While LiDAR and cameras are becoming ubiquitous for unmanned aerial vehicles (UAVs) but can be ineffective in challenging environments, 4D millimeter-wave (MMW) radars that can provide robust 3D ranging and Doppler velocity measurements are less exploited for aerial navigation. In this paper, we develop an efficient and robust error-state Kalman filter (ESKF)-based radar-inertial navigation for UAVs. The key idea of the proposed approach is the point-to-distribution radar scan matching to provide motion constraints with proper uncertainty qualification, which are used to update the navigation states in a tightly coupled manner, along with the Doppler velocity measurements. Moreover, we propose a robust keyframe-based matching scheme against the prior map (if available) to bound the accumulated navigation errors and thus provide a radar-based global localization solution with high accuracy. Extensive real-world experimental validations have demonstrated that the proposed radar-aided inertial navigation outperforms state-of-the-art methods in both accuracy and robustness.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, the lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace the traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within the ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the \textit{temporal consistency} and \textit{spatial complementarity} between these modalities, we propose two innovative modules, \textit{consistency-instructed collaborative tracking} and \textit{graph-informed adaptive joint optimization}, for accurate drone measurement extraction and efficient sensor fusion. Extensive real-world experiments in landing scenarios from a leading drone delivery company demonstrate that mmE-Loc outperforms state-of-the-art methods in both localization accuracy and latency.

Efficient crop production requires early detection of pest outbreaks and timely treatments; we consider a solution based on a fleet of multiple autonomous miniaturized unmanned aerial vehicles (nano-UAVs) to visually detect pests and a single slower heavy vehicle that visits the detected outbreaks to deliver treatments. To cope with the extreme limitations aboard nano-UAVs, e.g., low-resolution sensors and sub-100 mW computational power budget, we design, fine-tune, and optimize a tiny image-based convolutional neural network (CNN) for pest detection. Despite the small size of our CNN (i.e., 0.58 GOps/inference), on our dataset, it scores a mean average precision (mAP) of 0.79 in detecting harmful bugs, i.e., 14% lower mAP but 32x fewer operations than the best-performing CNN in the literature. Our CNN runs in real-time at 6.8 frame/s, requiring 33 mW on a GWT GAP9 System-on-Chip aboard a Crazyflie nano-UAV. Then, to cope with in-field unexpected obstacles, we leverage a global+local path planner based on the A* algorithm. The global path planner determines the best route for the nano-UAV to sweep the entire area, while the local one runs up to 50 Hz aboard our nano-UAV and prevents collision by adjusting the short-distance path. Finally, we demonstrate with in-simulator experiments that once a 25 nano-UAVs fleet has combed a 200x200 m vineyard, collected information can be used to plan the best path for the tractor, visiting all and only required hotspots. In this scenario, our efficient transportation system, compared to a traditional single-ground vehicle performing both inspection and treatment, can save up to 20 h working time.

At least one person has been killed in an Israeli drone attack on a vehicle in southern Lebanon, just one day after Israeli troops withdrew from most of the border area. Lebanon's state-run National News Agency (NNA) reported that "an enemy drone struck a vehicle… in the town of Aita al-Shaab" on Wednesday. Sanad, Al Jazeera's fact-checking agency, identified the victim as Yusuf Mohammed Sorour, the son of the mayor of the town, targeted while he was sitting in his car in front of his home. NNA had earlier reported that in a separate attack, one person was wounded in the Wazzani region after Israeli forces opened fire "while residents were inspecting" restaurants and cafes in the area. The NNA also reported Israeli forces shot towards homes near the town of Shebaa.

An Arizona state bill would allow local and state authorities to shoot down drones used by drug cartels. House Bill 2733, sponsored by Republican state Rep. David Marshall, would provide qualified immunity to authorities for injuries that may be caused by taking out an "unmanned" drone within 30 miles of the southern border. Cartels regularly use drones as a tool to monitor law enforcement activity around the border in hopes of evading them in their own smuggling operations, even using the technology to send their drugs into the country. Although the situation at the border is calming down, there are still plenty of issues to tackle when it comes to crime, according to one sheriff. A drone reportedly flew into a crowd of Boston Celtics fans Tuesday night at an outdoor party.

Kyiv also said that Russian forces launched two missile strikes and 72 air strikes, and used 1,024 kamikaze drones, along with 4,200 artillery attacks that targeted Ukrainian positions and settlements, AA reports. In Ukraine's Kharkiv region, Ukrainian forces said they prevented Russian advances towards Mala Shapkivka and Topoli, while Moscow's troops launched 16 attacks in Ukraine's Kupiansk region, with Kyiv's forces claiming to have repelled 14, as battles continue, Anadolu reports. Russia said oil flows through the Caspian Pipeline Consortium, a major route for supplying Kazakhstan and exporting to the global market, have been reduced by 30 to 40 percent after a Ukrainian drone attack on a pumping station. The Caspian pipeline, which ships more than 1 percent of daily global oil supplies, stretches over 1,500km (939 miles) and carries crude oil from Kazakhstan's Tengiz oilfield on Russia's northeastern shores of the Caspian Sea as well as from Russian producers. Freedom in Russia and the end of Russian President Vladimir Putin's government depends on Ukraine winning the war, former chess world champion and Kremlin critic Garry Kasparov said.

Trump border czar Tom Homan discusses the administration's latest action to secure the border. The Central Intelligence Agency (CIA) has been conducting surveillance flights with drones over Mexico in partnership with the U.S. neighbor to the south, to gather intelligence on cartels and fentanyl laboratories, according to a senior U.S. official. The Biden administration authorized the use of MQ9 Reaper drones, which the official said are not armed and "not lethal," over Mexico to focus on locating fentanyl labs and cartels. President Donald Trump's administration continued the program, which is being done in coordination with the Mexican government. The intelligence is shared with the Mexican government, which in turn has the authority to act on shutting down any illegal activities associated with the cartels and labs.

This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational constraints for real-time adaptation, and accounting for uncertainty from environmental changes. To address these challenges, we propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization, enabling rapid exploration of multiple trajectory options under uncertainty. The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions. The algorithm efficiently generates trajectories toward multiple potential target drone positions in parallel. It then assesses trajectory reachability by comparing traversal times with the target drone's predicted arrival time, ultimately selecting the minimum-time reachable trajectory. Through extensive validation in both simulated and real-world environments, we demonstrate our method's capability for high-rate online planning and its adaptability to unpredictable movements in unstructured settings.

Sumy is busy enough during the day, with shops open and well-stocked. But once it gets dark the streets are almost deserted. Air raid alerts come frequently. Anti-aircraft guns fire tracer into the sky for hours, aimed at the waves of Russian drones that cross the border near here to attack targets much deeper inside Ukraine – and sometimes in Sumy itself. A big block of flats has a hole three storeys high ripped out of it.

Ukraine and Russia are now three years into what has been called the first drone war: not the first in which they were used, but the first in which they have been a major factor on the battlefield. What lessons have others drawn about the shape of future wars? "Drones are here to stay, and they will be everywhere – on the ground, in the air and at sea – in numbers," says Oleksandra Molloy at the University of New South Wales in Canberra, Australia. "The point of no return was…