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Peer-reviewedCC BY 4.0PLOS One

Real-time gesture mapping system for a 15-DOF palm-scale bionic manipulator

  • Qunting Chen1
  • Xinhui Chen2
  • Geng Chen
  1. 1College of Information Engineering, Jinhua University of Vocational Technology, Zhejiang, China
  2. 2College of New Energy Equipmnet, Zhejiang College of Security Technology, Zhejiang, China
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Abstract

In this paper, a low-cost, high-precision, high-degree-of-freedom intelligent manipulator was developed. Specifically, a 15-DOF palm-scale bionic manipulator integrated with a vision-based gesture mapping framework is presented. The system consists of a power management module, palm module, joint module, and gesture recognition module, and can be controlled via smart devices. Experimental results demonstrate gesture-driven control with an average end-to-end response time of approximately 2s, including perception, communication, and execution delays. In addition, the system is lightweight and compact, with dimensions comparable to those of a human palm. The work demonstrates the feasibility of integrating established gesture-recognition components with a compact robotic manipulator for embedded and portable human-robot interaction.

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Cite

BibTeXRIS
BibTeX
@article{chen2026realtime,
  title = {{Real-time gesture mapping system for a 15-DOF palm-scale bionic manipulator}},
  author = {Qunting Chen and Xinhui Chen and Geng Chen},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0358347},
  url = {https://doi.org/10.1371/journal.pone.0358347},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Real-time gesture mapping system for a 15-DOF palm-scale bionic manipulator
AU  - Qunting Chen
AU  - Xinhui Chen
AU  - Geng Chen
PY  - 2026
DA  - 2026/09/16
JO  - PLOS One
DO  - 10.1371/journal.pone.0358347
UR  - https://doi.org/10.1371/journal.pone.0358347
AB  - In this paper, a low-cost, high-precision, high-degree-of-freedom intelligent manipulator was developed. Specifically, a 15-DOF palm-scale bionic manipulator integrated with a vision-based gesture mapping framework is presented. The system consists of a power management module, palm module, joint module, and gesture recognition module, and can be controlled via smart devices. Experimental results demonstrate gesture-driven control with an average end-to-end response time of approximately 2s, including perception, communication, and execution delays. In addition, the system is lightweight and compact, with dimensions comparable to those of a human palm. The work demonstrates the feasibility of integrating established gesture-recognition components with a compact robotic manipulator for embedded and portable human-robot interaction.
SN  - 1932-6203
ER  -