Abstract
Unmanned aerial vehicles (UAVs) with auto-pilot capabilities are often used for surveillance and patrol. Pilots set the flight points on a map in order to navigate to the imaging point where surveillance or patrolling is required. However, there is the limit denoting the information such as absolute altitudes and angles. Therefore, it is required to set the information accurately. This paper hereby proposes a method to construct environmental symmetric big data using an unmanned aerial vehicle (UAV) during flight by designating the imaging and non-imaging points for surveillance and patrols. The K-Means-based algorithm proposed in this paper is then employed to divide the imaging points, which is set by the pilot, into K clusters, and K imaging points are determined using these clusters. Flight data are then used to set the points to which the UAV will fly. In our experiment, flight records were gathered through an UAV in order to monitor a stadium and the imaging and non-imaging points were set using the proposed method and compared with the points determined by a traditional K-Means algorithm. Through the proposed method, the cluster centroids and cumulative distance of its members were reduced by 87.57% more than with the traditional K-Means algorithm. With the traditional K-Means algorithm, imaging points were not created in the five points desired by the pilot, and two incorrect points were obtained. However, with the proposed method, two incorrect imaging points were obtained. Due to these two incorrect imaging points, the two points desired by the pilot were not generated.
Highlights
Unmanned Aerial Vehicles (UAVs) [1] have been flown autonomously to record images for surveillance and patrols
Motor primitives are created using the UAV state data collected by the user and the surveillance point specified by the pilot
This paper proposes a method for expressing UAV flight points, for analyzing recorded UAV flight points and for generating UAV flight paths needed for autonomous flights
Summary
Unmanned Aerial Vehicles (UAVs) [1] have been flown autonomously to record images for surveillance and patrols. In order to fly UAVs autonomously, a method to set the flight path is required. In order to set a flight path for UAV surveillance and patrols, the following three requirements are necessary. This paper proposes a method that automatically categorizes a flight point based on the points that were and were not recorded by a pilot while flying a UAV for surveillance. The points that set the flight path are created using environment data from piloted UAV flight collected in advance.
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