Research on autonomous moving robot path planning based on improved particle swarm optimization

Abstract

Two improved particle swarm optimization algorithms are given to overcome the defects in the commonly used particle swarm optimization. These are particle swarm optimization with nonlinear inertia weight and simulated annealing particle swarm optimization. The global search ability and local search accuracy can be optimized by introducing nonlinear inertia weight coefficients. It is well known that the particle swarm optimization has a problem that the algorithm is easily trapped into the local optimum. This paper shows that such a problem can be solved partially by combining the particle swarm optimization with simulated annealing algorithm. Autonomous moving robot path planning is given based on improved particle swarm optimization. The simulation results show the validity of the proposed improved algorithm in moving robot path planning.