Innovation Name:

ARTEMIS- Railroad Crack Detection Robot

Theme:

Innovate for the Society

Stage of Innovation:

We have a working prototype

Member/s:

Shashwat Sahoo, Kavan Savla, Yash Patil, Anoubhav Agarwaal

College:

IIT Madras

City:

Chennai

Challenge:

The Indian Railways holds a vital position in the travel and transport scenario of the country. Covering around 115,000 km across the length and breadth of the country, it is the most important mode of inland transport. The smooth running of the railways is precious to the economy; so much so that we have a separate budget for the railways. Maintenance is one of the major hindrances that we believe plagues the colossal system. Maintenance that is manual, irregular and prone to accidents. Our focus is on the maintenance of the railway tracks. Owing to the sheer magnitude of the length of railway tracks, it becomes highly difficult and unreasonably costly to manually go and check for cracks in railway tracks; which if left undetected, may lead to derailments. And these derailments are a serious challenge for railways and can cause deaths. The year 2016-17 saw a total of 80 train mishaps, out of which an astounding 62 were derailments. Deaths related to derailment have been on the rise and reached 193 the same year. It really pains us to see fellow humans die in magnitudes comparable to that of a major deluge. This is what drove us to develop a system that automatically and regularly checks for cracks along all the tracks, analyses them and alerts us so that we can do repair work in time. This calls for cheap and fully automated system that does this work (without any human or parallax error), and this is exactly what we intend to do. It's not as if nothing is being done to improve this situation. First, the Indian Railways employs approximately 200,000 employees called gang men who go along tracks and manually check for cracks. This undertaking costs the Indian government a mammoth INR 43,200,000,000. This process is grossly inadequate, cash guzzling and understandably, has high human error. As reported by the Times of India coverage of our invention on January 29, 2018, an average of 400 people lose their lives in this line of job every year. Second, these inspections can only be carried out when trains are not running on the tracks. Given the vastness of the railway system, it is only logical to incorporate a system that does not disturb the present network and causes no delays in the train schedules. This is where the under-the-track model comes forth as the most innovative solution.

Solution:

We hold a provisional patent for our innovation, proposing a unique mechanism for travel and detection of cracks. The proposed robot, which is called Artemis, will travel along a track and use ultrasound technology to detect cracks. It will then go on to analyze cracks big enough to cause derailments, based on crack profile and help in the following.

1. Transmit the data to the central server (or a mobile SMS).

2. Save the data where mobile connectivity is not present. When the robot reaches the earmarked station, we will retrieve the data and will come to know exactly where the cracks are located, and the repair work can be initiated at the earliest. A GSM 800 module is used in combination with the microcontroller to transmit the location data from the Neo 6M GPS module. This robot will be much more efficient than a human being, since it can work 24 hours a day, moving slowly (50 centimeters per second) but steadily. This is powered by high capacity 12V lithium polymer batteries and the actuators are high torque (stall torque of 22 kg-cm), enabling a powerful six-wheel drive. A H-bridge motor driver is used to control the direction of motion of the robot. The proposed modus operandi is that there will be multiple robots, organized across the network, and each robot will move on the entire stretch of the track earmarked for it. The most striking feature of the robot is that it has been designed to integrate it with the current system. The robot will move on the insides of the track (with the axis of the wheels perpendicular to the ground) and thus, allowing for the trains to move over it simultaneously. One major problem in this type of a design was that the insides of a track are not smooth and are riddled with nut-bolts and “fish-plates”. This problem has been innovatively solved by attaching a calculated and custom-made suspension system on each of the six wheels. The modular nature of the custom manufactured parts will ensure that future additions will be done easily and any damage to the robot can be repaired easily and sans any exorbitant cost of maintenance.

Impact:

We believe that Artemis will have a marked social impact and will hold a very strong commercial potential. This will be a cheap alternative to any current methods since it is fully automatic. If mass produced, its cost will go down substantially and multiple variants and models can be launched for different railway tracks. Models that can store crack location, send crack alert on mobile phones, detect loose fish plates, surveillance to look out for anomalies on tracks or any animals sabotage and more. We intend to develop an interactive interface for people with login credentials to check all data about any cracks on any specific railway line or location. The system ensures instant replay of crack locations to the central database so that there is no lack of coordination between repair work and train schedules (which has led to derailments in the past). Also, the working principle behind the robot can be applied to other fields as well; for instance, checking cracks in oil pipelines. With the government’s recent push toward more safety checks for the railways, our product could just be the thing needed. With the Indian government looking for private players in the railways sector (as stated by the Economic Times in an article on February 2, 2018) our model can very much realize its full commercial potential with the US$25,000 billion worth Indian Railways as a major customer. Currently, about 2 lakh gang men are employed by the railways. Our robotic system can drastically reduce this number, causing huge operating cost cuts there. Because of our modular design the maintenance of the robots can be done by semi-skilled labourers. The gang men who will be replaced by the robot can do this maintenance after minimal training. The system being completely autonomous will reduce the errors and response time of crack detection when compared to manual inspection. The robot’s efficiency compared to manual is 20:1 in terms of cost and 7:1 in terms of speed. By leveraging our system, Indian Railways can be made a safer mode f transportation for people and goods. Then the current rampant loss of lives can be reduced. The system can be considered as a step toward modernization of the ageing infrastructure of the Indian Railways.

We intend to develop an interactive interface for people with login credentials to check all data about any cracks on any specific railway line or location. The system ensures instant replay of crack locations to the central database so that there is no lack of coordination between repair work and train schedules (which has led to derailments in the past).

Also, the working principle behind the robot can be applied to other fields as well; for instance, checking cracks in oil pipelines. With the government’s recent push toward more safety checks for the railways, our product could just be the thing needed. With the Indian government looking for private players in the railways sector (as stated by the Economic Times in an article on February 2, 2018) our model can very much realize its full commercial potential with the US$25,000 billion worth Indian Railways as a major customer.

Currently, about 2 lakh gang men are employed by the railways. Our robotic system can drastically reduce this number, causing huge operating cost cuts there. Because of our modular design the maintenance of the robots can be done by semi-skilled laborers. The gang men who will be replaced by the robot can do this maintenance after minimal training.