In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.
@article{arxiv.2003.10923,
title = {Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach},
author = {Omar Bouhamed and Hakim Ghazzai and Hichem Besbes and Yehia Massoud},
journal= {arXiv preprint arXiv:2003.10923},
year = {2020}
}
Comments
This paper is accepted for publication in IEEE International Symposium on Circuits and Systems (ISCAS'20), Seville, Spain, Oct. 2020