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Thermal imaging enables fire-fighting drones U
sing AI technology from the University of Sheffield – which
incorporates thermal and optical imaging – the drones can automatically detect and investigate fires, and then self-coordinate to deploy fire retardant onto the fire, monitor the situation and return to base. In August, the team worked
with local fire and rescue services to trial the system in Cornwall. Using drones from Windracers and swarm technology developed by University of Bristol, the week- long trial tested the autonomous fire detection and swarm capabilities. Windracers’ Ultra self-flying
cargo aircraft are each able to carry 100kg of fire retardant. They can fly autonomously in a search pattern to monitor danger areas over the summer months, with a swarm of drones potentially covering areas the size of Greece. The test included one Ultra aircraft, along with three smaller drones. The trial saw the technology
successfully identify and approach a number of small, controlled fires – which were monitored closely by fire and rescue services.
the development of fast and lightweight detection models that can operate using only simple image processing and morphological techniques, which allow for fast real-time detection and localisation.
Thermal imaging aids developments of autonomous fire detection The successful trial was the culmination of four years of research and development as part of the Innovate UK funded project ‘Protecting Environments with Swarms of UAVs’. To help develop autonomous
fire detection and firefighting software for the autonomous aerial platforms, University of Sheffield researchers worked with the Lancashire Fire and Rescue Services, who recorded different videos of fire events.
In most cases, the fire service used a DJI M300 RTK drone equipped with a Zenmuse H20T thermal camera, which includes an nncooled VOx microbolometer sensor, a 13.5mm focal length and a 10.6 degree display field of view. The videos were not embedded with any temperature data and serve purely as a visual means of identifying fire from the environment. “The rationale behind this is that the thermal camera serves as a very selective filter and allows only sufficiently hot areas to appear as high intensity regions in
Making robots easy to operate The University of Bristol’s swarming technology was another crucial element in the project, which involved working with Distributed Avionics on digital twinning, with real-time data and modeling techniques used to mirror physical counterparts in a virtual world. “Finding and tackling
wildfires before they become a problem requires many robots to work together as a swarm,” comments Sabine Hauert, Professor of Swarm Engineering at University of Bristol. “We’ve spoken to firefighters around the world to design a swarm that is useful and easy for them to operate. It was great to see this technology being tested for the first time.”
Agbotic collaborates with Siemens on smart farming project
As a pioneer of advanced automation and machine learning in the agricultural sector, Agbotic’s new partnership with Siemens will integrate the developer’s new artificial intelligence model for agriculture with Siemens’ global reach for industrial automation devices and software. The two companies announced a groundbreaking initiative to validate Agbotic’s AI model and improve farming efficiency. The patent-pending,
Artificially Intelligent Control System Agent works together with industrial controllers to reduce the need for complex manual programming, thus enabling rapid auto-response, iterative tuning and continuous improvement and optimisation across factory operations, with a simplified, cost-effective and sustainable process.
Automated factories to improve efficiency and sustainability
4 IMAGING AND MACHINE VISION EUROPE AUGUST/SEPTEMBER 2024
“[The] rapid commercialisation of this innovation will improve the way factories operate globally,” said John Parrott, VP and head of food & beverages and consumer packaged goods vertical markets at Siemens.”By integrating this capability, we are taking a significant step toward creating factories that are not only more automated and efficient but also more sustainable.” Agbotic utilises automation, machine learning and AI
technologies for a brand of regenerative, organic farming that minimises the use of resources, while also lowering crop production costs. “Historically, most agricultural automation has [been] run on proprietary, closed software,” said Agbotic founder and CEO, John Gaus. “We chose to run on the reliability, flexibility and power of open architecture PLCs. Once you make that move, there’s no better partner than Siemens.”
the image, thus alleviating the shortcomings of colour-based fire detection models which are susceptible to false-positives,” the researchers said. The thermal videos enabled
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