Path planning for general aircrafts under complex scenarios using an improved NSGA-II algorithm

General aviation rapidly becomes blossom with the growing economy. However, the complex flying environment and the flexibility for general aircrafts cause more difficulty to ensure the flying safety and efficiency. Hence it is vital to plan path for aircrafts to keep them away from threat and reduce fuel consumption. In this paper, we propose a path planning method based on an improved Nondominated Sorting Genetic Algorithm (NSGA-II) to generate ideal paths for aircraft(s) in a 2-D space to avoid conflict, prohibited flying zones and risk areas. In our method, the adaptive adjustment strategy for crossover and mutation is introduced to accelerate the computation speed and improve the solutions quality. Empirical studies under different scenarios with multiple aircrafts demonstrate that our algorithm can improve solution quality effectively and efficiently.