Ants Predictive Algorithm for Path Planning of Robot in a Complex Dynamic Environment

Abstract

Based on Ant Colony Optimization(ACO),this paper presents a novel algorithm underlying the robot path planning and dynamic obstacle avoidance in a complex and unfamiliar environment.By mimicking the food hunting behavior of ant colony,this algorithm can search for the global optimal path by adopting the nearest-neighbor search strategy combining an approximating direction function used by multiple ant groups.The virtual ants,based on this algorithm,are(able) to predict their potential collision with the moving obstacles.The subsequent local plans for avoiding such collisions are then scheduled under the ACO.The analytical and computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.