Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The FBI on Wednesday shared a wanted poster for Chinese national Baoxia "Emily" Liu, adding that the State Department is offering a reward of up to 15 million for information on her and others accused of smuggling U.S. drone weapons to Iran. Liu and three other fellow Chinese nationals were charged by President Joe Biden's Justice Department in January 2024 in an alleged years-long conspiracy in which they unlawfully exported and smuggled U.S. export-controlled items through China and Hong Kong to entities affiliated with Iran's Islamic Revolutionary Guard Corps (IRGC) and Ministry of Defense and Armed Forces Logistics (MODAFL), which supervises production of Tehran's missiles, weapons, and Unmanned Aerial Vehicles (UAVs). Her co-defendants are Li Yongxin, also known as "Emma Lee;" Yung Yiu Wa, also known as "Stephen Yung;" and Zhong Yanlai, also known as Sydney Chung.

Russia and Ukraine have exchanged 372 prisoners of war in a swap brokered by the United Arab Emirates, as US President Donald Trump said he held a "very good" phone call with Ukrainian President Volodymyr Zelenskyy. The Russian Defence Ministry announced on Wednesday that Moscow returned 175 soldiers and "22 seriously wounded prisoners of war in need of urgent medical assistance" in what it said was a "gesture of goodwill." It said that Kyiv returned 175 Russian troops. Zelenskyy confirmed the swap and wrote on X that it was "one of the largest" exchanges since the beginning of Russia's full-scale invasion of Ukraine in February 2022. "I thank our team for their important work in finding Ukrainian prisoners of war and facilitating exchanges, as well as for the results that bring hope. We are also grateful to all our partners, especially the United Arab Emirates, for making today's exchange possible," the Ukrainian leader added.

The past decade has witnessed remarkable advancements in robotics and AI technologies, ushering in the era of autonomous machines. In this new age, service robots, autonomous drones, delivery robots, self-driving vehicles and other autonomous machines are poised to replace humans in providing various services.5 While the rise of autonomous machines promises to revolutionize our economy, the reality has fallen short of expectations despite over a decade of intensive R&D investments. The current development paradigm, dubbed Autonomy 1.0, scales mainly with the size of engineering team rather than with the amount of relevant data or computational resources. This limitation prevents the autonomy industry from fully leveraging economies of scale, particularly the exponentially decreasing cost of computing power and the explosion of available data.

While the US and Russia praised a phone call between Trump and Putin on Tuesday over a potential ceasefire in Ukraine, Kyiv fears it may be forced into a deal it does not like. Assed Baig reports from Ukraine's capital which was the target of a Russian drone attack on Tuesday night.

Within hours of Putin agreeing to halt attacks on energy infrastructure, Zelensky said "there have been hits, specifically on civilian infrastructure", including a hospital in Sumy. Zelensky said that Russia launched more than 40 drones against Ukraine in the hours following the call between Trump and Putin. "It is these types of night-time attacks by Russia that destroy our energy sector, our infrastructure, and the normal life of Ukrainians," Zelensky said. "Today, Putin effectively rejected the proposal for a full ceasefire." Meanwhile, the Russian defence ministry said of the 57 Ukrainian drones it intercepted and destroyed overnight, 35 were over the Kursk border region.

Monitoring forest dynamics at an individual tree scale is essential for accurately assessing ecosystem responses to climate change, yet traditional methods relying on field-based forest inventories are labor-intensive and limited in spatial coverage. Advances in remote sensing using drone-acquired RGB imagery combined with deep learning models have promised precise individual tree crown (ITC) segmentation; however, existing methods are frequently validated against human-annotated images, lacking rigorous independent ground truth. In this study, we generate high-fidelity validation labels from co-located Terrestrial Laser Scanning (TLS) data for drone imagery of mixed unmanaged boreal and Mediterranean forests. We evaluate the performance of two widely used deep learning ITC segmentation models - DeepForest (RetinaNet) and Detectree2 (Mask R-CNN) - on these data, and compare to performance on further Mediterranean forest data labelled manually. When validated against TLS-derived ground truth from Mediterranean forests, model performance decreased significantly compared to assessment based on hand-labelled from an ecologically similar site (AP50: 0.094 vs. 0.670). Restricting evaluation to only canopy trees shrank this gap considerably (Canopy AP50: 0.365), although performance was still far lower than on similar hand-labelled data. Models also performed poorly on boreal forest data (AP50: 0.142), although again increasing when evaluated on canopy trees only (Canopy AP50: 0.308). Both models showed very poor localisation accuracy at stricter IoU thresholds, even when restricted to canopy trees (Max AP75: 0.051). Similar results have been observed in studies using aerial LiDAR data, suggesting fundamental limitations in aerial-based segmentation approaches in closed canopy forests.

Agile trajectory planning can improve the efficiency of multi-rotor Uncrewed Aerial Vehicles (UAVs) in scenarios with combined task-oriented and kinematic trajectory planning, such as monitoring spatio-temporal phenomena or intercepting dynamic targets. Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly computationally demanding. That reduces the prediction horizon length, leading to a decrease in solution quality. Besides, the fixed time-step length limits the utilization of the available UAV dynamics in the target neighborhood. In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length. A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the generation of feasible trajectories in the flat output space. Based on the presented evaluation results and experimentally validated deployment, the proposed method increases the solution quality by enabling planning for long flight segments but allowing tightly sampled maneuvering.

ViVa-SAFELAND is an open source software library, aimed to test and evaluate vision-based navigation strategies for aerial vehicles, with special interest in autonomous landing, while complying with legal regulations and people's safety. It consists of a collection of high definition aerial videos, focusing on real unstructured urban scenarios, recording moving obstacles of interest, such as cars and people. Then, an Emulated Aerial Vehicle (EAV) with a virtual moving camera is implemented in order to ``navigate" inside the video, according to high-order commands. ViVa-SAFELAND provides a new, safe, simple and fair comparison baseline to evaluate and compare different visual navigation solutions under the same conditions, and to randomize variables along several trials. It also facilitates the development of autonomous landing and navigation strategies, as well as the generation of image datasets for different training tasks. Moreover, it is useful for training either human of autonomous pilots using deep learning. The effectiveness of the framework for validating vision algorithms is demonstrated through two case studies, detection of moving objects and risk assessment segmentation. To our knowledge, this is the first safe validation framework of its kind, to test and compare visual navigation solution for aerial vehicles, which is a crucial aspect for urban deployment in complex real scenarios.

Obstacle avoidance for unmanned aerial vehicles like quadrotors is a popular research topic. Most existing research focuses only on static environments, and obstacle avoidance in environments with multiple dynamic obstacles remains challenging. This paper proposes a novel deep-reinforcement learning-based approach for the quadrotors to navigate through highly dynamic environments. We propose a lidar data encoder to extract obstacle information from the massive point cloud data from the lidar. Multi frames of historical scans will be compressed into a 2-dimension obstacle map while maintaining the obstacle features required. An end-to-end deep neural network is trained to extract the kinematics of dynamic and static obstacles from the obstacle map, and it will generate acceleration commands to the quadrotor to control it to avoid these obstacles. Our approach contains perception and navigating functions in a single neural network, which can change from a navigating state into a hovering state without mode switching. We also present simulations and real-world experiments to show the effectiveness of our approach while navigating in highly dynamic cluttered environments.

With the rapid development of artificial intelligence, intelligent decision-making techniques have gradually surpassed human levels in various human-machine competitions, especially in complex multi-agent cooperative task scenarios. Multi-agent cooperative decision-making involves multiple agents working together to complete established tasks and achieve specific objectives. These techniques are widely applicable in real-world scenarios such as autonomous driving, drone navigation, disaster rescue, and simulated military confrontations. This paper begins with a comprehensive survey of the leading simulation environments and platforms used for multi-agent cooperative decision-making. Specifically, we provide an in-depth analysis for these simulation environments from various perspectives, including task formats, reward allocation, and the underlying technologies employed. Subsequently, we provide a comprehensive overview of the mainstream intelligent decision-making approaches, algorithms and models for multi-agent systems (MAS). Theseapproaches can be broadly categorized into five types: rule-based (primarily fuzzy logic), game theory-based, evolutionary algorithms-based, deep multi-agent reinforcement learning (MARL)-based, and large language models(LLMs)reasoning-based. Given the significant advantages of MARL andLLMs-baseddecision-making methods over the traditional rule, game theory, and evolutionary algorithms, this paper focuses on these multi-agent methods utilizing MARL and LLMs-based techniques. We provide an in-depth discussion of these approaches, highlighting their methodology taxonomies, advantages, and drawbacks. Further, several prominent research directions in the future and potential challenges of multi-agent cooperative decision-making are also detailed.