A Vision-Based Line-Following Mobile Robot Utilizing Fuzzy Logic Control and Automatic Gamma Correction

This study proposes a vision-based line-following robot solution to overcome the limitations of infrared sensors in industrial environments with variable lighting conditions. The system integrates an automatic inverse gamma correction algorithm with Otsu’s thresholding to optimize contrast, ensuring accurate path extraction under diverse light intensities. Leveraging geometric parameters derived from principal component analysis, a Mamdani-type fuzzy logic controller is designed to coordinate movement, maintaining trajectory stability and mitigating mechanical oscillations. A central contribution of this research is the successful implementation of an intelligent control model on a low-cost hardware platform via an off-board processing architecture. Experimental results demonstrate that the system exhibits flexible responsiveness and reliable tracking, effectively overcoming wireless communication latency challenges to ensure operational performance in intralogistics automation tasks.