Derivation and Experimental Validation of Multi-Parameter Performance Optimization of Magnetic Adhesion Unit of Wall-Climbing Robot

Wall-climbing robots have broad application potential in industrial equipment inspection, chemical storage tank maintenance, and high-altitude operations. However, their practical implementation is challenged by the robots’ adhesion requirements in complex wall environments. This study uses a systematic methodology integrating computational simulation and experimental validation to design and optimize a magnetic adsorption system for wall-climbing robots. Firstly, an adjustable suspended magnetic adhesion unit is designed to achieve intelligent control of a wall-climbing robot’s adhesion force on a wall surface. The Maxwell software (AnsysEM21.1) is used to simulate and analyze the critical parameters of the magnetic adsorption unit, including the thickness of the magnet and yoke, as well as the distance and angle between the magnet and the wall surface. Then, a magnetic wheel is designed for the wall-climbing robot based on the optimization of the structure and parameters of the magnetic adhesion unit. The absorption and demagnetization of the magnetic wheels are achieved by rotating the magnetic absorption unit. Subsequently, the simulation results are verified on the experimental platform, and adhesion performance tests are conducted on both standard flat surfaces and inclined walls. The results show that the optimized single magnetic adhesion unit gives the wall-climbing robot an adhesion force of 2767 N under normal working conditions, with a simulation experiment error margin as low as 8.3%. These results both provide theoretical guidance and highlight practical methodologies for developing high-performance magnetic adsorption systems in complex operational environments.

Authors

Institutions

Publication Details

Journal
Actuators
Published
2025-05-29
DOI
https://doi.org/10.3390/act14060270
Citations
3
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
2.02

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Derivation and Experimental Validation of Multi-Parameter Performance Optimization of Magnetic Adhesion Unit of Wall-Climbing Robot

Leijie Lai, Helei Zhu, Peixing Li, Haifeng Ji
3 citations
Actuators
Soft Robotics and Applications
2.02
article

Derivation and Experimental Validation of Multi-Parameter Performance Optimization of Magnetic Adhesion Unit of Wall-Climbing Robot

Leijie Lai, Helei Zhu, Peixing Li, Haifeng Ji
article en
3 citations

Abstract

Wall-climbing robots have broad application potential in industrial equipment inspection, chemical storage tank maintenance, and high-altitude operations. However, their practical implementation is challenged by the robots’ adhesion requirements in complex wall environments. This study uses a systematic methodology integrating computational simulation and experimental validation to design and optimize a magnetic adsorption system for wall-climbing robots. Firstly, an adjustable suspended magnetic adhesion unit is designed to achieve intelligent control of a wall-climbing robot’s adhesion force on a wall surface. The Maxwell software (AnsysEM21.1) is used to simulate and analyze the critical parameters of the magnetic adsorption unit, including the thickness of the magnet and yoke, as well as the distance and angle between the magnet and the wall surface. Then, a magnetic wheel is designed for the wall-climbing robot based on the optimization of the structure and parameters of the magnetic adhesion unit. The absorption and demagnetization of the magnetic wheels are achieved by rotating the magnetic absorption unit. Subsequently, the simulation results are verified on the experimental platform, and adhesion performance tests are conducted on both standard flat surfaces and inclined walls. The results show that the optimized single magnetic adhesion unit gives the wall-climbing robot an adhesion force of 2767 N under normal working conditions, with a simulation experiment error margin as low as 8.3%. These results both provide theoretical guidance and highlight practical methodologies for developing high-performance magnetic adsorption systems in complex operational environments.

ActuatorsVol. 14(6)
Shanghai University of Engineering Science (CN), Jiaxing University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
Soft Robotics and Applications
2.02
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.