Israeli attacks on southern Lebanon on multiple vehicles have killed three people as attacks continue despite a November ceasefire with the armed group Hezbollah. Lebanon's Ministry of Public Health said on Saturday that one person was killed in an "Israeli enemy" drone strike on a car in the village of Kunin while two others were killed after an Israeli strike on a motorcycle in Mahrouna, near Tyre. The Israeli army claimed that the attack on the car "eliminated the terrorist Hassan Muhammad Hammoudi", who it said was responsible for antitank missile attacks on Israeli territory during the recent war. The latest Israeli attacks came a day after Israel killed a woman and wounded 25 people in attacks across southern Lebanon. Lebanon's National News Agency reported that the woman was killed in an Israeli drone strike on an apartment in the city of Nabatieh.

Ukraine said it carried out an overnight drone strike on the Kirovske airfield in Crimea and claimed that multiple Russian helicopters and an air defence system were destroyed in the strike. According to a Ukraine Security Service (SBU) statement, the drones targeted areas where Russian aviation units, air defence assets, ammunition depots and unmanned aerial vehicles were located. The agency claimed that Mi-8, Mi-26, and Mi-28 helicopters, as well as a Pantsir-S1 missile and gun system were destroyed. "Secondary detonations continued throughout the night at the airfield," the SBU said, calling the strike part of broader efforts to disrupt Russian aerial operations. "The enemy must understand that expensive military equipment and ammunition are not safe anywhere – not on the line of contact, not in Crimea, and not deep in the rear."

Ukraine's European allies pledged increased levels of military aid to Ukraine this year, making up for a United States aid freeze, as Russian President Vladimir Putin reaffirmed his ambition to absorb all of Ukraine into the Russian Federation. "At this moment, the Europeans and the Canadians have pledged, for this year, 35bn in military support to Ukraine," said NATO Secretary-General Mark Rutte ahead of the alliance's annual summit, which took place in The Hague on Tuesday and Wednesday, June 24-25. "Last year, it was just over 50bn for the full year. Now, before we reach half year, it is already at 35bn. And there are even others saying it's already close to 40bn," he added.

White House press secretary Karoline Leavitt answers questions on U.S. strikes on Iran amid an intelligence leak about the operation. FIRST ON FOX: A group of House Republicans is demanding to know how the U.S. is ready to protect its own domestic assets in the event of a potential attack on the homeland. "We write to inquire with the U.S. Department of Defense (DOD) and the Department of Homeland Security (DHS) about the current state of drone attack countermeasures for our military installations, government buildings, embassies, and consulates, both domestic and abroad," the GOP lawmakers wrote in a letter. "The ongoing conflicts in Ukraine and the Middle East have demonstrated that large-scale, highly coordinated mass-drone attacks can be highly effective if the defender lacks adequate counter-drone defenses." An Iranian demonstrator holds an anti-American sign.

--Reinforcement learning (RL) policies deployed in safety-critical systems, such as unmanned aerial vehicle (UA V) navigation in dynamic airspace, are vulnerable to out-of-distribution (OOD) adversarial attacks in the observation space. These attacks induce distributional shifts that significantly degrade value estimation, leading to unsafe or suboptimal decision-making rendering the existing policy fragile. T o address this vulnerability, we propose an antifragile RL framework designed to adapt against curriculum of incremental adversarial perturbations. The framework introduces a simulated attacker which incrementally increases the strength of observation-space perturbations which enables the RL agent to adapt and generalize across a wider range of OOD observations and anticipate previously unseen attacks. We begin with a theoretical characterization of fragility, formally defining catastrophic forgetting as a monotonic divergence in value function distributions with increasing perturbation strength. Building on this, we define antifragility as the boundedness of such value shifts and derive adaptation conditions under which forgetting is stabilized. Our method enforces these bounds through iterative expert-guided critic alignment using Wasserstein distance minimization across incrementally perturbed observations. We empirically evaluate the approach in a UA V deconfliction scenario involving dynamic 3D obstacles. Results show that the antifragile policy consistently outperforms standard and robust RL baselines when subjected to both projected gradient descent (PGD) and GPS spoofing attacks, achieving up to 15% higher cumulative reward and over 30% fewer conflict events. These findings demonstrate the practical and theoretical viability of antifragile reinforcement learning for secure and resilient decision-making in environments with evolving threat scenarios. Fragility, robustness, and antifragility represent a continuum of system responses to stress or external perturbations [1, 2].

Drones can inspect overhead power lines while they remain energized, significantly simplifying the inspection process. However, localizing a drone relative to all conductors using an onboard LiDAR sensor presents several challenges: (1) conductors provide minimal surface for LiDAR beams limiting the number of conductor points in a scan, (2) not all conductors are consistently detected, and (3) distinguishing LiDAR points corresponding to conductors from other objects, such as trees and pylons, is difficult. This paper proposes an estimation approach that minimizes the error between LiDAR measurements and a single geometric model representing the entire conductor array, rather than tracking individual conductors separately. Experimental results, using data from a power line drone inspection, demonstrate that this method achieves accurate tracking, with a solver converging under 50 ms per frame, even in the presence of partial observations, noise, and outliers. A sensitivity analysis shows that the estimation approach can tolerate up to twice as many outlier points as valid conductors measurements.

Gilan, Iran – The Israeli military used hundreds of fighter jets, armed drones and refuelling planes to attack Iran during its 12-day war backed by the United States, but it was also heavily assisted by operations launched from deep within Iranian soil. Just hours after the Israeli army and Mossad spy agency started their attacks before dawn on June 13, they released footage that appeared to have been recorded at night from undisclosed locations inside Iran. One grainy video showed Mossad operatives, camouflaged and wearing tactical gear including night-vision goggles, crouched in what looked like desert terrain, deploying weapons that aimed to destroy Iran's air defence systems to help pave the way for incoming attack aircraft. Others showed projectiles, with mounted cameras, descending to slam into Iranian missile defence batteries, as well as ballistic missile platforms. The projectiles appeared to be Spike missiles – relatively small, precision-guided anti-armour missiles that can be programmed to fly to targets that are out of their line of sight.

Sen. Tim Kaine, D-Va., tells Fox News Digital he's frustrated by US officials not being forthcoming about the drone incursions over Langley Air Force Base. FIRST ON FOX: A group of House Republicans is demanding details on how government agencies are addressing the growing threat of unauthorized drone incursions on U.S. military installations. In letters sent Thursday, the Subcommittee on Military and Foreign Affairs requested a trove of documents and communications from the Departments of Defense (DoD), Transportation (DOT), and Justice (DOJ). The letters note that in 2024 alone, there were 350 drone incursions at over 100 U.S. military bases. Lawmakers believe many of the responses to the illegal incursions, including an instance where a group of drones traipsed over Langley Air Force Base for over two weeks in December 2023, have been insufficient and fragmented.

This study addresses the optimisation of task allocation for Unmanned Aerial Vehicles (UAVs) within industrial monitoring missions. The proposed methodology integrates a Genetic Algorithms (GA) with a 2-Opt local search technique to obtain a high-quality solution. Our approach was experimentally validated in an industrial zone to demonstrate its efficacy in real-world scenarios. Also, a Hardware-in-the-loop (HIL) simulator for the UAVs team is introduced. Moreover, insights about the correlation between the theoretical cost function and the actual battery consumption and time of flight are deeply analysed. Results show that the considered costs for the optimisation part of the problem closely correlate with real-world data, confirming the practicality of the proposed approach.

Unmanned Aerial Vehicles (UAVS) are limited by the onboard energy. Refinement of the navigation strategy directly affects both the flight velocity and the trajectory based on the adjustment of key parameters in the UAVS pipeline, thus reducing energy consumption. However, existing techniques tend to adopt static and conservative strategies in dynamic scenarios, leading to inefficient energy reduction. Dynamically adjusting the navigation strategy requires overcoming the challenges including the task pipeline interdependencies, the environmental-strategy correlations, and the selecting parameters. To solve the aforementioned problems, this paper proposes a method to dynamically adjust the navigation strategy of the UAVS by analyzing its dynamic characteristics and the temporal characteristics of the autonomous navigation pipeline, thereby reducing UAVS energy consumption in response to environmental changes. We compare our method with the baseline through hardware-in-the-loop (HIL) simulation and real-world experiments, showing our method 3.2X and 2.6X improvements in mission time, 2.4X and 1.6X improvements in energy, respectively.