Although railroad and train technology has been around for hundreds of years, according to a report from the U.S. Bureau of Labor Statistics, railroads haul the most freight of any form of transport in terms of ton-miles, a measure of cargo volume that considers weight and distance carried. Moreover, the railway continues to be the most efficient, quickest, and cheapest method of transporting people and goods in the world. With 2.1 trillion tonne-kilometers of rail freight transported in 2020 in the U.S alone, the demand for safe and well-maintained railway lines is clear.

Currently though, inspection and repair tasks on a railway are dangerous and difficult jobs for humans. They include unsociable hours, long distances, exposure to the elements, and a variety of hazards. To maintain an appropriate degree of safety, it is important to reduce the number of people out on the track, while improving the observation and measurement of the railway infrastructure. As well, new systems must work cooperatively with people.

Warthog UGV Meets Trains

In2Smart 2 is a collaborative project using a Clearpath Warthog UGV between Cranfield University and Network Rail to develop an autonomous integrated inspection and repair system for railways. Cranfield University is a postgraduate institute focused on education, research, and development for industry in Bedford England, whereas Network Rail is the main line railway infrastructure for Great Britain. Together, they developed a robotic project that would demonstrate the command and control of autonomous inspection and repair. Starting from a job control system, an instruction would be fulfilled with a physical system that seeks a fault and then a repair action would be made. Further, the range, navigation, and communication challenges would also be demonstrated at scale.

However, such an autonomous system for railways isn’t as simple as it might seem. The environment of the railway infrastructure is surprisingly tough for autonomous devices. For example, there are some places with no communications, no GPS (e.g. tunnels and Faraday cages), few opportunities for charging, and uneven surfaces with obstacles. The scale of the challenge also requires mm accuracy within a 1000km network, to, for example, repeat the measurement of a crack. In2Smart 2 was intended to bridge those gaps.

To get their project up and running quickly, particularly for accelerated hardware integration, the In2Smart 2 team opted to utilize the Warthog UGV robotic platform. Warthog UGV allowed the team to test sensor systems, navigation concepts, and obstacle detection and avoidance within ready-to-run hardware and software. The robust running gear was particularly suitable for running across railway infrastructure, and additionally, the team built a trolley to mount Warthog UGV on the railway to increase its range while carrying more instruments. One of the core challenges though was the integration of the sub-systems. Oftentimes, there are unexpected outcomes in hardware, electronics, and software (e.g. Arduinos don’t all talk). Therefore, this range of skill sets necessitated the right equipment and a diverse team.

“The Warthog worked perfectly and the GPS was good for 0.2m including RTK.”

Professor Andrew Starr, Head of Life-cycle Engineering & Management

How to Keep Railways Safe

Warthog UGV’s main role for this autonomous rail inspection and repair system is to carry the sensors and tools needed for fixing the railway track. The mission for each investigation job involves a launch on-site before seeking faults. Physical movement is required to observe and inspect the infrastructure. In this stage, an accurate repeatable location must be achieved. Measurement of the fault, followed by monitoring of degradation (potentially many months on the real railway), needs repeat visits. The tool deployment then fixes the fault (returns data, in the first instance), before system recovery, and closure of the job.

To achieve its outlined tasks, the Warthog UGV rig is set up with a sensor pack of RTK-GPS module from NovAtel smart6-I, Lord 3DM-GX5-25, odometry, Velodyne VPL-16 Lidar, and an AXIS M5525-E PTZ Network Camera augmented by a robot wrist camera and stereo vision. As well, the system is equipped with ultrasonic flaw detection. Data fusion of the navigation and location sensors is necessary to improve location accuracy and to bridge GPS limitations – significant challenges for the typical railway application environments. The robot wrist camera, on the other hand, is used to get additional views of the rails and track components. Next, stereo vision is used to get a closer range for obstacle detection. Finally, ultrasonics allow for rail flaw detection.

Ultimately, Warthog UGV offered the team a mobile platform that could easily be fitted with the sensing systems and accessories that they required. They found the robot to be robust, ready-to-run and were able to leverage its sensor suite. Ultimately, Warthog UGV is exactly what they needed. “The Warthog worked perfectly and the GPS was good for 0.2m including RTK.” said Professor Andrew Starr, Head of Centre for Life-cycle Engineering & Management.

Future Plans

The In2Smart 2 team was able to successfully demonstrate autonomous flaw detection with a “rail robot” that has shown repeatable measurement in controlled conditions. With this project completed, the team’s next steps will be to develop measurement sequences on a railway test track with improved location measurement. Their goal with such advancement is to ultimately understand how autonomous vehicles can be permitted to operate on a working railway. Further, the project is already part of a major European programme, Shift2Rail, which includes almost all the European railway infrastructure operators, train operating companies, specialist contractors, and manufacturers. In this work package, the In2Sports 2 team partnered with Trafikverket and Strukton. Finally, the team’s work was published at the 2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE) which you can read here.

The In2Smart 2 project team were composed of members from both Cranfield University and Network Rail. The Cranfield University was comprised of Professor Andrew Starr (Head of Centre for Life-cycle Engineering & Management), Dr. Isidro Durazo Cardenas (Lecturer in Through-life Engineering Services), Dr. Haochen Liu (Research Fellow), Masoumeh Rahimi (PhD student), Miftahur Rahman (PhD student), and Wael Nofal (PhD student). The Network Rail team was comprised of Amanda Hall (Engineering Expert – Systems), Robert Anderson (Principal Engineer & Head of RCM – Fixed Assets), David Burbridge (Senior Engineer – Technical Services), and Sreenivaasa (Sri) Pamidi (Project Manager Shift2Rail – R&D)

To learn more about Cranfield University, you can visit their website here.

To learn more about Network Rail, you can visit their website here.

To learn more about Warthog UGV, visit our website here.

 

Clearpath Robotics Launches Outdoor Autonomy Software & Partner Program   |   Learn More
Hello. Add your message here.