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http://dx.doi.org/10.5392/JKCA.2019.19.03.072

Development of Reinforcement Learning-based Obstacle Avoidance toward Autonomous Mobile Robots for an Industrial Environment  

Yang, Jeong-Yean (목원대학교)
Publication Information
The Journal of the Korea Contents Association / v.19, no.3, 2019 , pp. 72-79 More about this Journal
Abstract
Autonomous locomotion has two essential functionalities: mapping builds and updates maps by uncertain position information and measured sensor inputs, and localization is to find the positional information with the inaccurate map and the sensor information. In addition, obstacle detection, avoidance, and path designs are necessarily required for autonomous locomotion by combining the probabilistic methods based on uncertain locations. The sensory inputs, which are measured by a metric-based scanner, have difficulties of distinguishing moving obstacles like humans from static objects like walls in given environments. This paper proposes the low resolution grid map combined with reinforcement learning, which is compared with the conventional recognition method for detecting static and moving objects to generate obstacle avoiding path. Finally, the proposed method is verified with experimental results.
Keywords
Obstacle Avoidance; Grid Map; Reinforcement Learning; Autonomous Locomotion; Path Generation;
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