An Obstacle Avoidance Approach Based on Naive Bayes Classifier

Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v3i1.139

Peiqiao Shang, Wenqian Li

Beijing University of Technology


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.


Robot Obstacle Avoidance; Naive Bayes; LiDAR; Gazebo Simulation


[1] Chang J, Wu C, Li B. A survey of obstacle avoidance methods for mobile robots. Proceedings of the Chinese Eighth National Conference on information acquisition and processing 2010.
[2] He Ming, Sun Jianjun, Cheng Ying. A review of the text classification based on the Naive Bayes[J]. Information Science 2016; 34(07): 147-154.
[3] Li Y, Cai Z. Obstacle avoidance of mobile robot based on Bayes classifier. Control Engineering of China 2004; 20(4): 332-334, 359.
[4] Chen Yi, Zhang Shuai, Wang Guiping. Vehicle detection algorithm based on the information fusion of the LiDAR and the camera[J]. Machinery & Electronics 2020; 38(01): 52-56.
[5] Qizhen H, GuoLong Z. On image edge detection method. International Conference on Electrical and Control Engineering, Yichang 2011; pp: 262-265.
[6] Hong W, Bartels M. Unsupervised segmentation using gabor wavelets and statistical features in lidar data analysis. Amsterdam: North-Holland Pub 2006.
[7] Lim D. Turtlebot3-Melodic. Received from 2018.
[8] Mohini Pandey.Different operator using in edge detection for image processing[J].International Journal of Computer Science Engineering and Information Technology Research 2014; 4(1): 57-61.
[9] YoonSeok P, HanCheol C, RyuWoon J, et al. ROS robot programming. Seoul: Robotis 2017.
[10] Woodall W, Liebhardt M, Stonier D, et al. ROS topics: capabilities [ROS Topics]. IEEE Robotics & Automation Magazine 2014; 21(4): 14-15.

Copyright © 2020 Peiqiao Shang, Wenqian Li

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License