Cross-entropy-based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

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.