Lifeboat Foundation

The US military and its contractors would be exempt.

Robots that are autonomous or semi-autonomous and carry weapons or offensive capabilities are often called armed robots. These robots can be employed in a variety of settings, including the military, law enforcement, industry, and security.

Today, many armed robots are controlled remotely by human operators who can keep a safe distance between themselves and the devices. This is particularly prevalent with military drones, as the operators control the aircraft and its weaponry from a distance, making the machines even more dangerous to civilians.

Continue reading “US state seeks to outlaw the use of armed robots” »

The new UAV features increased payload capacity, flight time, and a long-range resilient communications kit.

Drones have become an irreplaceable piece of technology to carry out various essential activities in fields like construction, defense, aerial photography, marketing, delivery, agriculture, and rescue, among others.

Firms are making massive strides in improving their versions of drones to cater to more operational requirements. To that extent, US-based Andruil Industries’ popular Ghost drone will get an update that features increased payload and longer flying time. Ghost is classified as a group 2 UAV, as they weigh less than 55 lbs (24 kg) and operate below 3,500 feet.

Get this tiny camera drone with tracking and AI technology for just $149.99. No remote needed when using the Air NEO app.

Processing on the fly gives extreme drones the edge.

The United States Air Force has completed a critical AI-controlled autonomous flight of its modified Osprey Mark III unmanned aerial system.

The USAF reports that the United States Air Force’s (USAF) “Osprey” Mark III unmanned aerial system (UAS) has completed its first fully autonomous test flight. Conducted on July 20, 2023, the test formed part of the USAF’s larger Autonomy, Data, and AI Experimentation (ADAx) Proving Ground effort for the program, specifically the USAF’s Autonomy Prime Environment for Experimentation or APEX, a subset of ADAx. The trial was conducted to evaluate and operationalize artificial intelligence and autonomy concepts to support warfighters on the evolving… More.

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The long-sought finding challenges scientists’ understanding of the strong nuclear force, and the AI that can beat human champions at drone racing.

In what can only bode poorly for our species’ survival during the inevitable robot uprisings, an AI system has once again outperformed the people who trained it. This time, researchers at the University of Zurich in partnership with Intel, pitted their “Swift” AI piloting system against a trio of world champion drone racers — none of whom could best its top time.

Swift is the culmination of years of AI and machine learning research by the University of Zurich. In 2021, the team set an earlier iteration of the flight control algorithm that used a series of external cameras to validate its position in space in real-time, against amateur human pilots, all of whom were easily overmatched in every lap of every race during the test. That result was a milestone in its own right as, previously, self-guided drones relied on simplified physics models to continually calculate their optimum trajectory, which severely lowered their top speed.

This week’s result is another milestone, not just because the AI bested people whose job is to fly drones fast, but because it did so without the cumbersome external camera arrays= of its predecessor. The Swift system “reacts in real time to the data collected by an onboard camera, like the one used by human racers,” an UZH Zurich release reads. It uses an integrated inertial measurement unit to track acceleration and speed while an onboard neural network localizes its position in space using data from the front-facing cameras. All of that data is fed into a central control unit — itself a deep neural network — which crunches through the numbers and devises a shortest/fastest path around the track.

Unmanned aerial vehicles (UAVs), commonly known as drones, are already used in countless settings to tackle real-world problems. These flying robotic systems can, among other things, help to monitor natural environments, detect fires or other environmental hazards, monitor cities and find survivors of natural disasters.

To tackle all of these missions effectively, UAVs should be able to reliably detect targets and objects of interest in their surroundings. Computer scientists have thus been trying to devise new computational techniques that could enable these capabilities, using deep learning or other approaches.

Researchers at Yunnan University and the Chinese Academy of Sciences recently introduced a new object-detection system based on edge computing. Their proposed system, introduced in the IEEE Internet of Things Journal, could provide UAVs with the ability to spot relevant objects and targets in their surroundings without significantly increasing their power-consumption.

The FAA made a big splash when it unveiled its Innovate28 plan for advanced air mobility (AAM) operations with electric vertical takeoff and landing (eVTOL) aircraft at scale by 2028. But while Innovate28 is just that—a plan—the agency’s friend across the Atlantic is already proposing hard requirements for AAM certification, operations, and maintenance.

The European Union Aviation Safety Agency (EASA) on Thursday shared its final opinion on rules and regulations for eVTOL air taxis, drones, and other emerging aircraft with the European Commission. Now, the ball is in the Commission’s court as it determines whether to accept EASA’s policy recommendations.

Opinion No 03/2023 lays out a comprehensive regulatory framework for safe operations of new aircraft types. It introduces requirements for piloted electric air taxi operations, flight crew licensing, air traffic management, and standardized European rules of the air (SERA). The proposal also suggests a criteria and process for the certification and maintenance of remotely piloted drones.