Flying Robots Monitor Infrastructure

Wednesday, September 24th, 2014
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Researchers from the Tufts School of Engineering are developing sophisticated new methods for monitoring the condition of bridges and other forms of infrastructure in order to improve the safety and convenience of the assessment process.

Babak Moaveni, an assistant professor of civil and environmental engineering at the Tufts School of Engineering, and Usman Khan, Tufts assistant professor of Electrical and Computer Engineering, are leading the development of the monitoring system, which makes use of flying autonomous robots in tandem with smart sensors to assess the condition of bridges and other forms of infrastructure with far enhanced accuracy and ease.

The system first involves attaching wireless smart sensors permanently to the beams and joints of bridges in order to achieve continuous monitoring of their vibrational levels, which can serve as a telling sign of even minor damage.

Autonomous flying robots in the form of quad-copters are then sent to collect this data by hovering in close proximity to the smart sensors and communicating with them via wireless transmission. This dispenses with the highly hazardous process of sending human workers to examine the bridge in person by ascending elevated viewing platforms or dangling from the bridge using cables.

In addition to greater safety, Moaveni’s method could provide a far more accurate means of assessing the condition of bridges than prevailing techniques, since the analysis of vibrational data is capable of uncovering damage which cannot be discerned by means of visual observation alone.

In order to test the method, Moaveni installed a set of 10 wired sensors to a 44-metre long bridge situated at Tuft’s Medford/Somerville campus, and loaded it with 2.26 tons of concrete to simulate the impact of damage.

When pedestrians walked across the span of the bridge both before and after the addition of the concrete, the sensors were capable of identifying the simulated damage based on vibrational differences, proving the effectiveness of the monitoring method.

Moaveni and his team now hope to automise the information analysis process as well by developing computer algorithms which are capable of determining if a bridge has suffered even minor forms of damage by parsing the vibrational data.

“Right now, if a bridge has severe damage, we’re pretty confident we can detect that accurately,” said Moaveni. “The challenge is building the system so it picks up small, less obvious anomalies.”

Khan has already received a $400,000 Early Career Award from the National Science Foundation to further develop the technology, focusing in particular on the navigational and communication challenges related to its deployment as an effective monitoring tool.

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