Decision Making Based on Physical and Neural Network Models for Precision Ball-Batting Robots

T. Hsiao, S. C. Wu

研究成果: Conference contribution同行評審

摘要

Ball-batting tasks requires tight integration of high-speed visual tracking of the flying ball, immediate decision making on when and how to hit the ball, and precise and agile motion control. The so-called 'precision ball-batting' in this paper means not only to hit the ball, but also to send the rebounding ball to a specified target location. It is challenging for robots, but professional human players can do it very well. Therefore, precision ball-batting can serve as a testbed for modern eye-hand coordinate techniques of robots. This paper extends the authors' previous work on the precision ball-batting robot by upgrading the vision system and proposing a novel decision making procedure that combines the advantages of the physical rebounding model and the neural network model. Experimental results show that the successful rate of precision ball-batting increases from 13.75% in the previous work to more than 60% in this paper, while the rate of swing and miss decreases from 11.25% in the previous work to 0% in this paper.

原文English
主出版物標題2021 American Control Conference, ACC 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面3787-3792
頁數6
ISBN(電子)9781665441971
DOIs
出版狀態Published - 25 5月 2021
事件2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
持續時間: 25 5月 202128 5月 2021

出版系列

名字Proceedings of the American Control Conference
2021-May
ISSN(列印)0743-1619

Conference

Conference2021 American Control Conference, ACC 2021
國家/地區United States
城市Virtual, New Orleans
期間25/05/2128/05/21

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