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

Abstract

Pavement crack tracking in unstructured road environments has been and continues to be a crucial and challenging task, playing a vital role in achieving accurate crack sealing for automated pavement crack repair. However, slender cracks suffer from insufficient feature extraction and low tracking efficiency. In this article, a hybrid adaptive control scheme combined with a self-tuning neural network and proportional–integral–derivative (PID) is proposed for dynamic visual tracking of pavement cracks. Specifically, the scheme extracts crack features on the road image plane based on a S2TNet system and determines an optimal control input to guide the robot. S2TNet cross-integrates the global features through the multi-head attention module. It also adaptively recalibrates the channel responses of partial feature maps for fusion operations with the transformer module. Moreover, the Neural–PID controller is designed for adaptive adjustment of control parameters, and the scheme was validated on a physical robot platform. Extensive experimental results showed that the effectiveness of the proposed method in achieving real-time tracking for pavement cracks.