OBSTACLE DETECTION AND AVOIDANCE VIA CASCADE CLASSIFIER FOR WHEELED MOBILE ROBOT

A cone obstacle detection method is proposed to detect the cone obstacle. The proposed method uses the Haar features trained by the Adaboost algorithm. After training, the cascaded classifiers are produced and used in a wheeled mobile robot to detect the cone obstacles. To apply the cone obstacle detection, the wheeled mobile robot uses the proposed cone obstacle detection algorithm to simulate children playing the cone maze. The wheeled mobile robot creates a twisty path to move through and around the cones to go to the end point. Experimental results show that the proposed method is effective to detect the cone obstacles. Furthermore, the wheeled mobile robot can create a twisty path to move through and around the cones to go to the end point.