Reinforcement Learning (RL) based technique to develop a simple neural network controller for the task of waypoint navigation in quadrotors. In this paper, the application of Twin Delayed Deep Deterministic (TD3) Policy Gradient algorithm for high and low-level control implementation for quadrotors is discussed. The proposed methods are tested on high fidelity Gym-Pybullet-Drones simulator. The effectiveness of the methods developed is validated through numerical simulations. The simulation results indicate that both control policies are successful in navigating through the assigned waypoint, with the low-level controller being accurate in the nominal flight conditions. In the presence of disturbance inputs, the high-level controller performs better when compared to the low-level controller.
Cite this Research Publication : Himanshu, Harikumar, K., and Pushpangathan, J. V., “Waypoint Navigation of Quadrotor Using Deep Reinforcement Learning", Proceedings of 22nd IFAC Symposium on Automatic Control in Aerospace, Mumbai, India, Vol. 55, No. 22, pp. 281-286,2022. doi: 10.1016/j.ifacol.2023.03.047.