Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russia launched a massive drone attack on the western city of Lviv early Tuesday, burning down a warehouse said to house humanitarian supplies and killing one man, Ukrainian authorities said. It was one of at least three deadly attacks in different cities. Ukraine intercepted most of the 30 Shahed drones overnight, the country's air force said.

The elite forces were focused primarily on Islamic State fighters in recent years, but other Islamic militant groups now also move through Iraq's porous borders with Iran and Turkey. The Iraqi government announced early on Tuesday that the drone was launched by Turkey and called on Ankara to halt such attacks. "This aggression constitutes a violation of Iraq's sovereignty, security and territorial integrity," said Maj. Gen. Yahya Rasoul Abdullah, military spokesman for the prime minister, "These repeated attacks are not consistent with the principle of good neighborly relations between countries, and threaten to undermine Iraq's efforts to build good and balanced political, economic, and security relations with its neighbors." Kurdish leaders, whose territory is routinely targeted by Turkey, used angrier rhetoric. "This criminal act is an open trespassing of the border of the Kurdistan Region and of Iraq, and it is part of the conspiracy aimed at disturbing the peace and stability of the Kurdistan Region," said Bafel Talabani, president of the Patriotic Union of Kurdistan, one of the two main political parties in Kurdistan and the dominant one in Sulaymaniyah.

The western Ukrainian city of Lviv was rocked by early morning explosions as air defences engaged in stopping waves of Russian air attacks, local officials said. The city's mayor, Andriy Sadoviy, wrote on the Telegram messaging app early on Tuesday that "air defences are operating in our region", and warned residents to seek shelter. Ukraine's Air Force wrote on Telegram that "the threat of Shahed [Iranian-made drones] remains in the Lviv region. Western Ukraine came under an air raid alert starting at about 00:00 GMT. According to reports, several waves of drones attempted to hit targets in Lviv city starting at about 01:30 GMT with numerous explosions heard as air defences intercepted the incoming unmanned aerial vehicles. Sadoviy later reported that a fire at an industrial warehouse had broken out and emergency services were tackling the blaze. Initial reports were of one person injured, he said. "So far, only one person has been injured in the attack," the mayor said on Telegram, adding that a man in his mid-20s was found under the rubble of a warehouse and transported to hospital. The scale of the attack and full extent of the damage were not immediately known. There was no immediate comment from Russia. The head of the Lviv region's military administration, Maksym Kozytskyi, said that two people, a man and a woman, were rescued from beneath rubble in the city. "The woman is initially believed to have no injuries.

The Australian government has said it will buy a new drone and upgrade existing patrol and response aircraft in a 1.5 billion Australian dollar ($964.88m) The military will buy a fourth MQ-4C Triton drone and upgrade the air force's existing fleet of 14 P-8A Poseidon maritime patrol aircraft, Pat Conroy, minister for defence industry, said in a statement on Tuesday. The Triton will be delivered in 2024 and be based in northern Australia. The aircraft upgrades will provide enhancements to anti-submarine warfare, maritime strike and intelligence collection capabilities, the statement said. The first Poseidon will enter the upgrade programme in 2026, with the final aircraft to be completed in 2030.

Autonomous flight in unknown environments requires precise planning for both the spatial and temporal profiles of trajectories, which generally involves nonconvex optimization, leading to high time costs and susceptibility to local optima. To address these limitations, we introduce the Learning-Initialized Trajectory Planner (LIT-Planner), a novel approach that guides optimization using a Neural Network (NN) Planner to provide initial values. We first leverage the spatial-temporal optimization with batch sampling to generate training cases, aiming to capture multimodality in trajectories. Based on these data, the NN-Planner maps visual and inertial observations to trajectory parameters for handling unknown environments. The network outputs are then optimized to enhance both reliability and explainability, ensuring robust performance. Furthermore, we propose a framework that supports robust online replanning with tolerance to planning latency. Comprehensive simulations validate the LIT-Planner's time efficiency without compromising trajectory quality compared to optimization-based methods. Real-world experiments further demonstrate its practical suitability for autonomous drone navigation.

At least six people have been killed in a suspected drone attack on an airport near the city of Sulaymaniyah in the semi-autonomous Kurdish region in northern Iraq, official sources have told Al Jazeera. Al Jazeera's Mahmoud Abdelwahed, reporting from the Iraqi capital Baghdad, said that the Arbat airport, located 50km (30 miles) to the east of Sulaimaniya, has been used by the "anti-terrorism" combat apparatus that is part of Sulaymaniyah security forces. "Whether all the victims are from the anti-terrorism apparatus remains to be known," he said. The airport was used for agricultural purposes in the past. Two members of the Kurdish security forces were wounded in the attack and were rushed to a military hospital in Sulaimaniya under tight security, a police source told Reuters.

Firefighters came to the rescue of delivery robots that found their path blocked by crews tackling a building blaze. The robots, now common in parts of Cambridge, are used by the Co-op in the city for customers to order home or workplace deliveries. But they found their path blocked by a fire engine and hoses in the city on Saturday night. Posting on X, Cambridgeshire Fire and Rescue said: "Sorry @coopuk our hoses and fire engines confused your delivery robots in Cambridge this evening as we tackled a building fire, but firefighters helped them on their way - hopefully not too many delays!" The supermarket thanked the crew for its assistance and posted it was " glad to hear they were able to help".

Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). However, this was not the case with security testing. Performing security testing in a simulator is very difficult because security attacks are performed on a different abstraction level. In addition, the attacks themselves are becoming more sophisticated, which directly contributes to the difficulty of executing them in a simulator. In this paper, we attempt to tackle the aforementioned gap by investigating possible attacks that can be simulated, and then performing their simulations. The presented approach shows that attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. This is achieved by exploiting vulnerabilities of the ROS and MAVLink protocol and injecting malicious processes into an application. As a result, messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle. This was tested in multiple scenarios, thereby proving that it is indeed possible to simulate certain attack types, such as spoofing and jamming.

For drones, as safety-critical systems, there is an increasing need for onboard detect & avoid (DAA) technology i) to see, sense or detect conflicting traffic or imminent non-cooperative threats due to their high mobility with multiple degrees of freedom and the complexity of deployed unstructured environments, and subsequently ii) to take the appropriate actions to avoid collisions depending upon the level of autonomy. The safe and efficient integration of UAV traffic management (UTM) systems with air traffic management (ATM) systems, using intelligent autonomous approaches, is an emerging requirement where the number of diverse UAV applications is increasing on a large scale in dense air traffic environments for completing swarms of multiple complex missions flexibly and simultaneously. Significant progress over the past few years has been made in detecting UAVs present in aerospace, identifying them, and determining their existing flight path. This study makes greater use of electronic conspicuity (EC) information made available by PilotAware Ltd in developing an advanced collision management methodology -- Drone Aware Collision Management (DACM) -- capable of determining and executing a variety of time-optimal evasive collision avoidance (CA) manoeuvres using a reactive geometric conflict detection and resolution (CDR) technique. The merits of the DACM methodology have been demonstrated through extensive simulations and real-world field tests in avoiding mid-air collisions (MAC) between UAVs and manned aeroplanes. The results show that the proposed methodology can be employed successfully in avoiding collisions while limiting the deviation from the original trajectory in highly dynamic aerospace without requiring sophisticated sensors and prior training.

Unmanned aerial vehicles (UAVs) are capable of surveying expansive areas, but their operational range is constrained by limited battery capacity. The deployment of mobile recharging stations using unmanned ground vehicles (UGVs) significantly extends the endurance and effectiveness of UAVs. However, optimizing the routes of both UAVs and UGVs, known as the UAV-UGV cooperative routing problem, poses substantial challenges, particularly with respect to the selection of recharging locations. Here in this paper, we leverage reinforcement learning (RL) for the purpose of identifying optimal recharging locations while employing constraint programming to determine cooperative routes for the UAV and UGV. Our proposed framework is then benchmarked against a baseline solution that employs Genetic Algorithms (GA) to select rendezvous points. Our findings reveal that RL surpasses GA in terms of reducing overall mission time, minimizing UAV-UGV idle time, and mitigating energy consumption for both the UAV and UGV. These results underscore the efficacy of incorporating heuristics to assist RL, a method we refer to as heuristics-assisted RL, in generating high-quality solutions for intricate routing problems.