A Study on the Implementation of Crawling Robot using Q-Learning

• 영문명: A Study on the Implementation of Crawling Robot using Q-Learning
• 발행기관: 한국인공지능학회
• 저자명: 김현기(Hyunki KIM) Myung-Ae CHUNG 김경아(Kyung-A KIM) 강민수(Min-Soo KANG)
• 간행물 정보: 『인공지능연구』Vol.11 No. 4, 15~20쪽, 전체 6쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 초록

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically , reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches ,and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

목차

1. Introduction

2. Related Research

3. Implementation

4. Experimental Result

5. Conclusions

References

키워드

Q-Learning Machine Learning Reinforcement learning Markov-Modeling