Obstacle avoidance with multi-objective optimization by PSO in dynamic environment

Abstract

The second order motion model is one of the fundamental questions, a mostly important object in motion planning research of mobile robots, especially in complex environment. Based on the research of the second order motion model, this paper puts forward a new method for adjusting robots to avoid obstacles in dynamic environment. A mathematical model is first established in which environmental information such as, destination of a mobile robot, velocity and direction of obstacles are considered. Secondly, a new particle swarm optimization (PSO) algorithm is used to search for solution of the multi-objective optimization problem as described in the mathematical model. Finally, by adjusting the velocity and direction of the mobile robot to avoid obstacles in real time, the robot can reach the goal safely. Simulation experiment shows that this method is better than tradition artificial potential field (APF) algorithm and its improved algorithm based on genetic algorithm for obstacle avoidance.