Autoencoder

Unmanned aerial vehicles may collide with obstacles, such as trees or other unmanned aerial vehicles, while flying. A waypoint-based flight path is an approach to avoid such obstacles. To specify waypoints for the safe flight of unmanned aerial vehicles, it is necessary to define a flight path in advance by analyzing the flight records of unmanned aerial vehicles and thereby designate the waypoints automatically. However, there is a problem in that pilots tend to make errors in controlling unmanned aerial vehicles and collecting flight records. This article proposes a method to generate candidate waypoints for a flight path by removing such unintended flight records. In this method, images representing the positions in the collected flight records are generated. The candidate waypoints are generated as positions corresponding to the overlapping pixels of the images generated via image accumulation based on the flight records and the ones generated by accumulating the images reconstructed using an Autoencoder. The unmanned aerial vehicles can be set the waypoints for an autonomous flight using the candidate waypoints. An experiment was conducted in a university to generate candidate waypoints for road monitoring. The results obtained using the proposed method and K-means algorithm were compared. The candidate waypoints generated using the proposed method were reduced by 84.21% compared to those generated using the K-means algorithm