Background: With the continuous expansion of metro system scale and the increase of service years, inspection and maintenance work to ensure safe and reliable metro operation has become increasingly critical, demanding more sophisticated approaches to address the growing complexity of infrastructure management. Against the backdrop of smart urban rail construction, research on intelligent robotic systems holds important theoretical and practical significance for promoting the transformation of maintenance models and ensuring safe train operation. Purpose: To address the escalating operational and maintenance pressures resulting from the expansion of urban rail transit systems, this study establishes three progressive research goals: (1) to construct a technical classification framework for intelligent inspection robots based on locomotion modes (rail-mounted, wheeled, tracked, and quadruped), clarifying the functional boundaries and applicable conditions of each category within typical operational scenarios; (2) to quantitatively evaluate the actual effectiveness of intelligent inspection robots in terms of inspection efficiency, data standardization levels, and operational safety, while systematically identifying critical bottlenecks including environmental adaptability, intelligent decision-making capabilities, collaborative operation mechanisms, and cost control; and (3) to propose a development pathway for an integrated intelligent system covering the complete "inspection- diagnosis-maintenance" workflow, thereby providing actionable decision-making foundations and technical roadmaps for the intelligent transformation of operational and maintenance paradigms. Methods: Based on the aforementioned classification framework, this study synthesizes representative case studies from recent years to systematically summarize the technical characteristics, applicable scenarios, and inherent limitations of each robot category. Results: Empirical evidence demonstrates that various types of intelligent inspection robots can effectively improve inspection quality, enhance data acquisition standardization, and reduce safety risks in diverse operational environments such as subway tunnels, underground passages, and rail vehicle depots. However, several critical challenges persist in current applications, including limited environmental adaptability under complex terrain and extreme weather conditions, insufficient autonomous intelligence for complex decision-making, difficulties in achieving collaborative multi-robot operations, and relatively high costs associated with deployment and maintenance that hinder large-scale promotion. Conclusion: Future research and development efforts should concentrate on breakthroughs in key enabling technologies, including multi-modal locomotion, intelligent perception and decision-making, and human-robot collaboration. The ultimate objective is to construct an integrated intelligent O&M ecosystem that encompasses the entire process chain of "intelligent inspection —fault diagnosis — predictive maintenance," thereby providing robust technological support for the safe, efficient, and sustainable development of modern urban rail transit systems.
Ying et al. (Wed,) studied this question.