Robotic Simulation Systems and Intelligent Offline Teaching for Urban Rail Transit Maintenance

摘要

Intelligent operation and maintenance of urban rail transit systems is essential for improving train safety and efficiency. This study focuses on reducing time, physical effort, and safety risks in deploying intelligent metro inspection robots. This study introduces a design approach for an undercarriage robot simulation system and an offline teaching method. Gazebo and Isaac Sim are combined in this study. Gazebo is used for lightweight simulation in model development and algorithm testing. Isaac Sim is used for high-fidelity rendering and robust simulation in complex large-scale scenarios. This combined approach addresses critical aspects of system development. The research proposes environment data collection and processing methods for metro inspection scenarios. It also provides solutions for hole problems in point cloud mesh models and approaches for robot modeling and sensor configuration. Additionally, it involves developing a target vector labeling platform. Using these elements, an offline teaching system for undercarriage inspection robots has been designed with simulation tools. Offline teaching is unrestricted by on-site space and time. It reduces physical demands and boosts robot teaching efficiency. Experimental results indicate that it takes about 30 s to program a single manipulator motion offline. In contrast, manual on-site teaching takes about 5 min. This represents a significant efficiency improvement. While offline teaching results have some errors, high success rates can still be achieved through error correction. Despite challenges in modeling accuracy and sensor data precision, the simulation system and offline teaching approach decrease metro vehicle operation risks and enhance robot deployment efficiency. They offer a novel solution for intelligent rail transit operation and maintenance. Future research will focus on high-quality environmental point cloud data collection and processing, high-precision model development, and enhancing and expanding simulation system functionality.