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http://dx.doi.org/10.46670/JSST.2020.29.5.348

Adaptive Obstacle Avoidance Algorithm using Classification of 2D LiDAR Data  

Lee, Nara (Mechatronics Engineering, Kangwon National University)
Kwon, Soonhwan (Mechatronics Engineering, Kangwon National University)
Ryu, Hyejeong (Mechatronics Engineering, Kangwon National University)
Publication Information
Journal of Sensor Science and Technology / v.29, no.5, 2020 , pp. 348-353 More about this Journal
Abstract
This paper presents an adaptive method to avoid obstacles in various environmental settings, using a two-dimensional (2D) LiDAR sensor for mobile robots. While the conventional reaction based smooth nearness diagram (SND) algorithms use a fixed safety distance criterion, the proposed algorithm autonomously changes the safety criterion considering the obstacle density around a robot. The fixed safety criterion for the whole SND obstacle avoidance process can induce inefficient motion controls in terms of the travel distance and action smoothness. We applied a multinomial logistic regression algorithm, softmax regression, to classify 2D LiDAR point clouds into seven obstacle structure classes. The trained model was used to recognize a current obstacle density situation using newly obtained 2D LiDAR data. Through the classification, the robot adaptively modifies the safety distance criterion according to the change in its environment. We experimentally verified that the motion controls generated by the proposed adaptive algorithm were smoother and more efficient compared to those of the conventional SND algorithms.
Keywords
Obstacle avoidance; Reactive navigation; Nearness diagram; Logistic regression; Softmax function; Classification; 2D LiDAR point cloud;
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