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Title: Modeling and analysis of magnetic adhesion module for wall-climbing robot
Authors: Gao, Xiaoshan
Yan, Liang
Wang, Gang
Chen, I-Ming
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Gao, X., Yan, L., Wang, G. & Chen, I. (2022). Modeling and analysis of magnetic adhesion module for wall-climbing robot. IEEE Transactions On Instrumentation and Measurement, 72, 7500209-.
Journal: IEEE Transactions on Instrumentation and Measurement
Abstract: Magnetic wall-climbing robots have wide applications due to their large adhesive force and loading capacity. However, researches have seldom focused on the magnet patterns and magnetic characteristics of the adhesive module, which may influence the output performance of the robot greatly. Therefore, the purpose of this study is to analyze the magnet array, flux distribution and magnetic adhesive force of wall-climbing robot in both quantitative and qualitative ways. It helps to improve the magnetic force and loading capacity significantly. The concept design of the wall-climbing robot is introduced and the system stability is analyzed. Following that, four magnet patterns are presented and the magnetic flux distribution is numerically simulated. The comparison among them shows that the alternatively magnetization pattern can generate larger magnetic flux density relatively. Subsequently, the magnetic field and adhesive force are both formulated analytically. It is worth noting that the curved surface is considered for the analytical modeling in this study. Thus, the modeling approach can be implemented to the magnetic characteristic analysis of other similar magnetic adhesive robots. Then, the numerical simulation is conducted on the magnetic field and force output to validate the analytical models. One research prototype of the magnetic wall-climbing robot is developed, and the system hardware architecture is introduced. Experiments are carried out to validate the analytical model and output performance of the robot system. The experimental result is consistent with the analytical model and numerical computation.
ISSN: 0018-9456
DOI: 10.1109/TIM.2022.3224522
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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