FOX News' Jennifer Griffin reports the latest on the Israel-Hamas war. Sirens are sounding off in northern Israel on Wednesday and residents are being told to shelter in place amid reports of an incoming "large-scale" drone attack. Israeli Defense Forces said it received a report of a "suspected infiltration" from Lebanon into Israeli airspace. "All residents in the areas where the warning was sounded are asked to enter the protected areas and stay in them until further notice," IDF said. "Israelis throughout the country were ordered to shelter in place amid a large-scale attack involving drones coming from the direction of the northern border on Wednesday evening," the Jerusalem Post reported.

In this paper, we present an approach for coverage path planning for a team of an energy-constrained Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). Both the UAV and the UGV have predefined areas that they have to cover. The goal is to perform complete coverage by both robots while minimizing the coverage time. The UGV can also serve as a mobile recharging station. The UAV and UGV need to occasionally rendezvous for recharging. We propose a heuristic method to address this NP-Hard planning problem. Our approach involves initially determining coverage paths without factoring in energy constraints. Subsequently, we cluster segments of these paths and employ graph matching to assign UAV clusters to UGV clusters for efficient recharging management. We perform numerical analysis on real-world coverage applications and show that compared with a greedy approach our method reduces rendezvous overhead on average by 11.33\%. We demonstrate proof-of-concept with a team of a VOXL m500 drone and a Clearpath Jackal ground vehicle, providing a complete system from the offline algorithm to the field execution.

Russia launched 36 drone attacks overnight on Ukraine, according to Kyiv's air force, in Moscow's latest air raid targeting the country. Ukraine's air force said in a statement on Tuesday that its defence systems had destroyed 27 of the drones. The attacks using Iran-made Shahed drones targeted the Odesa, Mykolaiv and Kherson regions of Ukraine, the air force said on the Telegram messaging app. Moscow launched a total of 36 Iranian-made drones from the Russia-annexed Crimean peninsula, it added. The air force did not say which targets, if any, the nine other drones may have hit.

Air Force spokesperson Yuriy Ihnat said that Ukraine was expecting a record number of Russian drone attacks this winter. Ihnat told national television that data Russia had already used a "record' number of more than 500 Iranian-made Shahed drones in September, compared with about 1,000 over a six-month period during last winter. Governor Oleksandr Prokudin said the southern Ukrainian region of Kherson had "another terrible night" as it was targeted in some 59 Russian attacks that left 12 people injured, including a mother and her nine-month-old baby. Several houses and gas pipelines were also damaged. Four people including a nine-year-old girl were injured in a rocket attack on Konstantinivka, according to the Donetsk regional Governor Ihor Moroz. Several homes and other buildings were also damaged. The General Staff of Ukraine's Armed Forces said the situation on the battlefield in the east and south of the country remained difficult, with troops coming under intense artillery and mortar fire in and around the front line in areas including Bakhmut, Kupiansk and Lyman. The General Staff said Ukrainian forces had inflicted casualties and equipment losses on the Russians. Air Force spokesperson Yuriy Ihnat said that Ukraine was expecting a record number of Russian drone attacks this winter. Ihnat told national television that data Russia had already used a "record' number of more than 500 Iranian-made Shahed drones in September, compared with about 1,000 over a six-month period during last winter.

Precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms like Autonomous Surface Vehicles (ASVs) is both important and challenging, especially in GPS-denied environments, for collaborative navigation of heterogeneous vehicles. UAVs need to land within a confined space onboard ASV to get energy replenishment, while ASV is subject to translational and rotational disturbances due to wind and water flow. Current solutions either rely on high-level waypoint navigation, which struggles to robustly land on varied-speed targets, or necessitate laborious manual tuning of controller parameters, and expensive sensors for target localization. Therefore, we propose an adaptive velocity control algorithm that leverages Particle Swarm Optimization (PSO) and Neural Network (NN) to optimize PID parameters across varying flight altitudes and distinct speeds of a moving boat. The cost function of PSO includes the status change rates of UAV and proximity to the target. The NN further interpolates the PSO-founded PID parameters. The proposed method implemented on a water strider hexacopter design, not only ensures accuracy but also increases robustness. Moreover, this NN-PSO can be readily adapted to suit various mission requirements. Its ability to achieve precise landings extends its applicability to scenarios, including but not limited to rescue missions, package deliveries, and workspace inspections.

In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with automatically labeled images. Finally, we evaluate the performance of different neural networks.

Reinforcement learning generates policies based on reward functions and hyperparameters. Slight changes in these can significantly affect results. The lack of documentation and reproducibility in Reinforcement learning research makes it difficult to replicate once-deduced strategies. While previous research has identified strategies using grounded maneuvers, there is limited work in more complex environments. The agents in this study are simulated similarly to Open Al's hider and seek agents, in addition to a flying mechanism, enhancing their mobility, and expanding their range of possible actions and strategies. This added functionality improves the Hider agents to develop a chasing strategy from approximately 2 million steps to 1.6 million steps and hiders

Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its potential negative impacts on aspects such as job security, safety, and privacy. Nevertheless, this did not receive sufficient attention, particularly within the academic community. This research systematically reviews AI and robotics research through the lens of ethics in the AEC community for the past five years. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability, by summarizing existing literature and filtering it further based on its AEC relevance. Furthermore, thirteen research topics along the process were identified based on existing AEC studies that had direct relevance to the theme of ethics in general and their parallels are further discussed. Finally, the current challenges and knowledge gaps are discussed and seven specific future research directions are recommended. This study not only signifies more stakeholder awareness of this important topic but also provides imminent steps towards safer and more efficient realization.

Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms. Multi-agent reinforcement learning (MARL) has emerged as a solution, but requires extensive and costly real-world training data. To tackle this challenge, we propose a novel model-aided federated MARL algorithm to coordinate multiple UAVs on a data harvesting mission with only limited knowledge about the environment. The proposed algorithm alternates between building an environment simulation model from real-world measurements, specifically learning the radio channel characteristics and estimating unknown IoT device positions, and federated QMIX training in the simulated environment. Each UAV agent trains a local QMIX model in its simulated environment and continuously consolidates it through federated learning with other agents, accelerating the learning process. A performance comparison with standard MARL algorithms demonstrates that our proposed model-aided FedQMIX algorithm reduces the need for real-world training experiences by around three magnitudes while attaining similar data collection performance.

An American F-16 fighter jet shot down a Turkish military drone on Thursday that entered a restricted zone in northeastern Syria and came within about 550 yards of U.S. ground forces, according to Pentagon officials. No American troops were harmed in the incident, U.S. officials said. "Turkey is one of our strongest and most valued NATO allies, and that partnership continues and will continue," Brig. Gen. Patrick S. Ryder, the Pentagon spokesman, told reporters. "So this is certainly a regrettable incident."