Integrated moment-based LGMD and deep reinforcement learning for UAV obstacle avoidance
Lei He, Nabil Aouf, James F. Whidborne, Bifeng Song
Abstract
In this paper, a bio-inspired monocular vision perception method combined with a learning-based reaction local planner for obstacle avoidance of micro UAVs is presented. The system is more computationally efficient than other vision-based perception and navigation methods such as SLAM and optical flow because it does not need to calculate accurate distances. To improve the robustness of perception against illuminance change, the input image is remapped using image moment which is independent of illuminance variation. After perception, a local planner is trained using deep reinforcement learning for mapless navigation. The proposed perception and navigation methods are evaluated in some realistic simulation environments. The result shows that this light-weight monocular perception and navigation system works well in different complex environments without accurate depth information.