Russia has conferred state awards on the two fighter pilots involved in the downing of a US surveillance drone that crashed into the Black Sea, the Russian Defence Ministry said, while United States officials announced that its spy flights in the region have resumed. Presenting the awards on Friday to the Su-27 jet fighter pilots, Russia's Defence Minister Sergei Shoigu lauded their achievement in preventing the drone from flying into an area near Crimea to which Moscow has banned access. "The drone flew with its transponders off, violating the boundaries of the area of the temporary airspace usage regime established for the special military operation [and] communicated to all users of international airspace," Russia's defence ministry said in a statement, according to The Moscow Times. Pro-Kremlin political analyst Sergei Markov said the awards for the pilots were "a clear sign that Russia will keep downing" US drones. "This decision will receive a strong support from the Russian society that wants the government to toughen its policy," Markov wrote in a commentary. Russia's presentation of the awards comes a day after the US military released a declassified 42-second video clip showing the Russian Su-27 fighter jets intercepting the drone and making close passes while dumping fuel in an apparent bid to damage the drone's optical and other hi-tech instruments.

In many specific scenarios, accurate and effective system identification is a commonly encountered challenge in the model predictive control (MPC) formulation. As a consequence, the overall system performance could be significantly weakened in outcome when the traditional MPC algorithm is adopted under those circumstances when such accuracy is lacking. This paper investigates a non-parametric closed-loop behavior learning method for multi-agent motion planning, which underpins a data-driven predictive control framework. Utilizing an innovative methodology with closed-loop input/output measurements of the unknown system, the behavior of the system is learned based on the collected dataset, and thus the constructed non-parametric predictive model can be used to determine the optimal control actions. This non-parametric predictive control framework alleviates the heavy computational burden commonly encountered in the optimization procedures typically in alternate methodologies requiring open-loop input/output measurement data collection and parametric system identification. The proposed data-driven approach is also shown to preserve good robustness properties. Finally, a multi-UAV system is used to demonstrate the highly effective outcome of this promising development.

Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap, thus suitable for exploring narrow spaces. However, due to their extremely limited computing power and storage, vision algorithms designed for high-performance GPU platforms cannot be used for nano drones. To address this issue this paper presents a lightweight CNN depth estimation network deployed on nano drones for obstacle avoidance. Inspired by Knowledge Distillation (KD), a Channel-Aware Distillation Transformer (CADiT) is proposed to facilitate the small network to learn knowledge from a larger network. The proposed method is validated on the KITTI dataset and tested on a nano drone Crazyflie, with an ultra-low power microprocessor GAP8.

Cellular-connected unmanned aerial vehicle (UAV) swarm is a promising solution for diverse applications, including cargo delivery and traffic control. However, it is still challenging to communicate with and control the UAV swarm with high reliability, low latency, and high energy efficiency. In this paper, we propose a two-phase command and control (C&C) transmission scheme in a cellular-connected UAV swarm network, where the ground base station (GBS) broadcasts the common C&C message in Phase I. In Phase II, the UAVs that have successfully decoded the C&C message will relay the message to the rest of UAVs via device-to-device (D2D) communications in either broadcast or unicast mode, under latency and energy constraints. To maximize the number of UAVs that receive the message successfully within the latency and energy constraints, we formulate the problem as a Constrained Markov Decision Process to find the optimal policy. To address this problem, we propose a decentralized constrained graph attention multi-agent Deep-Q-network (DCGA-MADQN) algorithm based on Lagrangian primaldual policy optimization, where a PID-controller algorithm is utilized to update the Lagrange Multiplier. Simulation results show that our algorithm could maximize the number of UAVs that successfully receive the common C&C under energy constraints. This paper was presented in part at the 2022 IEEE Global Communications Conference, December 2022 [1].

China has developed a new drone that functions in air and water. The year 2023 is turning out to be the year of drone delivery. Several startups have been hard at work testing, learning and honing their ability to deploy a network of drones for efficient delivery. Instant gratification in getting a last-minute item, prescription drug and fast food in record time are some of the focuses anticipated to drive initial demand. Food delivery has grown immensely in popularity, especially since the start of the COVID-19 pandemic.

Rep. Nick LaLota, R-N.Y., reacts to new video showing a Russian jet hitting a U.S. drone and weighs in on the border crisis. EXCLUSIVE: Several Republican senators are calling on Congress to exercise its oversight authority after a Russian fighter jet downed a U.S. drone over international waters earlier this week – an incident that has many worried about more direct conflict between the two superpowers as the Ukraine war enters its second year. "We need a hearing on it. We've asked the Pentagon for hearing, of course, they're probably a little bit busy right now," Sen. Tommy Tuberville, R-Ala., a member of the Armed Services Committee, told Fox News Digital this week. When asked if he was concerned about Russian officials' announcement that Moscow will try to retrieve the debris, Tuberville said, "Yeah, we should all be."

Mark Kelly, D-Ariz., weighs in on challenges Border Patrol faces as more migrants flood the southern border on'Special Report.' The crash between a Russian fighter jet and a U.S. drone likely resulted from the pilot's aggression and "incompetence," according to former astronaut and Navy captain, Sen. Mark Kelly. "Look at the level of incompetence – I mean when we saw the flanker yesterday, which basically had a midair with the MQ-9 [drone], with a reaper … I spent 15 years in the astronaut office, I used to fly with Russian fighter pilots in the backseat of my plane," Kelly, D-AZ., told Fox News chief political anchor and host of "Special Report with Brett Baier" during an interview Thursday. "The level of incompetence in the Russian pilots that I flew with was shocking to me." Russia has denied that its plane crashed into the U.S. drone despite video evidence showing the plane make at least two fly-bys, including one in which it appeared to dump fuel on the drone before the feed abruptly cut off, and the drone crashed into the Black Sea. On Friday, Russian Minister of Defense Sergei Shoigu reportedly presented the pilots responsible for crashing the drone with state awards, saying the pilots prevented the drone from "violating the boundaries of the temporary airspace regime established for the special military operation," referring to the invasion of Ukraine.

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Kyiv, Ukraine – It looked like a deliberate manoeuvre by a skilled pilot that led to the first direct military clash between the United States and Russia since Moscow invaded Ukraine. Two Russian fighter jets approached a US drone flying in the cloudless, azure sky over international waters in the Black Sea on Tuesday morning. One of the Russian Su-27s released a stream of jet fuel on the MQ-9 Reaper drone, causing its cameras to shut off. Then the Su-27 hit the Reaper's propeller, causing it to tumble into the sea, the Pentagon said. It said the Reaper was a "reconnaissance drone" and carried no arms, although the unmanned aircraft with a wingspan of 26 metres (85 feet) was designed as a "hunter-killer" armed with laser-guided bombs and missiles.

Former U.S. Amb. to NATO Kurt Volker says the Russian fighter jet collision was'intentional' and requires a'firm response' from the U.S. The Russian government has awarded the pilots involved in the harassment and crash of a U.S. drone in international airspace. Russian minister of Defense Sergei Shoigu presented state awards to the fighter jet pilots responsible for downing a U.S. drone over the Black Sea earlier this week. Russian President Vladimir Putin, right, and Russian Defence Minister Sergei Shoigu, left, attend a wreath-laying ceremony at the Eternal Flame and the Unknown Soldier's Grave in Alexander Garden during an event marking Defender of the Fatherland Day in Moscow. In an official statement, the Ministry of Defense commended the pilots for preventing the drone from "violating the boundaries of the temporary airspace regime established for the special military operation." The statement accuses the U.S. drone of flying with its transponders off.