Ant colony optimization and particle swarm optimization for robot-path planning in obstacle environment

Abstract

For searching the best path for a robot in an obstacle environment, this paper proposes an algorithm of ant colony optimization(ACO) and particle swarm optimization(PSO) for path planning. The new algorithm effectively combines the advantages of ACO and PSO. It adopts the grid method for environment modeling and makes use of the efficiency and succinctness of PSO to obtain the initial distribution of pheromone, reducing the number of iterations and accelerating the convergence. At the same time, by using the parallelizability of ants and distributed parallelized-searching technology, the performance of the algorithm is effectively improved. The simulation result shows the effectiveness of the proposed algorithm in solving the problem of path planning.