Efficient aerial data collection is important in many remote sensing applications. In large-scale monitoring scenarios, deploying a team of unmanned aerial vehicles (UAVs) offers improved spatial coverage and robustness against individual failures. However, a key challenge is cooperative path planning for the UAVs to efficiently achieve a joint mission goal. We propose a novel multi-agent informative path planning approach based on deep reinforcement learning for adaptive terrain monitoring scenarios using UAV teams. We introduce new network feature representations to effectively learn path planning in a 3D workspace. By leveraging a counterfactual baseline, our approach explicitly addresses credit assignment to learn cooperative behaviour. Our experimental evaluation shows improved planning performance, i.e. maps regions of interest more quickly, with respect to non-counterfactual variants. Results on synthetic and real-world data show that our approach has superior performance compared to state-of-the-art non-learning-based methods, while being transferable to varying team sizes and communication constraints.

Kyiv, Ukraine – "UFOs" have rained on Russia in recent days – some dangerously close to the capital Moscow and President Vladimir Putin's hometown. Russian officials and media, using that term – "unidentified foreign objects" – seem unnerved and are accusing Ukraine of drone attacks. Ukraine on Wednesday denied targeting Russia, suggesting attempts at domestic assaults – which Moscow did not accept. With a dash of black humour, presidential adviser Mykhailo Podolyak tweeted that a sense of "panic and collapse" was growing in Russia, "manifested by increasing domestic attacks of unidentified flying objects on infrastructure sites". Throughout the war, Ukrainian leaders and top brass have routinely refused any responsibility for attacks on Russian soil – and often resort to ridiculing disorganised Russian servicemen.

Ukraine said it had survived a months-long winter onslaught of Russian strikes on water and energy infrastructure, as it marked the first day of spring Wednesday. But Kyiv was under fierce pressure in the eastern town of Bakhmut while Moscow said it had downed a "massive" barrage of Ukrainian drones launched at the Crimean peninsula, annexed by the Kremlin in 2014. Since October Russia has been pummelling key facilities in Ukraine with missiles and drones, disrupting water, heating and electricity supplies to millions of people. Foreign Minister Dmytro Kuleba said Ukraine had overcome "winter terror" brought against his country by Russian leader Vladimir Putin and hailed the first day of spring as another "major defeat" for the Kremlin. "We survived the most difficult winter in our history. It was cold and dark, but we were unbreakable," Kuleba said in a statement.

UNITED24 and Monobank have partnered with actor Mark Hamill to launch a fundraising campaign with the aim of collecting funds to help Ukraine purchase drones to use in the war against Russia. The fundraising campaign will send out 500 packs of unique iodized salt from the state-owned company Artemsil, which will be raffling off the prize among those who contribute at least UAH 10 ($0.27) or more. The first 100 participants who contribute at least UAH 100,000 ($2,701) will receive a salt pack signed by the commander of the ground forces of the Armed Forces of Ukraine Oleksandr Syrskyi. Another 10 packs of salt will also be signed by Hamill, the ambassador for UNITED24 and an actor well-known for his role as Luke Skywalker in the Star Wars saga, according to a press release. The project aims to raise UAH 59.2 million ($1.6 million) for Ukraine to purchase 300 DJI Mavic 3T Thermal drones.

Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track in a 35m * 35m space with a top speed of 14m/s. Flight tests are performed on two quadrotors passing through 3 waypoints in a 4m * 2m flight arena to demonstrate the feasibility of the proposed method in the real world. Both simulations and real-world flight tests show that the proposed method can generate the optimal aggressive trajectories for a swarm of autonomous racing drones. The method can also be easily transferred to other types of robot swarms.

Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high-resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. The code is shared here: https://github.com/bilel-bj/TAU. Video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI.

The capability to extract task specific, semantic information from raw sensory data is a crucial requirement for many applications of mobile robotics. Autonomous inspection of critical infrastructure with Unmanned Aerial Vehicles (UAVs), for example, requires precise navigation relative to the structure that is to be inspected. Recently, Artificial Intelligence (AI)-based methods have been shown to excel at extracting semantic information such as 6 degree-of-freedom (6-DoF) poses of objects from images. In this paper, we propose a method combining a state-of-the-art AI-based pose estimator for objects in camera images with data from an inertial measurement unit (IMU) for 6-DoF multi-object relative state estimation of a mobile robot. The AI-based pose estimator detects multiple objects of interest in camera images along with their relative poses. These measurements are fused with IMU data in a state-of-the-art sensor fusion framework. We illustrate the feasibility of our proposed method with real world experiments for different trajectories and number of arbitrarily placed objects. We show that the results can be reliably reproduced due to the self-calibrating capabilities of our approach.

A Ukrainian drone attack on an airbase for bombers in southern Russia in December left three people dead, Moscow said. The Ukrainian military did not officially admit to the attack, but air force spokesman Yuriy Ihnat said the explosions were the result of what Russia was doing on Ukrainian soil.

Fox News chief national security correspondent Jennifer Griffin discusses the war in Ukraine and the growing divide over the United States' role on'Sunday Night In America.' Russian President Vladimir Putin ordered officials on Tuesday to tighten up Russia's borders after the country saw a series of overnight drone strikes that allegedly targeted oil depots. In what appeared to be three separate attacks, Russia saw at least two strikes in its southern regions north of Georgia, as well as outside Moscow. Russia's RIA reported that one drone "crashed" near a gas distribution facility roughly 60 miles outside of Moscow. While nothing was hit and no injuries were reported following the incident, the regional governor said a "civilian infrastructure facility" was likely the target. Russian President Vladimir Putin chairs a meeting of the Supervisory Board of the Agency for Strategic Initiatives to promote new projects in Moscow Feb. 9, 2023.

Russian President Vladimir Putin has ordered officials to tighten control of the border with Ukraine after a spate of drone attacks that Russian authorities blamed on Kyiv delivered a new challenge to Moscow a year after its full-scale invasion of its neighbour. One drone crashed on Tuesday just 100km (60 miles) southeast of Moscow in an alarming development for Russian defences. While Putin didn't refer to any specific attacks in a speech in the Russian capital, he stepped up border controls hours after drone attacks targeted several areas in southern and western Russia and authorities closed the airspace over St Petersburg in response to what some reports said was a drone. Also on Tuesday, several Russian television stations aired a missile attack warning that officials blamed on hacking. The drone attacks caused no casualties but provoked a security stir after the war in Ukraine marked its first anniversary last week.