Soft Target Based Obstacle Avoidance for Car-like Mobile Robot in Dynamic Environment

Abstract

The real time flexible operation of a car-like mobile robot with nonholonomic constraints in dynamic environment is still a very challenging problem. The difficulty lies in the setting of moving sub-target in real-time and appropriately to obtain a collision-free and low cost path. In this paper, we present a new approach to obstacle avoidance for mobile robots in a narrow area with static and dynamic obstacles. It is based on selection of the sub-target points of robot's movement called "soft target" which is a target set defined as all possible and reachable via-points in a navigation space. The soft target is acquired by on-line learning based on the final target and environment information. Each element of it has its membership value between 0 to 1 denoting its evaluation. The algorithm of the presented method is realized by fuzzy predictive control (FPC). The simulation results show the validity and effectiveness of the proposed robot motion control method.