Modified central force optimization (MCFO) algorithm for 3D UAV path planning

Abstract

Path planning for the three-dimensional (3D) unmanned aerial vehicles (UAV) is a very important element of the whole UAV autonomous control system. In this paper, a modified central force optimization (MCFO) method is introduced to solve this complicated path-optimization problem for the rotary wing vertical take-off and landing (VTOL) aircraft. In the path planning process, the idea from the particle swarm optimization (PSO) algorithm and the mutation operator of the genetic algorithm (GA) are applied to improve the original CFO method. Furthermore, the convergence analysis of the whole MCFO method is established by the linear difference equation method. Then, in order to verify the effectiveness and practicality of this new path planning method, the path following process is put forward based on the six-degree-of-freedom quadrotor helicopter control system. At last, the comparison simulations among the six algorithms show that the trajectories produced by the whole MCFO method are more superior than the original CFO algorithm, the GA, the Firefly algorithm (FA), the PSO algorithm, the random search (RS) way and the other MCFO algorithm under the same conditions. What is more, the path following process results show that the path planning results are practical for the real dynamic model of the quadrotor helicopter.