Unmanned combat aerial vehicles path planning using a novel probability density model based on Artificial Bee Colony algorithm
Bai Li, Ligang Gong, Chunhui Zhao · 2013
Path planning of unmanned combat aerial vehicle (UCAV) aims to seek an optimal flight route considering threats and constraints along the way towards the terminal target. This paper proposed a novel probability density model to transform the initial path planning task into a numerical problem, which shows higher accuracy in comparison with the traditional circle treat model. The well-known Artificial Bee Colony algorithm (ABC) is used to settle this corresponding optimization problem and comparisons are made between the proposed algorithm and other intelligence algorithms regarding convergence rate and efficiency in various series of combat fields. Experimental results verified with statistical significance the superiority of ABC for the UCAV path planning problem.