Pipeline crack defect inspection robot design

Detection of actual pipeline cracks is an important task of road administration. Aiming at the practical difficulties of the current single means of pipeline defect inspection, a pipeline crack defect intelligent robot inspection system based on Yolov8 visual detection model is designed. The pipeline robots are mainly composed of underlying motion control, high-performance computing, and automatic recharging in terms of hardware. The equipped camera acquires real-time camera image data from the pipeline, sends it to the Yolov8 node through the robot operating system (ROS) topic, and outputs the crack location information after GPU calculation. The on-board LiDAR acquires the surrounding environment information, builds a map of the surrounding environment using the GMAPPING algorithm, and then applies the TEB path planning algorithm to realize the robot's automatic pipeline inspection navigation based on, Simultaneous localization and mapping (SLAM). This study also uses ROSJAVA to design an Android host computer system for controlling, mapping, and navigating the pipeline robot.