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JRM Vol.33 No.5 pp. 1145-1154
doi: 10.20965/jrm.2021.p1145
(2021)

Paper:

Development of Automatic Chair Transport System – Chair Recognition and Approach Strategy –

Koshiro Miyauchi and Nobuaki Nakazawa

Department of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University
29-1 Hon-cho, Ota, Gunma 373-0057, Japan

Received:
February 28, 2021
Accepted:
June 2, 2021
Published:
October 20, 2021
Keywords:
chair, recognition, automatic, mobile robot, image processing
Abstract
Development of Automatic Chair Transport System – Chair Recognition and Approach Strategy –

Robot configuration and chair detection

In schools and other educational institutions, there are many instances that require the arrangement of chairs depending on the required purpose, such as a class or event. Some chairs are on casters; however, educational institutions typically use stacking or folding chairs. Although effective in terms of storage, these must be lifted by hand during transportation, increasing the burden on the workforce. While automation of baggage transport in warehouses has improved significantly, little attention has been paid to the automation of chair transport. Despite the demand and the fact that self-propelled chairs have already been developed, automatic transport of chairs without casters has never been reported. In this study, we constructed an automatic chair-transport system using an omnidirectional mobile robot and focused on a stacking chair that allowed the robot to position itself underneath. The developed system utilizes the image of the seat and frame pipe of the stacking chair to estimate the chair’s position and direction with respect to the robot. Once the robot has positioned itself under the chair, the chair is lifted and transported using a lifter device attached to the robot.

Cite this article as:
Koshiro Miyauchi and Nobuaki Nakazawa, “Development of Automatic Chair Transport System – Chair Recognition and Approach Strategy –,” J. Robot. Mechatron., Vol.33, No.5, pp. 1145-1154, 2021.
Data files:
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