[1]XU Xiaoli,GUO Xudong,ZHENG Wendong,et al.Electrical capacitance tomography sensor for contactless material recognition[J].CAAI Transactions on Intelligent Systems,2025,20(5):1232-1242.[doi:10.11992/tis.202408021]
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Electrical capacitance tomography sensor for contactless material recognition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 5 Page number: 1232-1242 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2025-09-05
Title:
Electrical capacitance tomography sensor for contactless material recognition
Author(s):
XU Xiaoli1;  GUO Xudong1;  ZHENG Wendong2;  LIU Huaping3
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200082, China;
2. School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China;
3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
electrical capacitance tomography; planar capacitance sensor; sensor modeling; untouched recognition; material recognition; classification algorithm; LightGBM; Bayesian optimization
CLC:
TP391
DOI:
10.11992/tis.202408021
Abstract:
Electrical capacitance tomography (ECT), known for its rapid and nonintrusive characteristics, effectively avoids the optical interference problem commonly encountered in material property identification based on optical imaging technologies. However, conventional ECT sensor research has predominantly focused on solving the inverse problem, with limited emphasis on achieving noncontact, nonintrusive material identification through permittivity distribution analysis. To address this gap, this study introduces a planar ECT sensor designed for noncontact material recognition. A material prediction model based on Bayesian-LightGBM is developed, significantly enhancing the predictive performance through Bayesian optimization algorithms. Experimental results demonstrate a high accuracy rate of 95.83% when in contact and 85.28% accuracy within a noncontact range of 20 mm from the sensor. This indicates that robots can precisely acquire material information in the environment in a noncontact and nonintrusive manner, paving the way for new possibilities in the application of robotics technology in complex environments.
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