Fires in industrial facilities pose special challenges to firefighters, e.g., due to the sheer size and scale of the buildings. The resulting visual obstructions impair firefighting accuracy, further compounded by inaccurate assessments of the fire's location. Such imprecision simultaneously increases the overall damage and prolongs the fire-brigades operation unnecessarily. We propose an automated assistance system for firefighting using a motorized fire monitor on a turntable ladder with aerial support from an unmanned aerial vehicle (UAV). The UAV flies autonomously within an obstacle-free flight funnel derived from geodata, detecting and localizing heat sources. An operator supervises the operation on a handheld controller and selects a fire target in reach. After the selection, the UAV automatically plans and traverses between two triangulation poses for continued fire localization. Simultaneously, our system steers the fire monitor to ensure the water jet reaches the detected heat source. In preliminary tests, our assistance system successfully localized multiple heat sources and directed a water jet towards the fires.

Cooperative heterogeneous multi-agent tasks require agents to effectively coordinate their behaviors while accounting for their relative capabilities. Learning-based solutions to this challenge span between two extremes: i) shared-parameter methods, which encode diverse behaviors within a single architecture by assigning an ID to each agent, and are sample-efficient but result in limited behavioral diversity; ii) independent methods, which learn a separate policy for each agent, and show greater behavioral diversity but lack sample-efficiency. Prior work has also explored selective parameter-sharing, allowing for a compromise between diversity and efficiency. None of these approaches, however, effectively generalize to unseen agents or teams. We present Capability-Aware Shared Hypernetworks (CASH), a novel architecture for heterogeneous multi-agent coordination that generates sufficient diversity while maintaining sample-efficiency via soft parameter-sharing hypernetworks. Intuitively, CASH allows the team to learn common strategies using a shared encoder, which are then adapted according to the team's individual and collective capabilities with a hypernetwork, allowing for zero-shot generalization to unseen teams and agents. We present experiments across two heterogeneous coordination tasks and three standard learning paradigms (imitation learning, on- and off-policy reinforcement learning). CASH is able to outperform baseline architectures in success rate and sample efficiency when evaluated on unseen teams and agents despite using less than half of the learnable parameters.

Drones could soon be working together in swarms to put out flames before they become wildfires, UK researchers hope. A team of firefighters, scientists and engineers are working on a project they say will allow swarms of up to 30 autonomous planes to spot and extinguish fires by working collectively using artificial intelligence. Drones piloted by people are already used in firefighting, for example to detect hidden blazes and assess safety risks. The research is still in the test phase and has not been used on a wildfire, but the team claims it is the first to combine unpiloted drone technology with swarm engineering in the field of firefighting.

This communication presents preliminary findings from comparing two recent chatbots, OpenAI's ChatGPT and Google's Bard, in the context of fire engineering by evaluating their responses in handling fire safety related queries. A diverse range of fire engineering questions and scenarios were created and examined, including structural fire design, fire prevention strategies, evacuation, building code compliance, and fire suppression systems (some of which resemble those commonly present in the Fire Protection exam (FPE)). The results reveal some key differences in the performance of the chatbots, with ChatGPT demonstrating a relatively superior performance. Then, this communication highlights the potential for chatbot technology to revolutionize fire engineering practices by providing instant access to critical information while outlining areas for further improvement and research. Evidently, and when it matures, this technology will likely be elemental to our engineers' practice and education.

The prototype drone, called FireDrone, could be sent into burning buildings or woodland to assess hazards and provide crucial first-hand data from danger zones. The data would then be sent to first responders to help inform their emergency response. The drone is made of a new thermal aerogel insulation material and houses an inbuilt cooling system to help it withstand temperatures of up to 200 C for ten minutes. Currently at prototype stage, the researchers believe FireDrone could eventually be used to scope out fires for people and extra hazards to bolster firefighting. Principal Investigator Professor Mirko Kovac, Director of the Aerial Robotics Lab at Imperial College London and Head of the Laboratory of Sustainability Robotics at Empa, said: "Until they enter the danger zone, firefighters can't be certain of what or who they'll find, and what challenges they'll encounter. "FireDrone could be sent in ahead to gather crucial information so that responders can prepare accordingly to ...

High above the valley in California's wine country, Joanna Wells' vineyard is a challenging place to grow grapes. It's nearly 3,000 feet above sea level, atop a mountain ridge, "Forty minutes off a main road just to get there," Wells says. But it's these rocky hilltop terrains, with plenty of sunshine and maritime breezes, that Wells says, are perfect for the job. What's not idyllic, though, is California's extreme weather. "Every year tends to be climatically extreme now."

Using satellites, drones and artificial intelligence, emerging technology is changing the way firefighting agencies and governments battle the ever-increasing threat of wildfires as hundreds of thousands of acres burn across the western United States. New programs are being developed by startups and research institutions to predict fire behavior, monitor drought and even detect fires when they first start. As climate change continues to increase the intensity and frequency of wildfires, these breakthroughs offer at least one tool in the growing arsenal of prevention and suppression strategies. "This is not to replace firefighting on the ground," said Ilkay Altintas, a computer scientist with the University of California, San Diego, who developed a fire map for the region. "The more science and data we can give firefighters and the public, the quicker we'll have solutions to combat and mitigate wildfires."

An illustration of a drone that sprays crops, the kind of gadget that Google saw as potentially useful for fighting fires. Google asked the US Federal Aviation Administration for permission to test a drone for monitoring and fighting fires. However, its drone plans, which were published Thursday in the federal register, have since been extinguished. The request came from Alphabet's Google Research Climate and Energy Group -- not the company's Wing subsidiary, whose drone delivery service was certified by the FAA in 2019. Wing drones are being used to deliver food and medicine during the coronavirus pandemic.

AI startup Merlin Labs today deactivated stealth mode to announce a $25 million funding round and a partnership with Dynamic Aviation to put a fleet of 55 King Air planes in the sky without humans aboard. What we're building is software that creates a think-for-itself-pilot … fully-autonomous flight take-off to touchdown. The big idea: See a need, fill a need. Merlin Labs is taking autonomous software technology and building an artificially intelligent pilot. Autonomous fixed-wing flight might sound familiar, but there's a huge difference between designing a remote or hybrid-controlled drone from the ground up and building a system that can fly nearly any fixed wing aircraft.

In this special guest feature, Sarah Lahav of SysAid Technologies takes a look at machine learning and how it provides a vehicle for "fireproofing" in IT service management – anticipating and preventing problems rather than waiting for them. As the company's first employee, Sarah has remained the vital link between SysAid Technologies and its customers since 2003. She is the current CEO and former VP of Customer Relations at SysAid – two positions that have fueled her passion in customer service. In IT service management, "firefighting" has become a metaphor for much of the work. Someone or something starts a digital fire, and we put it out.