An Obstacle Avoidance Approach Based on Naive Bayes Classifier

Abstract

Obstacle avoidance plays an important role in mobile robot. However, the traditional methods of obstacle avoidance have difficulty in distinguishing multiple obstacles by edge detection. In this paper, the traditional obstacle avoidance methods are improved to realize the function of multi-obstacle avoidance. Regarding the implementation process, the LiDAR is used instead of the camera, which reduces the difficulty of handling image noise and achieves reliable obstacle detection. It can accurately detect the borders of the nearest obstacle even in complex environments and perform obstacle avoidance. Regarding the obstacle avoidance prediction, the model training is performed through the Naive Bayes classifier based on the three attributes of the velocity of the robot, the left boundary of the obstacle and the right boundary of the obstacle. In the training process, dataset was expanded to enhance the accuracy of classifier model. When the robot goes forward, the improved method enables the robot to move at a higher velocity. The results show the feasibility of advanced obstacle avoidance method by simulation.