Pervasive autonomous sensing of buried pipes

Lead Research Organisation: University of Sheffield
Department Name: Mechanical Engineering


In Europe, the total value of the sewer assets amounts to Euros 2 trillion. The US EPA estimates that water collection systems in the USA have a total replacement value between USD 1 and USD 2 trillion. Similar figures can be assigned to other types of buried pipe assets that supply clean water and gas. In China alone 40,000 km of new sewer pipes are laid every year. However, little is known about the conditions of these pipes despite the pressure on water and gas supply utility companies to ensure that they operate continuously, safely, and efficiently. Existing inspection solutions are slow, relatively expensive and all require human intervention.
The academic excellence in this PhD project is that it will pave the way to the development of a pervasive autonomous sensing technology for buried pipe inspection which does not currently exist, but badly needed. The novelty of this project will be to study the performance of a dynamically reconfigurable swarm of acoustic, radio wave or IR sensors for the inspection of sewer and clean water pipes. This has not been done before. It is expected that these sensors will be deployed on autonomous robots when the robotic technology is ready in the next 10-20 years. Key academic challenges here is to demonstrate that this technology is feasible, accurate and robust to operate across scales in a range of fully surcharged and partially filled buried pipes. This will contribute to the complete elimination of human intervention required for the inspection and rehabilitation of buried pipes.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509735/1 01/10/2016 30/09/2021
1943491 Studentship EP/N509735/1 01/10/2017 30/03/2021 Richard Michael Molyneux
Description My PhD has focused on analysing potential algorithms which dictate the routes that swarms of inspection robots follow in underground water systems. The objective is to determine the algorithm which can provide the best, even coverage of a network with the realistic conditions of dynamic water flows and limited communication ranges and computing power, so as to reduce the number of robots required to inspect a network to a sufficient standard.

So far I have adapted four different algorithms of varying degrees of autonomy - stigmergy, AdHoc Net, an m-CPP solution and an Out-of-Sync Cyclic behaviour. The findings thus far have shown that simpler behaviours tend to be unbiased, providing even coverage, whereas the more intelligent behaviours inspect more frequently but have a higher standard deviation between central and outlying pipes. All of the behaviours, however, prove that autonomous sensing is feasible and increases the frequency of inspection by several orders of magnitude to current methods. Simulations are currently being run analysing the changes with different running speeds, communication ranges and power capabilities whilst the behaviours are being further adapted to consider flow changes better.
Exploitation Route The research's main goal was to prove feasibility of autonomous inspection, which it has done, helping gain further investment via the Pipebots programme. Following the conclusion of the funding, the simulation currently used to output data will be able to take any network and output which algorithms will be most effective for inspection, improving the performance of the robots. This can be taken forward by making advances in the hardware of the robots so as to implement the algorithms in a real world swarm for a network. On the simulation side, it is possible that further measures could be taken to reduce the computational complexity of some algorithms, though this remains a real goal for the outcome of my research currently.
Sectors Communities and Social Services/Policy,Construction,Environment