AI Robots to Autonomously Inspect Railway Tracks

Korea Railroad Research Institute Begins Development of Core Technologies for Autonomous Track Inspection Robots
Preventive Maintenance Realized Through Integration of Digital Twin and Physical AI

The era is dawning in which artificial intelligence (AI) and robots will autonomously inspect railway tracks. Technology is now being developed that enables autonomous inspection robots, trained in virtual environments, to detect hazards on actual railway tracks.

On June 22, the Korea Railroad Research Institute announced that it has launched research on “Developing Core Technologies for Railway Track Inspection Using Railway-Specialized Robot Platforms,” which combines digital twin and physical AI technologies.

An example of generating railway environmental data using the Real-World Foundation Model (RWFM). When actual railway track footage is input, the AI generates virtual videos reflecting various weather and environmental conditions such as rain, snow, and fallen leaves. The research team plans to use this to train and enhance the visual recognition performance of autonomous inspection robots in railway environments where actual accident or abnormal situation data are insufficient. Provided by Korea Railroad Research Institute

The core of this research is to use a digital twin that faithfully replicates real railway environments as a training ground for robots. The robots will repeatedly learn about various hazardous situations and environmental changes in the virtual space before being deployed to actual railway sites.

Railway track inspection faces limitations due to worker safety concerns and a lack of data on rare defects. Because there are not many real-life accidents or defect cases, it is difficult to secure sufficient data for AI training, and it is virtually impossible to repeatedly simulate dangerous situations on site.

To address this, the research team plans to construct a three-dimensional virtual space of actual tracks and their surroundings by utilizing drones, cameras, and LiDAR. In this space, robots will experience a variety of scenarios—including track intrusions, obstacles, heavy snow or rain, nighttime or backlighting conditions—enabling them to learn visual recognition and movement control capabilities.

Training in Virtual Space, Inspecting on Real Tracks

The research team plans to continuously enhance performance by applying data learned in the virtual space to actual robots and then feeding data collected in the field back into training.

This study especially incorporates physical AI technology, which has recently attracted attention. Physical AI refers to technology in which AI perceives the real world, makes its own decisions, and even performs physical actions.

The Korea Railroad Research Institute plans to enhance the robots’ visual recognition capabilities using the Real-World Foundation Model (RWFM) and to secure control technologies that enable stable movement in real railway environments through reinforcement learning and Sim2Real (simulation-to-reality) transfer techniques.

If the research is successful, the railway maintenance system is also expected to change significantly. It will be possible to move away from the current post-incident inspection method towards a preventive and autonomous maintenance system that detects risks in advance. This is expected to reduce workers’ track entry, enhance safety, and minimize variations in inspection quality.

This research is being carried out as a National Research Council of Science & Technology (NST) basic project and will be conducted over three years until 2028.

Byun Sungjun, principal researcher at the Korea Railroad Research Institute and project leader, said, “The performance of autonomous inspection robots depends on how accurately they see and how stably they move. Our goal is to secure autonomous inspection technology that can be applied at actual railway sites through the Real-World Foundation Model and reinforcement learning.”

Sagong Myung, President of the Korea Railroad Research Institute, stated, “Railway maintenance must transition to a preventive and autonomous system that detects risks in advance and ensures worker safety. We will do our utmost to develop railway safety technologies that allow the public to use railways with peace of mind by utilizing physical AI and robotics technology.”

This content was produced with the assistance of AI translation services.