Trail navigation with obstacle avoidance using convolutional neural networks

Abstract

In this paper, we propose a method of following bike trails while avoiding obstacles using convolutional neural networks (CNN) for an unmanned aerial vehicle (UAV). The direction of the UAV is controlled to follow the given trail, while keeping its position near the center of the trail using the CNN. In addition, to return to the UAV’s original path whenever it goes out of the path due to disturbance, the yaw rates from the CNN are stored and utilized the recent past. To avoid obstacles during the trail navigation, the optical flow estimated from another CNN is used. By integrating these methods, the UAV deals with various situations encountered while traveling on the road, and the proposed approach is verified through simulations using ROS and Gazebo.