Fermat-Weber location particle swarm optimization for cooperative path planning of unmanned aerial vehicles

Abstract

This paper presents an effective algorithm, called the Fermat-Weber location particle swarm optimization (FWL-PSO), developed for cooperative path planning of Unmanned Aerial Vehicles (UAVs). Initially, FWL-PSO is constructed by harnessing the Fermat-Weber optimality to identify potential solutions. Within the framework of FWL-PSO, a collection of high-performing particles is established, determined by their respective fitness scores. Following this, the Fermat-Weber location of these elite particles is calculated to supersede the traditional global best, thereby augmenting the learning strategy of the standard PSO. As a result, this method enables the evolution of information while encouraging search diversity. Subsequently, FWL-PSO is employed for handling the interactions of multiple UAVs. In this context, the path planning for a group of UAVs is formulated as a Nash game that incorporates all cooperative interdependencies and safety conditions. The algorithm is then integrated to solve the optimization problem for achieving the Nash equilibrium. To assess its efficacy, extensive simulations and experiments are conducted across a variety of path-planning scenarios. Comparative analyses between FWL-PSO and existing PSO variants underscore the enhanced efficiency and reliability of our proposed approach.