USE OF GENETIC ALGORITHMS FOR UNMANNED AERIAL SYSTEMS PATH PLANNING

This paper shows a comparison of different methods based on genetic algorithms in order to find a computational cost efficient path planning strategy for unmanned aerial systems (UAS). For that purpose, two different population generations and three crossover operators are proposed, comparing the computational time they require and the paths found. Results prove that it is possible to design a reliable and fast evolutive algorithm, capable of finding a sub-optimal solution without too high computational cost for a complex problem such as minimizing the path between two points for rotorcrafts.