Autonomously tracking a dynamic unmanned ground vehicle (UGV) with an unmanned aerial vehicle (UAV) is challenging due to the difficulty for the UAV to track the UGV’s real-time state and adjust its policy accordingly. In this paper, we propose a deep reinforcement learning approach for a quadrotor UAV to track a moving UGV without knowing its motion dynamics. The proposed learning framework consists of two main systems: a Tracking Vision System (TVS) utilizing deep learning and a Tracking Control System (TCS) utilizing deep reinforcement learning. The TVS recognizes the UGV by analyzing images captured by the UAV, and then it provides the TCS with input data that is used to fulfill Markov Decision Process (MDP) in the TCS. The TCS generates optimal landing policies learned through simulated training. Moreover, we propose an Expert Knowledge Transfer (EKT) algorithm that allows human intervention in the UAV’s action space, which can speed up the training process. We demonstrate the effectiveness of the proposed method in simulation experiments.
A Deep Reinforcement Learning-Based Approach for Autonomous Quadrotor Tracking Control
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2023 ; Nanjing, China September 09, 2023 - September 11, 2023
Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) ; Chapter : 14 ; 145-154
2024-04-23
10 pages
Article/Chapter (Book)
Electronic Resource
English
Inverse Reinforcement Learning Control for Trajectory Tracking for a Quadrotor UAV
BASE | 2014
|Attitude Control Based on Reinforcement Learning for Quadrotor
TIBKAT | 2022
|