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Team members

Lim Jon Hui Ryan (EPD), Neo Guang Zhi Malcom (EPD), Nikos Chan Wai (ISTD), Sim Seng Wee Christopher (EPD), Antonio Miguel Canlas Quizon (ISTD), Lee Jia Yi (ESD)


Robert Edward Simpson, Jiang Wenchao, Peter Jackson

Writing Instructors:

Grace Kong

Teaching Assistant:

Doreen Steven Mlote


Safety is critical in the Oil & Gas industry. To mitigate the risk of tank leakage and/or explosion caused by corrosion, the American Petroleum Institute (API) stipulates the regular inspection of tanks. This has created a need for certified inspection services.

Our project aims to address problems faced during the hazardous, labour-intensive, and time-consuming inspection of oil storage tank floors. As such, we developed an automated solution to reduce the duration and costs of conducting floor inspections.



About the Product

FLOOR-E is an autonomous ground-based inspection robot that detects, quantifies, and maps corrosion damage in storage tank floors. Its fully-automated capabilities eliminate the need for trained inspectors to manually operate the device and enable it to complete floor inspections in just 13.6% of the time taken for a manual inspection to complete. This suggests potential cost savings of $20,160 for the inspection of a 30m large tank.



FLOOR-E’s journey within the tank begins with map generation i.e. the creation of a floor plan that marks out obstacles across the tank floor. With a Light Detection and Ranging (LiDAR) sensor onboard, it roams around autonomously to create a map of the entire floor within a few minutes.

Next, an optimised algorithm is used to generate an efficient route for FLOOR-E to traverse across the tank floor and perform a complete scan of the floor area in the shortest time possible. FLOOR-E is equipped with both magnetic flux leakage (MFL) and ultrasonic testing (UT) sensors onboard, to enable large surface or precise scanning, respectively. Therefore, FLOOR-E allows for fully autonomous inspection of the oil storage tanks.

Upon completion of the inspection, a report generation feature highlights key areas of concern where corrosion had been detected during the inspection. 




Gibraltar Storage Tank Explosion <Perera, J. (2011, May 31). Gibraltar Explosion [Photograph]. Flickr. >



Future Works

Moving forward from the Capstone Project, the full-scale prototype should be fully integrated and functional, with additional features such as a drone mount to enable integrated usage with PR’s drone products. This would allow the robot to perform wall and floor inspections simultaneously when scanning along the tank circumference.

A further extension of the project would be to incorporate corrosion detection abilities based on computer vision. With a camera and floodlights fitted onto the robot, photos can be collected and labelled during the inspection process to provide training data for a machine learning model. In the future, corrosion detection via computer vision should facilitate the accurate and efficient inspection of large areas, as a first-line supplement for MFL and UT scans.




Project Stakeholders

Performance Rotors (PR) remains the primary stakeholder in the project. As an inspection company based in Singapore, they deploy unmanned aerial vehicles (UAVs) to conduct tank inspections in confined spaces or areas which require working at height. Apart from PR, there are three other stakeholders: Oil & Gas corporations, storage tank owners and certified inspection companies. 

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FLOOR-E Render Image (Front) 


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Product User Interface (UI) 



Project Benefits

Cost Reduction

FLOOR-E promises significant cost savings by drastically reducing completion times and the need for manual labour. 

Environmental Protection

Our solution improves upon the inspection process and therefore reduces the risk of environmental destruction from oil leakages or tank explosions. 


FLOOR-E enables inspections to proceed without the need for humans to be inside the storage tank, which greatly reduces safety hazards such as inhalation of toxic vapours, slipping and other risks within the tank environment. 





Detection of Pitting Corrosion via Computer Vision <Koz5000. (2016, February 26). A high-resolution camera image of an internal corrosion pit in a pipe wall [Photograph]. Wikipedia. >



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student Lim Jon Hui Ryan Engineering Product Development
student Neo Guang Zhi Malcom Engineering Product Development
student Nikos Chan Wai Information Systems Technology and Design
student Sim Seng Wee Christopher Engineering Product Development
student Antonio Miguel Canlas Quizon Information Systems Technology and Design
student Lee Jia Yi Engineering Systems and Design
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