Scientists are building autonomous repair robots that will use AI to identify and fix potholes in UK roads.
The electric, self-driving bots – which are being built by a spin-out company from the University of Liverpool called Robotiz3d – can find small cracks in the road and cover them with asphalt.
Researchers say the machines, which look like a cross between a tank and a road roller, will transform road maintenance when they hit the roads in 2021, and finally offer a cost effective fix for the UK’s pothole problem.
Currently, no autonomous technology solutions exist to tackle potholes, which are estimated to have cost UK taxpayers more than £1 billion to fix over the last decade.
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Artist’s impression of the autonomous road repair system, which looks part-tank, part road roller. The Robotiz3d vehicle should be seen on UK roads next year
The cost of repairing all of the UK’s roads that are currently damaged, meanwhile, is estimated to exceed £10 billion.
HOW DO POTHOLES FORM?
Road surfaces will deteriorate because of two main factors – traffic and weather.
The greater number (and weight) of vehicles using a road, the faster the road surface wears out.
Over time, this flexibility diminishes and the surface essentially snaps – by cracking and crazing.
This deterioration is exacerbated by both hot and cold weather extremes.
The most damaging impact comes from sub-zero temperatures.
If water can penetrate even the smallest of cracks in a road surface, a pothole will appear as it turns to ice and expands.
Source: Department for Transport
While potholes can be a very costly inconvenience for drivers, they can be fatal for cyclists and motorcyclists.
Poor road surfaces contributed to 517 accidents in 2018 – including eight fatalities and 348 serious injuries – the Department of Transport reported last year.
‘Current methods to detect and repair potholes are labour intensive and as such are slow, unsafe, and costly to the economy and environment,’ said Dr Sebastiano Fichera, technical director of Robotiz3d.
‘The new technology we are developing will make road maintenance tasks faster, cheaper, and cleaner and ultimately make roads safer and more accessible.’
The robot will be built with a continuous track – a system of wheels within with a circle of heavy duty rubber, much like a tank.
It will autonomously patrol UK roads without the need for road closures and will be able to detect defects such as cracks and potholes, characterise their geometry, collect measurements and capture images.
All this data, along with the hole’s location, will then be sent it to local authorities, who can decide whether to send a team of repair personnel, depending on its size and severity.
For smaller cracks, the vehicle will be able to emit quick drying asphalt as a quick fix before they get bigger and become fully-fledged potholes.
A mini road roller at the back of the robot flattens the sealing material as the robot drives over the site of the defect.
Artist’s impression of the vehicle. Current methods to detect and repair of potholes are labour intensive and as such are slow, unsafe, and costly to the economy and environment
The system’s AI capabilities can also predict road conditions, facilitating the advancement ‘from reactive to preventative road maintenance’.
‘If a pothole is detected, our vehicle will stop, flag its presence, and complete the repair within a few minutes,’ Fichera told Digital Trends.
The electric vehicles will be able to operate continuously for several hours on a single charge, he added.
MailOnline has contacted Robotiz3d over how fast the robot would be travelling and whether it would hold up traffic or potentially congest roads.
The company aims to have its road damage detection unit on the market in six months, although the crack-filling asphalt emission functionality will take a bit longer and should be up and running by the end of 2021.
While potholes can be a very costly inconvenience for drivers, they can be tragically fatal for cyclists and motorcyclists
The machines are anticipated to improve the safety and lifespan of road networks and make more maintenance tasks ‘Covid-resilient’ – by avoiding sending out human workers during social distancing.
Once in operation, an entire fleet around the country will also contribute to the reduction in costs, fuel consumption and greenhouse gas emissions that come from regular pothole-filling, fuel-burning vehicles.
‘This is an exciting new spin out to take forward,’ said Lisa Layzell, co-founder and CEO of Robotiz3d Ltd.
‘The team at Robotiz3d has the expertise and experience in robotics and AI to deliver the project and introduce world-leading innovation to the management of roads and highways.
‘We have developed a robust business plan to take forward the portfolio of Robotiz3d envisaged products.’
The robots may not provide a long-term fix for potholes, however, which are exacerbated by freezing weather due to expanding ice.
Rather than resurfacing stretches of crumbling tarmac at greater expense, potholes that are filled in as they appear on the cheap can quickly open up again.
THE UK’S POTHOLE CRISIS
Pothole numbers and severity are at crisis levels in the UK.
Over the last decade, 18 million potholes have been filled, at a cost exceeding £1 billion.
The cost of repairing all of the UK damaged roads is even more staggering, estimated to exceed £10 billion and may take many decades to complete at the current rate.
The situation is expected to worsen with the rates of pothole occurrence increasing across the UK due to ageing roads, the increasing number of road users, and enhanced vulnerability under new extreme climate scenarios.
Conventional remediation methods are time-consuming, labour intensive and costly.
This is in part due to the outdated and localised repair methods, and poor on-site quality testing means that future repair of the same site is often required.
Robotiz3d says: ‘The posed solution to continually pump money into an increasingly out of control problem is unsustainable.’
In 2018, the number of potholes reported in Scotland was 16,645, in England 15,542 and Wales 3,729.