Transformer-based Pavement Crack Tracking with Neural-PID Controller on Vision-guided Robot

Transformer-based Pavement Crack Tracking with Neural-PID Controller on Vision-guided Robot Jianqi Zhang, Xu Yang, Wei Wang, Ioannis Brilakis, Hainian Wang, Ling Ding Pages 699-706 (2024 Proceedings of the 41st ISARC, Lille, France, ISBN 978-0-6458322-1-1, ISSN 2413-5844) Abstract: Pavement crack tracking in unstructured road environments stands as a crucial and ongoing challenge, playing a pivotal role in ensuring precise crack sealing for automated pavement repair. However, slender cracks often present issues with insufficient feature extraction and reduced tracking efficiency. This article proposes a dynamic visual tracking method for pavement cracks utilizing a hybrid adaptive control scheme combined with a self-tuning neural network and proportional-integral-derivative (PID). Specifically, the system capitalizes on a transformer-based crack segmentation system to extract crack features from the road image plane and determine an optimized control input to direct the robot. Segmentation module cross-integrates coarse-grained and fine-grained features using the self-attention (SA) and cross-attention (CA) for fusion operations within the fusion transformer module. Crack tracking is additionally equipped with a Neural–PID controller for adaptable control parameter adjustment. The effectiveness of this proposed method, tested thoroughly on a physical robot platform, illustrates significantly positive results in achieving real-time tracking of pavement cracks. Keywords: Crack Tracking, Crack Segmentation, Transformer, Neural–PID Control, Mobile Robot DOI: https://doi.org/10.22260/ISARC2024/0091 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley