We consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions. Drones are an innovative method with many benefits including low-contact delivery thereby reducing the spread of pandemic and vaccine-preventable diseases. While we focus on medical supply delivery for this work, it is applicable to drone delivery for many other applications, including food, postal items, and e-commerce delivery. In this paper, our goal is to address drone delivery challenges by optimizing the distribution operations at a drone hub that dispatch drones to different geographic locations generating stochastic demands for medical supplies. By considering different geographic locations, we consider different classes of demand that require different flight ranges, which is directly related to the amount of charge held in a drone battery. We classify the stochastic demands based on their distance from the drone hub, use a Markov decision process to model the problem, and perform computational tests using realistic data representing a prominent drone delivery company. We solve the problem using a reinforcement learning method and show its high performance compared with the exact solution found using dynamic programming. Finally, we analyze the results and provide insights for managing the drone hub operations.

Eggs littered the sand, but there was no sign of life around or in them. The seabirds that should have been keeping watch had taken off, terrified by a drone that crash-landed into their nesting grounds on an island at the Bolsa Chica Ecological Reserve. "We've never seen such devastation here," said Melissa Loebl, an environmental scientist who manages the Huntington Beach reserve. "This has been really hard for me as a manager." Some 3,000 elegant terns fled the reserve after the drone crashed May 12, leaving behind 1,500 to 2,000 eggs, none of them viable.

It's the sort of thing that can almost pass for background noise these days: over the past week, a number of publications tentatively declared, based on a UN report from the Libyan civil war, that killer robots may have hunted down humans autonomously for the first time. As one headline put it: "The Age of Autonomous Killer Robots May Already Be Here." As you might guess, it's a hard question to answer. The new coverage has sparked a debate among experts that goes to the heart of our problems confronting the rise of autonomous robots in war. Some said the stories were wrongheaded and sensational, while others suggested there was a nugget of truth to the discussion.

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.

A moment later, a missile fired from a drone slams into the vehicle, exploding in an orange flash, blowing the man off his feet and leaving the tank a smoldering wreck. The scene is one of dozens of aerial videos that were posted online in Azerbaijan last year showing off a new weapon. Over six weeks, it helped the nation regain territory in the Nagorno-Karabakh region that had been held by Russian-backed Armenian forces for more than two decades. The videos show attacks on tanks, trucks, command posts, mortar positions and radar installations. Smaller militaries around the world are deploying inexpensive missile-equipped drones against armored enemies, a new battlefield tactic that proved successful last year in regional conflicts, shifting the strategic balance around Turkey and Russia.

Exyn Technologies says it's achieved the highest level of drone autonomy ever, which the company has classified at autonomy level 4A. That's two levels short of full autonomy, but it does enable sophisticated transport, delivery, security, inspection, and research tasks, as well as new collaborative modes with other drones as well as land-based robots. "The operator's just giving very, very high level mission parameters to the drone and leaving it to the drone to figure out how it's going to fly itself -- not just going from point A to point B, but just figuring out how it's going to complete the mission from there," Exyn CEO Nader Elm told me on a recent episode of the TechFirst podcast. "We call this'Scoutonomy' and we've just launched the first iteration of that, and that's our Level 4A." Level zero, as you might expect, is a pilot flying a drone. So are levels one through three, although they add increasing doses of automated stabilization, crash avoidance, position sensing, as well as pilot assistance and warnings as you progress through those levels.

A company-provided photo of a Kargu Rotary Wing Attack Drone Loitering Munition System manufactured by the STM defense company of Turkey. A U.N. report says the weapons system was used in Libya in March 2020. A company-provided photo of a Kargu Rotary Wing Attack Drone Loitering Munition System manufactured by the STM defense company of Turkey. A U.N. report says the weapons system was used in Libya in March 2020. Military-grade autonomous drones can fly themselves to a specific location, pick their own targets and kill without the assistance of a remote human operator.

It is one of the more unlikely consequences of the pandemic: a plague of dog shit, with no obvious solution in sight. This story originally appeared on WIRED UK. The rise is down to the sheer number of potential pet owners that rushed to realize the dream of owning a dog while in lockdown. Such was the demand, the price of puppies in the UK more than doubled last year, with popular breeds selling for more than £3,000 a pup. And with such money came the thieves and fraudsters.

A military drone may have autonomously attacked humans for the first time without being instructed to do so, according to a recent report by the UN Security Council. The report, published in March, claimed that the AI drone – Kargu-2 quadcopter – produced by Turkish military tech company STM, attacked retreating soldiers loyal to Libyan General Khalifa Haftar. The 548-page report by the UN Security Council's Panel of Experts on Libya has not delved into details on if there were any deaths due to the incident, but it raises questions on whether global efforts to ban killer autonomous robots before they are built may be futile. Over the course of the year, the UN-recognized Government of National Accord pushed the Haftar Affiliated Forces (HAF) back from the Libyan capital Tripoli, and the drone may have been operational since January 2020, the experts noted. "Logistics convoys and retreating HAF were subsequently hunted down and remotely engaged by the unmanned combat aerial vehicles or the lethal autonomous weapons systems such as the STM Kargu-2," the UN report noted.

A "lethal" weaponized drone "hunted down a human target" without being told to for the first time, according to a UN report seen by the New Scientist. The March 2020 incident saw a KARGU-2 quadcopter autonomously attack a human during a conflict between Libyan government forces and a breakaway military faction, led by the Libyan National Army's Khalifa Haftar, the Daily Star reported. The Turkish-built KARGU-2, a deadly attack drone designed for asymmetric warfare and anti-terrorist operations, targeted one of Haftar's soldiers while he tried to retreat, according to the paper. The drone, which can be directed to detonate on impact, was operating in a "highly effective" autonomous mode that required no human controller, the New York Post said. "The lethal autonomous weapons systems were programmed to attack targets without requiring data connectivity between the operator and the munition: in effect, a true'fire, forget and find' capability," the report from the UN Security Council's Panel of Experts on Libya said.