Path planning based on unmanned aerial vehicle performance with segmented cellular genetic algorithm

An important part of UAV technological development consists of improvements in the scope of path planning. Different choices can be made in path planning according to operational priorities, it may be preferred to reach the destination as fast as possible or to increase the airtime by compromising speed. For every speed and altitude that the UAV can fly; fuel data of cruise, climb and descent phases are used in the path planning algorithm. Thus, economical and airtime-maximizing paths could be produced on the basis of performance characteristics compatible with the kinematic constraints customized for the UAV. In this study, Cellular (cGA) and Segmented Cellular Genetic Algorithm (scGA) are proposed. The novel over protective algorithm which has a fixed initial population and segmented chromosome structure achieves a high convergence speed to optimal solution and can generate paths which have 5.2 times higher fitness value on average compared with a conventional Genetic Algorithm (GA). It has been seen that scGA improves the initial population in terms of the best solutions 1.9 times and the general population 5.8 times better compared with GA.