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Modeling and Analysis of Dynamic Electrostatic Adhesion Based on Triboelectric Nanogenerators

  • Ao Qin
  • , Jun Peng
  • , Boshi Zhu
  • , Kanglong Yuan
  • Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Electrostatic adhesion is emerging as a promising mechanism for wall-climbing robots to achieve both adhesion and detachment. Although numerous studies have investigated the gait and structure of these robots, the adhesion mechanism remains controlled by an external voltage source, which increases the weight and wiring burden. Additionally, high adhesion typically requires high voltage. To address these issues, this paper proposes a dynamic electrostatic adhesion model for the first time. The adhesive force is entirely generated by the contact-separation movement between the adhesive footpads and the substrate. We developed a theoretical model to elucidate the generation of electrostatic adhesion. By combining multiple parameters into dimensionless variables, we identified optimal conditions using only two parameters to predict optimal adhesion, thereby guiding the parameter design of dynamic electrostatic adhesion. Furthermore, we found that the electrical energy dissipated by resistance is equal to the negative work done by adhesion in one cycle, offering a theoretical basis for measuring the adhesion force.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications - 17th International Conference, ICIRA 2024, Proceedings
EditorsXuguang Lan, Xuesong Mei, Caigui Jiang, Fei Zhao, Zhiqiang Tian
PublisherSpringer Science and Business Media Deutschland GmbH
Pages418-428
Number of pages11
ISBN (Print)9789819607792
DOIs
StatePublished - 2025
Event17th International Conference on Intelligent Robotics and Applications, ICIRA 2024 - Xi'an, China
Duration: 31 Jul 20242 Aug 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15207 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Intelligent Robotics and Applications, ICIRA 2024
Country/TerritoryChina
CityXi'an
Period31/07/242/08/24

Keywords

  • Electrostatic Adhesion
  • Triboelectric Nanogenerators
  • Wall-climbing Robots

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