Design of a Modular Pipe Inspection Robot with Internal Damage Detection Using a Convolutional Neural Network

摘要

In an increasingly demanding industrial environment, proper inspection and maintenance of pipelines has become a priority to ensure safe and efficient operations. The main objective of this project is to develop a highly efficient and reliable inspection robot that is able to adapt to pipelines of different diameters and capable of identifying and locating internal damage. The proposed inspection robot uses a combination of state-of-the-art technologies, such as computer vision and data analysis, to process images in real time and accurately detect cracks. Also, an adaptive navigation system has been designed to allow the robot to move easily inside pipelines, avoid obstacles, and follow an optimal route. In the project, an exhaustive study of the inspection robot is carried out in Solid-Works, performing detailed simulations of the robot, allowing a static and dynamic analysis of its behavior under different load and motion conditions. The static analysis focuses on evaluating the structural resistance of the robot to external loads, ensuring that the materials and connections used are able to Withstand the stresses and forces involved during pipe inspection. This guarantees the durability and safety of the robot in continuous operations and challenging environments. On the other hand, the dynamic analysis focuses on studying the behavior of the robot during its displacement inside the pipelines. Different scenarios and movements are simulated to evaluate the robot’s stability, agility, and responsiveness to obstacles and changes in direction. This allows optimizing the design and programming of the robot, ensuring a smooth and accurate displacement at all times.