Obstacle Avoidance Method for Autonomous UAV for Structural Health Monitoring

This chapter presents an obstacle avoidance method (OAM) to realize an autonomous collision-avoiding unmanned aerial vehicle (UAV) for the collection of structural data from civil infrastructure. OAM is of high importance to avoid serious accidents during autonomous flights of UAVs for monitoring purposes. A collision must be avoided to avert the loss of human and financial loss. Therefore, this chapter provides a new unique real-time OAM that consists of four steps: obstacle detection, obstacle clustering, distance estimation, and generation of new waypoints. For obstacle detection, deep learning algorithm YOLOv3 is implemented, which can detect obstacles at 10 frames per second during the flight. Obstacles are detected in the form of bounding boxes in the image stream of the UAV. Next, if more than one obstacle is detected, the k-means clustering algorithm is used to group the obstacles based on their relative position and the nearest obstacle group is selected to be avoided first. Then the distance from the nearest obstacle to the UAV is estimated using monocular depth estimation and a pinhole camera model. If the obstacle is dangerously close to the UAV, it must be avoided. For this purpose, a new obstacle avoidance waypoint is generated based on the obstacle position and flight path of the UAV. Experimentally, it has been shown that our obstacle avoidance method has real-time and robust performance compared to existing state-of-the-art OAM methods.