Multiple Moving Obstacles Avoidance for Humanoid Service Robot Using Stereo Vision  and Bayesian Approach

Abstract

In this paper, we propose a robust multiple moving obstacles avoidance strategy using stereo vision for Humanoid Robot in indoor environment. We assume that this model of humanoid robot is used as a service robot to deliver a cup to customer from starting point to the destination. We have developed and introduced successfully three main modules to recognize faces, to identify obstacles and to maneuver. A group of people who are walking will be tracked as multiple moving obstacles. Predefined maneuver to avoid obstacles applied to robot because the limitation of view angle from stereo camera to detect obstacles. The contribution of this research is a new method for multiple moving obstacle avoidance strategy with Bayesian approach using stereo vision based on the direction and speed of obstacles. We present the results of the experiment of the humanoid robot called Gatotkoco II which uses our proposed method and we also evaluate its performance. The proposed moving obstacles avoidance strategy was tested empirically and proven effective for humanoid robot.