Mechanism Design and Motion Planning of a Hexapod Curling Robot Exhibited During the Beijing 2022 Winter Olympics Games

When a curling rock slides on an ice sheet with an initial rotation, a lateral movement occurs, which is known as the curling phenomenon. The force of friction between the curling rock and the ice sheet changes continually with changes in the environment; thus, the sport of curling requires great sk...

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Bibliographic Details
Published in:Engineering
Main Authors: Ke Yin, Yue Gao, Feng Gao, Xianbao Chen, Yue Zhao, Yuguang Xiao, Qiao Sun, Jing Sun
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2024
Subjects:
Online Access:https://doi.org/10.1016/j.eng.2023.10.018
https://doaj.org/article/678adfe574a041e7875470f3c6c90ba8
Description
Summary:When a curling rock slides on an ice sheet with an initial rotation, a lateral movement occurs, which is known as the curling phenomenon. The force of friction between the curling rock and the ice sheet changes continually with changes in the environment; thus, the sport of curling requires great skill and experience. The throwing of the curling rock is a great challenge in robot design and control, and existing curling robots usually adopt a combination scheme of a wheel chassis and gripper that differs significantly from human throwing movements. A hexapod curling robot that imitates human kicking, sliding, pushing, and curling rock rotating was designed and manufactured by our group, and completed a perfect show during the Beijing 2022 Winter Olympics Games. Smooth switching between the walking and throwing tasks is realized by the robot’s morphology transformation based on leg configuration switching. The robot’s controlling parameters, which include the kicking velocity vk, pushing velocity vp, orientation angle θc, and rotation velocity ω, are determined by aiming and sliding models according to the estimated equivalent friction coefficient μequ and ratio e of the front and back frictions. The stable errors between the target and actual stopping points converge to 0.2 and 1.105 m in the simulations and experiments, respectively, and the error shown in the experiments is close to that of a well-trained wheelchair curling athlete. This robot holds promise for helping ice-makers rectify ice sheet friction or assisting in athlete training.