基于遗传算法与粒子群算法融合的协同定位算法

o-location technology is an important technology for the autonomous behavior of multiple robots. Indoor robots will receive non-line-of-sight signals in remote locations or locations with poor signals, resulting in low positioning accuracy. This paper proposes a collaborative positioning algorithm (GPSO) that uses genetic algorithm and particle swarm optimization algorithm fusion to locate multiple robots in a wireless sensor network environment. The algorithm introduces the selection, crossover and mutation ideas of genetic algorithm (GA) to filter particles in classic particle swarm optimization algorithm (PSO), and dynamically set inertia weights and learning factors for particles, which enhances the optimization of the algorithm Ability to solve the problem of too fast convergence of the PSO algorithm and improve the positioning accuracy of multi-robots. In this paper, simulation experiments are carried out in line-of-sight and non-line-of-sight environments. The results show that the positioning accuracy of the GPSO algorithm proposed in this paper is better than that of the classic PSO algorithm