DESIGN OF A CAMERA-ENABLED MOBILE ROBOT FOR IN-PIPE INSPECTION

The role of pipeline infrastructure is crucial in modern society, as it transports necessary resources such as water, gas, and oil. It is essential to prioritize the maintenance of these pipelines to prevent leaks, environmental harm, and economic losses. Traditional inspection techniques, which involve manual inspection, can be inefficient, laborious, and susceptible to mistakes. To overcome these obstacles, this article introduces a camera-equipped mobile robot specifically created for automated crack detection in pipelines. The robot, which is equipped with a high-resolution camera and advanced image processing algorithms, navigates inside pipelines on its own to capture detailed images of the pipe walls. These images are then processed using computer vision techniques to identify and categorize potential cracks. The algorithms utilize machine learning models that have been trained on a dataset of labelled crack images, enabling accurate and efficient crack detection. This paper provides a comprehensive overview of the robot system's design, development, and validation, detailing the critical hardware components such as the mobility platform, camera system, and sensor integration, as well as the software architecture, which encompasses image processing and machine learning frameworks. Field studies demonstrate the robot's ability to effectively detect cracks across various pipe materials and under diverse environmental conditions. Results indicate that the automated system not only improves inspection efficiency and accuracy but also significantly reduces the need for human intervention, thereby enhancing overall pipeline safety Keywords: pipeline inspection, crack detection, mobile robot, image processing, computer vision.