An improved UAV path planning method based on APSOvnp-APF algorithm

In recent years, unmanned aerial vehicle(UAV) path planning has been a popular research topic. This paper proposes an improved artificial potential field method based on the virtual navigation path generated by the adaptive particle swarm optimization algorithm(APSOvnp-APF). Firstly, an adaptive particle swarm optimization algorithm(APSO) which adaptively adjusts the inertia weight and learning factor is designed, and the fitness function is constructed by considering the flight characteristics of UAV.Then, a global virtual navigation path(VNP) is initially obtained by the APSO algorithm to improve the artificial potential field method(APF). The VNP provides APF with new guiding meaning and updates the gravitational rule. Furthermore, the repulsive potential field is redefined. The APSOvnp-APF algorithm solves the problems of local minimum and target unreachable in traditional APF, and achieves a more comprehensive path selection. The simulation results show that the algorithm is reliable and effective, which can formulate a smooth global optimal path for UAV.