This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)'s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated trajectories for a multirotor UAV to precisely land on the deck of a moving USV even in cases where the deck's inclination is continuously changing. We use an MPC-based scheme to create trajectories that consider both the UAV dynamics and the predicted states of the USV up to the first derivative of position and orientation. Compared to existing approaches, our method dynamically modifies the penalization matrices to precisely follow the corresponding states with respect to the flight phase. Especially during the landing maneuver, the UAV synchronizes attitude with the USV's, allowing for fast landing on a tilted deck. Simulations show the method's reliability in various sea conditions up to Rough sea (wave height 4 m), outperforming state-of-the-art methods in landing speed and accuracy, with twice the precision on average. Finally, real-world experiments validate the simulation results, demonstrating robust landings on a moving USV, while all computations are performed in real-time onboard the UAV.

The increasing rates of global urbanization and vehicle usage are leading to a shift of mobility to the third dimension-through Advanced Air Mobility (AAM)-offering a promising solution for faster, safer, cleaner, and more efficient transportation. As air transportation continues to evolve with more automated and autonomous systems, advancements in AAM require a deep understanding of human-computer interaction and human-AI collaboration to ensure safe and effective operations in complex urban and regional environments. There has been a significant increase in publications regarding these emerging applications; thus, there is a need to review developments in this area. This paper comprehensively reviews the current state of research on human-computer interaction and human-AI collaboration in AAM. Specifically, we focus on AAM applications related to the design of human-machine interfaces for various uses, including pilot training, air traffic management, and the integration of AI-assisted decision-making systems with immersive technologies such as extended, virtual, mixed, and augmented reality devices. Additionally, we provide a comprehensive analysis of the challenges AAM encounters in integrating human-computer frameworks, including unique challenges associated with these interactions, such as trust in AI systems and safety concerns. Finally, we highlight emerging opportunities and propose future research directions to bridge the gap between human factors and technological advancements in AAM.

Federated learning (FL) has emerged as a pivotal solution for training machine learning models over wireless networks, particularly for Internet of Things (IoT) devices with limited computation resources. Despite its benefits, the efficiency of FL is often restricted by the communication quality between IoT devices and the central server. To address this issue, we introduce an innovative approach by deploying an unmanned aerial vehicle (UAV) as a mobile FL server to enhance the training process of FL. By leveraging the UAV's maneuverability, we establish robust line-of-sight connections with IoT devices, significantly improving communication capacity. To improve the overall training efficiency, we formulate a latency minimization problem by jointly optimizing the bandwidth allocation, computing frequencies, transmit power for both the UAV and IoT devices, and the UAV's trajectory. Then, an efficient alternating optimization algorithm is developed to solve it efficiently. Furthermore, we analyze the convergence and computational complexity of the proposed algorithm. Finally, numerical results demonstrate that our proposed scheme not only outperforms existing benchmark schemes in terms of latency but also achieves training efficiency that closely approximate the ideal scenario.

As waves of loud, car-sized mystery drones continue to buzz over New Jersey, one family reported that the craft changed time on their car's clock. The family of Morris County locals said they were following one of these seemingly terrestrial UFOs in their vehicle, only to experience the odd effect on their car's electronics as the unexplained craft'hovered above them.' 'The clock in their car changed time,' according to one Fox News reporter who spoke to the unnamed family. 'They say the clock went back to normal after they drove off.' While local law enforcement in Morris County has issued a statement asserting that'there is no known threat to public safety' at this time -- the Federal Aviation Administration (FAA) has issued a ban on drone flights over sensitive areas in state.

Fox News' Stephanie Bennett reports the latest on the unidentified drones from London. The Pentagon unveiled a new counter-drone strategy after a spate of incursions near U.S. bases prompted concerns over a lack of an action plan for the increasing threat of unmanned aerial vehicles. Though much of the strategy remains classified, Defense Secretary Lloyd Austin will implement a new counter-drone office within the Pentagon – Joint Counter-Small UAS Office – and a new Warfighter Senior Integration Group, according to a new memo. The Pentagon will also begin work on a second Replicator initiative, but it will be up to the incoming Trump administration to decide whether to fund this plan. The first Replicator initiative worked to field inexpensive, dispensable drones to thwart drone attacks by adversarial groups across the Middle East and elsewhere.

Yemen's Houthi group says it has carried out a drone attack in central Israel's Tel Aviv area in "a specific military operation" in support of Palestinians in Gaza. The Houthis said in a statement on Monday that their forces struck "a sensitive target of the Israeli enemy". An Israeli military statement said a drone hit a building in the city of Yavne after air defence systems failed to detect it and an investigation into the failure is under way. The Houthis said the operation "achieved its objective" without providing details. No injuries were reported in the attack, which caused damage to several apartments in the building, according to Israeli media reports.

At least 22 Palestinians, including women and children, have been killed after Israel launched air and drone attacks across Gaza, while a power outage threatens the lives of more than 100 patients at a hospital in the besieged territory's north. In the latest Israeli attack in the Jabalia refugee camp in northern Gaza on Monday morning, three people were targeted with a missile launched from a drone, instantly killing them, sources told Al Jazeera. "[The victims] were trying to leave their home in search of food in the vicinity of their neighbourhood when they were targeted by a drone," said Al Jazeera's Hani Mahmoud, reporting from central Deir el-Balah in Gaza. "They were killed right away. Their bodies are still in the street and nobody has the ability to get to the bombed site and remove the bodies from the street."

Russian air defence units destroyed 13 Ukrainian drones over three western Russia regions, the Russian Defence Ministry said on the Telegram messaging app. Ukraine's air force said the country's air defence shot down two missiles and 18 drones launched by Russia overnight. Russian forces have taken control of the settlement of Blahodatne in eastern Ukraine, Russia's RIA state news agency reported, citing the Defence Ministry. Ukrainian President Volodymyr Zelenskyy said 43,000 Ukrainian soldiers have been killed since the beginning of the Russian invasion in February 2022. During the same period, an estimated 370,000 soldiers were injured, he added in a post on X. Russian air defence units destroyed 13 Ukrainian drones over three western Russia regions, the Russian Defence Ministry said on the Telegram messaging app.

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor-Critic architecture to provide the agent with privileged information during training, which enhances the model's perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against the state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate.

This paper proposes a data-driven model predictive control for multirotor collision avoidance considering uncertainty and an unknown model from a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is used to obtain the governing equation of the multirotor system. The SINDy can discover the equations of target systems with low data, assuming that few functions have the dominant characteristic of the system. Model predictive control (MPC) is utilized to obtain accurate trajectory tracking performance by considering state and control input constraints. To avoid a collision during operation, MPC optimization problem is again formulated using inequality constraints about an obstacle. In simulation, SINDy can discover a governing equation of multirotor system including mass parameter uncertainty and aerodynamic effects. In addition, the simulation results show that the proposed method has the capability to avoid an obstacle and track the desired trajectory accurately.