A fast humanoid robot arm for boxing based on servo motors

Lin, Jianchu; Gu, Zhouzhi; Amir, Ali; Chen, Xiaobin; Ashim, Kahadka; Shi, Kun

doi:10.1109/hpbdis53214.2021.9658471

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…149 Stepper motors, DC motors, and servo motors also have different requirements in terms of electrical signals and driving methods. [150][151][152] Therefore, to simultaneously meet Fig. 8 Schematic illustration of the wide range of robotic applications of MABs.…”

Section: Land Robotsmentioning

confidence: 99%

“…149 Stepper motors, DC motors, and servo motors also have different requirements in terms of electrical signals and driving methods. 150–152 Therefore, to simultaneously meet the requirements of various components, the internal structure of robots includes numerous power management units, converters, and complex power circuits, thereby increasing their volume and mass.…”

Section: Excellent Electrochemical Performance Of Mabsmentioning

confidence: 99%

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Metal–air batteries for powering robots

Zhong,

Wang,

Zuo

et al. 2023

J. Mater. Chem. A

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Section: Land Robotsmentioning

confidence: 99%

Section: Excellent Electrochemical Performance Of Mabsmentioning

confidence: 99%

Metal–air batteries for powering robots

Zhong,

Wang,

Zuo

et al. 2023

J. Mater. Chem. A

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“…However, this results in a new problem of a coach shortage. Many researchers have tried to employ computer vision and robot technology to solve the shortage problem of coaches ( Huang et al, 2019 ; Li et al, 2021 ; Lin et al, 2021 , 2022 ; Mendez et al, 2022 ). The human pose estimation technology can predict boxing elements for better teaching, which can reduce reliance on coaches and increase entertainment in boxing training.…”

Section: Introductionmentioning

confidence: 99%

Model transfer from 2D to 3D study for boxing pose estimation

Lin

Xie

et al. 2023

Front. Neurorobot.

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IntroductionBoxing as a sport is growing on Chinese campuses, resulting in a coaching shortage. The human pose estimation technology can be employed to estimate boxing poses and teach interns to relieve the shortage. Currently, 3D cameras can provide more depth information than 2D cameras. It can potentially improve the estimation. However, the input channels are inconsistent between 2D and 3D images, and there is a lack of detailed analysis about the key point location, which indicates the network design for improving the human pose estimation technology.MethodTherefore, a model transfer with channel patching was implemented to solve the problems of channel inconsistency. The differences between the key points were analyzed. Three popular and highly structured 2D models of OpenPose (OP), stacked Hourglass (HG), and High Resolution (HR) networks were employed. Ways of reusing RGB channels were investigated to fill up the depth channel. Then, their performances were investigated to find out the limitations of each network structure.Results and discussionThe results show that model transfer learning by the mean way of RGB channels patching the lacking channel can improve the average accuracies of pose key points from 1 to 20% than without transfer. 3D accuracies are 0.3 to 0.5% higher than 2D baselines. The stacked structure of the network shows better on hip and knee points than the parallel structure, although the parallel design shows much better on the residue points. As a result, the model transfer can practically fulfill boxing pose estimation from 2D to 3D.

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