Unmanned Aircraft Vehicle Autopilot Using Genetic Algorithm for Minimizing Blank Spot
Abstract
This paper presents the autopilot of unmanned aerial vehicles (UAV) with the ability to minimize blank spots on aerial mapping using the genetic algorithm. The purpose of the developed autopilot is to accelerate the times required for aerial mapping and save battery consumption. Faster time in conducting aerial mapping saves operational costs, saves battery consumption, and reduces UAV maintenance costs. The proposed autopilot has the ability to analyze blank spots from aerial shots and optimize flight routes for re-photography. The genetic algorithm was applied to obtain the shortest distance, which was done to save battery consumption and flight time. When developing the autopilot, the operator would manually set the flight route, then the aircraft would fly according to that route. The unstable wind factor has caused a shift in the flight route, which correspondingly caused blank spots. After all flight routes were traversed, the system developed would analyze the location of the blank spots. The new flight route was calculated using the genetic algorithm to determine the shortest distance from all the blank spot locations. The system developed consisted of a UAV equipped with autopilot and a ground control station (GCS). At the time of flight, the UAV would send the coordinates of the path traversed to the GCS to calculate the blank spot analysis. After the flight mission has been completed, the GCS would create a new route and send it to the UAV. The test carried out was an aircraft with a height of 120m using a 4S 4,200 mAh 25C lipo battery, and the percentage of throttle when flying straight was 30%. The results obtained are that the developed autopilot saves 46.4% of the time and saves 41.18% of battery capacity compared to conventional autopilots.
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