TY - JOUR
T1 - Cross-entropy-based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot
AU - Zhang, Jianqi
AU - Yang, Xu
AU - Wang, Wei
AU - Guan, Jinchao
AU - Liu, Wenbo
AU - Wang, Hainian
AU - Ding, Ling
AU - Lee, Vincent C.S.
N1 - Funding Information:
The study presented in the article was partially supported by the National Key Research and Development Program of China (No. 2021YFB2601000), National Natural Science Foundation of China (No. 52078049), Fundamental Research Funds for the Central Universities, CHD (No. 300102210302, No. 300102210118), the 111 Project of Sustainable Transportation for Urban Agglomeration in Western China (No. B20035), and Natural Science Foundation of Shaanxi Province of China (S2022‐JC‐YB‐0169). The authors also appreciate the help of graduate student Han Hong for experimental assistance.
Publisher Copyright:
© 2023 The Authors. Computer-Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor.
PY - 2024/3/15
Y1 - 2024/3/15
N2 - Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross-entropy-based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT-UMbot) for road cracks. Specifically, the CEAFC method uses cross-entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT-UMbot based on a four-wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state-of-the-art performance with high efficiency.
AB - Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross-entropy-based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT-UMbot) for road cracks. Specifically, the CEAFC method uses cross-entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT-UMbot based on a four-wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state-of-the-art performance with high efficiency.
UR - https://www.scopus.com/pages/publications/85173720512
U2 - 10.1111/mice.13108
DO - 10.1111/mice.13108
M3 - Article
AN - SCOPUS:85173720512
SN - 1093-9687
VL - 39
SP - 891
EP - 910
JO - Computer-Aided Civil and Infrastructure Engineering
JF - Computer-Aided Civil and Infrastructure Engineering
IS - 6
ER -