Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Sirens wailed across Russia and TV stations interrupted regular programming to broadcast warnings Wednesday as part of sweeping drills intended to test the readiness of the country's emergency responders amid the fighting in Ukraine. The exercise that started Tuesday follows Ukrainian drone attacks on Moscow and other cities. As the readiness drill went on, the Russian Defense Ministry said air defenses shot down 31 Ukrainian drones over border regions early Wednesday. The readiness of the public warning system is being tested!

White House correspondent Peter Doocy reports on how the White House push for Ukraine spending may be jeopardized after McCarthy ouster on'Special Report.' Russian military officials claim they fended off a massive Ukrainian drone attack overnight Wednesday, which would be the largest single cross-border drone assault reported by Moscow since it first invaded Ukraine 20 months ago. The Russian Defense Ministry said Wednesday its national air defenses shot down 31 Ukrainian drones launched by Kyiv's forces on border regions, but there were no immediate reports of any damage or casualties. The Russian Defense Ministry didn't provide any evidence for its claims about intercepting Ukrainian drones nor any details about any damage or casualties. Moscow also said Russian aircraft thwarted a Ukrainian attempt to deploy soldiers on Russian-annexed Crimea.

Russian and Ukrainian forces remained largely static on the battlefield in the 84th week of the war after a month of vigorous Ukrainian advances that saw Kyiv break through the first of three Russian lines of defence on the southern front. Still, Ukraine proved it could hold onto its gains against Russian counterattacks and even made a few advances. Russian forces appeared to have lost a kilometre-long (0.6-mile-long) trench west of Verbove, a village on the front line of the main Ukrainian thrust through Russian defences in the Zaporizhia region in southeastern Ukraine. Vladimir Rogov, an occupation official, said at least four companies of Ukrainian troops had launched an attack on the trench on September 26 supported by armoured fighting vehicles. Geolocated footage that Russian sources released the following day confirmed that Ukraine held the position it had stormed.

The governor of Russia's Bryansk region accused Ukraine of using cluster munitions against a Russian village near the Ukrainian border. Several houses in the village of Klimovo were damaged, although no casualties were reported. The Ukrainian Air Force said it destroyed 29 of 31 drones and one cruise missile launched by Russia, mostly towards the regions of Mykolaiv and Dnipropetrovsk, during overnight attacks that lasted more than three hours. Falling debris from destroyed Russian drones caused fires in Dnipro and in an industrial enterprise in Pavlograd, two cities in Ukraine's eastern Dnipropetrovsk region. Firefighters managed to extinguish both fires and there were not initial reports regarding victims.

Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of payloads by multiple drones is another important area of recent research. However, efficient coordination algorithms for collaborative transportation of many payloads by many drones remain to be considered. In this work, we formulate the collaborative transportation of payloads by a swarm of drones as a novel, under-capacitated generalization of vehicle routing problems (VRP), which may also be of separate interest. In contrast to standard VRP and capacitated VRP, we must additionally consider waiting times for payloads lifted cooperatively by multiple drones, and the corresponding coordination. Algorithmically, we provide a solution encoding that avoids deadlocks and formulate an appropriate alternating minimization scheme to solve the problem. On the hardware side, we integrate our algorithms with collision avoidance and drone controllers. The approach and the impact of the system integration are successfully verified empirically, both on a swarm of real nano-quadcopters and for large swarms in simulation. Overall, we provide a framework for collaborative transportation with aerial drone swarms, that uses only as many drones as necessary for the transportation of any single payload.

Senior foreign affairs correspondent Greg Palkot reports the latest. Russia on Tuesday is claiming that so far this year, more than 335,000 people have signed up to fight in its military and volunteer units, although a further deployment to Ukraine is not coming, a report says. Reuters, citing Russian state television, quoted Defense Minister Sergei Shoigu telling top generals that there are "no plans for an additional mobilization" and that "the armed forces have the necessary number of military personnel to conduct the special military operation" in Ukraine. "Since the start of the year, more than 335,000 people have entered military service under contract and in volunteer formations," Shoigu reportedly added. "In September alone, more than 50,000 citizens signed contracts."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Russian government said Tuesday it will be pursuing charges against high-ranking members of the Ukrainian military for "terrorist attacks." The country's Investigative Committee accused four individuals of terrorism in connection to drone strikes on Russian territory and regions of Ukraine currently being held by Russian invading forces. Russia's official statement named the following officials -- Main Directorate of Intelligence Chief Kyrylo Budanov, Ukrainian Air Force Commander Mykola Oleshchuk, Ukrainian Naval Forces Commander Oleksiy Neizhpapa and 383rd Unmanned Aviation Brigade Commander Serhiy Burdenyuk.

Due to the state trajectory-independent features of invariant Kalman filtering (InEKF), it has attracted widespread attention in the research community for its significantly improved state estimation accuracy and convergence under disturbance. In this paper, we formulate the full-source data fusion navigation problem for fixed-wing unmanned aerial vehicle (UAV) within a framework based on error state right-invariant extended Kalman filtering (ES-RIEKF) on Lie groups. We merge measurements from a multi-rate onboard sensor network on UAVs to achieve real-time estimation of pose, air flow angles, and wind speed. Detailed derivations are provided, and the algorithm's convergence and accuracy improvements over established methods like Error State EKF (ES-EKF) and Nonlinear Complementary Filter (NCF) are demonstrated using real-flight data from UAVs. Additionally, we introduce a semi-aerodynamic model fusion framework that relies solely on ground-measurable parameters. We design and train an Long Short Term Memory (LSTM) deep network to achieve drift-free prediction of the UAV's angle of attack (AOA) and side-slip angle (SA) using easily obtainable onboard data like control surface deflections, thereby significantly reducing dependency on GNSS or complicated aerodynamic model parameters. Further, we validate the algorithm's robust advantages under GNSS denied, where flight data shows that the maximum positioning error stays within 30 meters over a 130-second denial period. To the best of our knowledge, this study is the first to apply ES-RIEKF to full-source navigation applications for fixed-wing UAVs, aiming to provide engineering references for designers. Our implementations using MATLAB/Simulink will open source.

Unmanned aerial vehicles (UAV) showed great potential in improving the efficiency of parcel delivery applications in the coming smart cities era. Unfortunately, the trustworthy positioning and control algorithms of the UAV are significantly challenged in complex urban areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, for example, the system dynamics of the control can largely help with the positioning, this paper proposed a joint positioning and control method (JPCM) based on factor graph optimization (FGO), which combines sensors' measurements and control intention. In particular, the positioning measurements are formulated as the factors in the factor graph model, such as the positioning from the GNSS. The model predictive control (MPC) is also formulated as the additional factors in the factor graph model. By solving the factor graph contributed by both the positioning factor and the MPC-based factors, the complementariness of positioning and control can be fully explored. To guarantee reliable system dynamic parameters, we validate the effectiveness of the proposed method using a simulated quadrotor system which showed significantly improved trajectory following performance. To benefit the research community, we open-source our code and make it available at https://github.com/RoboticsPolyu/IPN_MPC.

Localization of networked nodes is an essential problem in emerging applications, including first-responder navigation, automated manufacturing lines, vehicular and drone navigation, asset navigation and tracking, Internet of Things and 5G communication networks. In this paper, we present Locate3D, a novel system for peer-to-peer node localization and orientation estimation in large networks. Unlike traditional range-only methods, Locate3D introduces angle-of-arrival (AoA) data as an added network topology constraint. The system solves three key challenges: it uses angles to reduce the number of measurements required by 4x and jointly use range and angle data for location estimation. We develop a spanning-tree approach for fast location updates, and to ensure the output graphs are rigid and uniquely realizable, even in occluded or weakly connected areas. Locate3D cuts down latency by up to 75% without compromising accuracy, surpassing standard range-only solutions. It has a 10.2 meters median localization error for large-scale networks (30,000 nodes, 15 anchors spread across 14km square) and 0.5 meters for small-scale networks (10 nodes).