Unified path planning for composite UAVs via Fermat point-based grouping particle swarm optimization

Abstract

This paper is concerned with the problem of path planning for a class of composite unmanned aerial vehicles (UAVs). The composite UAV in the paper refers to an aerial vehicle consisting of a carrier UAV and several parasite UAVs to be launched, which combines the advantages of long flight range and high flexibility. A Fermat point-based grouping particle swarm optimization (FP-GPSO) algorithm is proposed to simultaneously determine the air-launch position and optimize the resulting multi-segmented paths. In the proposed approach, the particles are initialized into three groups considering the spatial distribution of the air-launch position, and one of the groups makes full use of the geometric property of the Fermat point. An update strategy of particles is then devised, in which not only the global best particle but the group-best particles are utilized to guide the other particles. By this means, as compared to conventional methods, the developed algorithm is more capable of effectively avoiding the local optimum, as such finding the optimal paths. Simulations are provided to demonstrate the superiority of FP-GPSO in path planning for composite UAVs.