Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Canada Soccer has parted ways with Bev Priestman. The decision to relieve Priestman of her coaching duties comes after an independent review was launched into her role in a drone surveillance scandal at this past summer's Olympics in Paris. Assistant coach Jasmine Mander and analyst Joseph Lombardi were also relieved of duty as Canada Soccer released the findings of the investigation.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Canada Soccer has parted ways with Bev Priestman. The decision to relieve Priestman of her coaching duties comes after an independent review was launche into her role in a drone surveillance scandal at this past summer's Olympics in Paris. Assistant coach Jasmine Mander and analyst Joseph Lombardi were also relieved of duty as Canada Soccer released the findings of the investigation.

US warships came under sustained missile and drone attack from Houthi fighters as they sailed off the coast of Yemen, the Pentagon has confirmed, with the armed group claiming it attacked the US aircraft carrier Abraham Lincoln and two US destroyers. Pentagon spokesperson Air Force Major General Patrick Ryder said on Tuesday that the United States military's Central Command (CENTCOM) forces "successfully repelled multiple Iranian backed Houthi attacks during a transit of the Bab al-Mandeb strait", which connects the Red Sea to the Gulf of Aden. Ryder told reporters at a news conference that two US-guided missile destroyers – the USS Stockdale and USS Spruance – were attacked by at least eight one-way attack drones, five antiship ballistic missiles and three antiship cruise missiles. All the Houthi drones and missiles "were successfully engaged and defeated", and neither of the US Navy ships were damaged or personnel hurt, he said. Ryder added that he was not aware of any attacks against the aircraft carrier USS Abraham Lincoln.

Russia is launching mass drone strikes on Ukraine. A 14-year-old girl was one of the latest victims.

Drone detection has benefited from improvements in deep neural networks, but like many other applications, suffers from the availability of accurate data for training. Synthetic data provides a potential for low-cost data generation and has been shown to improve data availability and quality. However, models trained on synthetic datasets need to prove their ability to perform on real-world data, known as the problem of sim-to-real transferability. Here, we present a drone detection Faster-RCNN model trained on a purely synthetic dataset that transfers to real-world data. We found that it achieves an AP_50 of 97.0% when evaluated on the MAV-Vid - a real dataset of flying drones - compared with 97.8% for an equivalent model trained on real-world data. Our results show that using synthetic data for drone detection has the potential to reduce data collection costs and improve labelling quality. These findings could be a starting point for more elaborate synthetic drone datasets. For example, realistic recreations of specific scenarios could de-risk the dataset generation of safety-critical applications such as the detection of drones at airports. Further, synthetic data may enable reliable drone detection systems, which could benefit other areas, such as unmanned traffic management systems. The code is available https://github.com/mazqtpopx/cranfield-synthetic-drone-detection alongside the datasets https://huggingface.co/datasets/mazqtpopx/cranfield-synthetic-drone-detection.

Vision-and-Language Navigation (VLN), as a widely discussed research direction in embodied intelligence, aims to enable embodied agents to navigate in complicated visual environments through natural language commands. Most existing VLN methods focus on indoor ground robot scenarios. However, when applied to UAV VLN in outdoor urban scenes, it faces two significant challenges. First, urban scenes contain numerous objects, which makes it challenging to match fine-grained landmarks in images with complex textual descriptions of these landmarks. Second, overall environmental information encompasses multiple modal dimensions, and the diversity of representations significantly increases the complexity of the encoding process. To address these challenges, we propose NavAgent, the first urban UAV embodied navigation model driven by a large Vision-Language Model. NavAgent undertakes navigation tasks by synthesizing multi-scale environmental information, including topological maps (global), panoramas (medium), and fine-grained landmarks (local). Specifically, we utilize GLIP to build a visual recognizer for landmark capable of identifying and linguisticizing fine-grained landmarks. Subsequently, we develop dynamically growing scene topology map that integrate environmental information and employ Graph Convolutional Networks to encode global environmental data. In addition, to train the visual recognizer for landmark, we develop NavAgent-Landmark2K, the first fine-grained landmark dataset for real urban street scenes. In experiments conducted on the Touchdown and Map2seq datasets, NavAgent outperforms strong baseline models. The code and dataset will be released to the community to facilitate the exploration and development of outdoor VLN.

From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relative distance measurements between the agents and the targets can be obtained by using onboard sensors. Based on the measurements, the center of all the targets is estimated directly by a fuzzy wavelet neural network (FWNN) and the least squares fit method. Then, a new distributed anti-synchronization controller (DASC) is designed so that the two tasking agents are able to encircle all targets while staying opposite to each other. In particular, the radius of the desired encirclement trajectory can be dynamically determined to avoid potential collisions between the two agents and all targets. Based on the Lyapunov stability analysis method, the convergence proofs of the neural network prediction error, the target-center position estimation error, and the controller error are addressed respectively. Finally, both numerical simulations and UAV flight experiments are conducted to demonstrate the validity of the encirclement algorithms. The flight tests recorded video and other simulation results can be found in https://youtu.be/B8uTorBNrl4.

Moldova has handed a note of protest to the Russian ambassador to Chisinau over alleged interference in its recent elections. The foreign ministry in Chisinau said in a statement on Tuesday that it turned over the "note of firm protest" in relation to the "illegal and deliberate interference" to envoy Oleg Ozerov during a meeting at its offices. Moldova has accused Russia of seeking to influence its recent presidential election and referendum on joining the European Union. Russia sought to affect results and delegitimise the democratic process, the ministry complained. Chisinau accused Russia of organising ineligible voting, bribery, and security threats in a bid to influence the votes.

Amid a rising tide of low-cost weaponized adversary drones menacing American troops abroad, the US military is pulling out all the stops to protect its forces from the ever-present threat of death from above. But between expensive munitions, futuristic but complicated directed energy weapons, and its own growing drone arsenal, the Pentagon is increasingly eyeing an elegantly simple solution to its growing drone problem: reinventing the gun. At the Technology Readiness Experimentation (T-REX) event in August, the US Defense Department tested an artificial intelligence-enabled autonomous robotic gun system developed by fledgling defense contractor Allen Control Systems dubbed the "Bullfrog." Consisting of a 7.62-mm M240 machine gun mounted on a specially designed rotating turret outfitted with an electro-optical sensor, proprietary AI, and computer vision software, the Bullfrog was designed to deliver small arms fire on drone targets with far more precision than the average US service member can achieve with a standard-issue weapon like the M4 carbine or next-generation XM7 rifle. Indeed, footage of the Bullfrog in action published by ACS shows the truck-mounted system locking onto small drones and knocking them out of the sky with just a few shots.

Search-based motion planning algorithms have been widely utilized for unmanned aerial vehicles (UAVs). However, deploying these algorithms on real UAVs faces challenges due to limited onboard computational resources. The algorithms struggle to find solutions in high-dimensional search spaces and require considerable time to ensure that the trajectories are dynamically feasible. This paper incorporates the lazy search concept into search-based planning algorithms to address the critical issue of real-time planning for collision-free and dynamically feasible trajectories on UAVs. We demonstrate that the lazy search motion planning algorithm can efficiently find optimal trajectories and significantly improve computational efficiency.