face_with_colon_three circa 2019.
Drones have already conquered Earth, and now they’re heading out into the solar system.
NASA announced yesterday (June 27) that it will launch a life-hunting rotorcraft called Dragonfly toward Saturn’s huge moon Titan in 2026. If all goes according to plan, Dragonfly will land on the hazy, frigid satellite in 2034 and then spend several years flying around, gathering a variety of data and snapping amazing photos of the exotic landscape.
SpaceX is launching South Korea’s first dedicated military communications satellite on Monday, with a target liftoff time of 5 PM EDT (2 PM PDT). The launch window spans nearly four hours, ending at 8:55 PM EDT (5:55 PM PDT), so SpaceX has considerable flexibility in terms of when the launch could actually take place.
The Falcon 9 rocket being used for this mission includes a first-stage booster that flew previously on SpaceX and NASA’s Demo-2 mission — the historic mission that carried astronauts on board a SpaceX rocket for the first time. That launch, which took place on May 30, saw astronauts Bob Behnken and Doug Hurley successfully delivered to the International Space Station — where they’re currently preparing to depart on Demo-2’s concluding trip home on August 1.
This mission will include a recovery attempt for the first stage, using SpaceX’s “Just Read the Instructions” drone landing ship in the Atlantic Ocean.
Swarm intelligence (SI) is concerned with the collective behaviour that emerges from decentralised self-organising systems, whilst swarm robotics (SR) is an approach to the self-coordination of large numbers of simple robots which emerged as the application of SI to multi-robot systems. Given the increasing severity and frequency of occurrence of wildfires and the hazardous nature of fighting their propagation, the use of disposable inexpensive robots in place of humans is of special interest. This paper demonstrates the feasibility and potential of employing SR to fight fires autonomously, with a focus on the self-coordination mechanisms for the desired firefighting behaviour to emerge. Thus, an efficient physics-based model of fire propagation and a self-organisation algorithm for swarms of firefighting drones are developed and coupled, with the collaborative behaviour based on a particle swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change. Numerical experiments demonstrate that the proposed self-organising system is effective, scalable and fault-tolerant, comprising a promising approach to dealing with the suppression of wildfires – one of the world’s most pressing challenges of our time.
According to Forbes, payroll costs consume up to 25 per cent of a restaurant’s profit. Restaurateurs in Sydney and other parts of Australia hope to combat that expense by following in the footsteps of venues in Asia that have used drone waiters instead of human wait staff.
Faster and Human-Free Waiter drones are robotic devices that soar through the air with platters of food and glasses of beverages perched on top. Customers place their orders via electronic devices or other means, then the kitchen sends out their food on trays carried by machines rather than humans. Each drone can carry up to 4.4 pounds of cargo.
Sensors on the sides of the drones prevent them from crashing into objects or people as they navigate busy restaurants. While this strategy eliminates the human element that many experts believe is essential to the hospitality industry, the waiter drones’ success in Asia suggests they might prove a valuable contribution to restaurants in Australia.
Drone companies like DJI, Aerones, and Walkera are thinking about how drones can soon revolutionize the way we fight fires.
A drone has successfully inspected a 19.4 meter high oil tank onboard a Floating Production, Storage and Offloading vessel. The video shot by the drone was interpreted in real-time by an algorithm to detect cracks in the structure.
Scout Drone Inspection and class society DNV GL have been working together to develop an autonomous drone system to overcome the common challenges of tank inspections. For the customer, costs can run into hundreds of thousands of dollars as the tank is taken out of service for days to ventilate and construct scaffolding. The tanks are also tough work environments, with surveyors often having to climb or raft into hard to reach corners. Using a drone in combination with an algorithm to gather and analyse video footage can significantly reduce survey times and staging costs, while at the same time improving surveyor safety.
“We’ve been working with drone surveys since 2015,” said Geir Fuglerud, director of ofshore classification at DNV GL – Maritime. “This latest test showcases the next step in automation, using AI to analyse live video. As class we are always working to take advantage of advances in technology to make our surveys more efficient and safer for surveyors, delivering the same quality while minimising our operational downtime for our customers.”
More durable prosthetics and medical devices for patients and stronger parts for airplanes and automobiles are just some of the products that could be created through a new 3D printing technology invented by a UMass Lowell researcher.
Substances such as plastics, metals and wax are used in 3D printers to make products and parts for larger items, as the practice has disrupted the prototyping and manufacturing fields. Products created through the 3D printing of plastics include everything from toys to drones. While the global market for 3D plastics printers is estimated at $4 billion and growing, challenges remain in ensuring the printers create objects that are produced quickly, retain their strength and accurately reflect the shape desired, according to UMass Lowell’s David Kazmer, a plastics engineering professor who led the research project.
Called injection printing, the technology Kazmer pioneered is featured in the academic journal Additive Manufacturing posted online last week.
Drones of all sizes are being used by environmental advocates to monitor deforestation, by conservationists to track poachers, and by journalists and activists to document large protests. As a political sociologist who studies social movements and drones, I document a wide range of nonviolent and pro-social drone uses in my new book, “The Good Drone.” I show that these efforts have the potential to democratize surveillance.
But when the Department of Homeland Security redirects large, fixed-wing drones from the U.S.-Mexico border to monitor protests, and when towns experiment with using drones to test people for fevers, it’s time to think about how many eyes are in the sky and how to avoid unwanted aerial surveillance. One way that’s within reach of nearly everyone is learning how to simply disappear from view.
Drone disruptions have become notorious at a number of European airports in the last few years, causing thousands of canceled flights. New technology, present at airports now, could help in detecting drones early and preventing disruptions. The anti-drone technology could be a gamechanger, allowing for seamless operations even with rogue drones around.
The first major case of a drone disruption came in 2018 at London’s Gatwick Airport. The airport had to shut down intermittently for three days after suspected drones were spotted near the runway. The shutdowns caused millions in losses for airlines and left thousands of flights affected.
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StartX startup Buzz Solutions out of Stanford, California just introduced its AI solution to help utilities quickly spot powerline and grid faults so repairs can be made before wildfires start.
Their unique platform uses AI and machine vision technology to analyze millions of images of powerlines and towers from drones, helicopters, and aircraft to find dangerous faults and flaws as well as overgrown vegetation, in and around the grid infrastructure to help utilities identify problem areas and repair them before a fire starts.
This system can do the analysis at half the cost and in a fraction of the time compared to humans, hours to days not months to years.
