A Parallel Algorithm for UAV Flight Route Planning on GPU
Aerial surveillance missions require a geographical region known as the area of interest to be inspected. The route that the aerial reconnaissance vehicle will follow is known as the flight route. Flight route planning operation has to be done before the actual mission is executed. A flight route may consist of hundreds of pre-defined geographical positions called waypoints. The optimal flight route planning manages to find a tour passing through all of the waypoints by covering the minimum possible distance. Due to the combinatorial nature of the problem it is impractical to devise a solution using brute force approaches. This study presents an approach to find a near-optimal solution to the flight route planning problem. The proposed approach is based on converting the problem into Traveling Salesman Problem which is solved using Genetic Algorithms on Graphical Processing Unit (GPU). The parallel genetic algorithm devised for GPUs has been compared to the alternative algorithms and found to be promising in terms of speed-up. We also present a thorough analysis of the implemented algorithm for several cases using different parameter values.
flight route planning gpu aerial surveillance waypoint cuda