At least seven people were hurt after a Russian attack on the northeastern town of Chuhuiv in the Kharkiv region with S-400 missiles. The attack damaged a kindergarten building and a private home, according to the regional police. Russian drones struck energy facilities in Ukraine cutting power to more than 500,000 people in the northern Sumy region, according to regional authorities. The attacks targeted the cities of Shostka and Konotop, northeast of Kyiv and near the Russian border.

Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computational modalities for feedback loops, model updates and decision-making, including autonomous ones. However, understanding autonomous decision-making often requires technical and domain-specific knowledge. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs, generating natural language explanations of the system's decision-making by leveraging domain-specific knowledge bases. A case study from smart agriculture is presented.

The Autonomy Economy represents a transformative phase in our society, driven by the integration of autonomous machines such as autonomous vehicles, delivery robots, drones, and more into the provision of goods and services. Central to this revolution is Autonomous Machine Computing (AMC), the computing technological backbone enabling these diverse autonomous systems. This article delves into AMC's critical role in fostering the Autonomy Economy. Originally confined to basic robotics and industrial applications, these autonomous machines now permeate everyday life, signaling a move towards the Autonomy Economy era. For example, in China, when you check into a hotel, it's likely that a delivery robot is going to bring what you need to your room.

Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their search paths is crucial. This paper explores the use of deep reinforcement learning to create efficient search missions for drones in wilderness environments. Our approach leverages a priori data about the search area and the missing person in the form of a probability distribution map. This allows the deep reinforcement learning agent to learn optimal flight paths that maximize the probability of finding the missing person quickly. Experimental results show that our method achieves a significant improvement in search times compared to traditional coverage planning and search planning algorithms. In one comparison, deep reinforcement learning is found to outperform other algorithms by over $160\%$, a difference that can mean life or death in real-world search operations. Additionally, unlike previous work, our approach incorporates a continuous action space enabled by cubature, allowing for more nuanced flight patterns.

A unique approach for the mid-air autonomous aerial interception of non-cooperating UAV by a flying robot equipped with a net is presented in this paper. A novel interception guidance method dubbed EPN is proposed, designed to catch agile maneuvering targets while relying on onboard state estimation and tracking. The proposed method is compared with state-of-the-art approaches in simulations using 100 different trajectories of the target with varying complexity comprising almost 14 hours of flight data, and EPN demonstrates the shortest response time and the highest number of interceptions, which are key parameters of agile interception. To enable robust transfer from theory and simulation to a real-world implementation, we aim to avoid overfitting to specific assumptions about the target, and to tackle interception of a target following an unknown general trajectory. Furthermore, we identify several often overlooked problems related to tracking and estimation of the target's state that can have a significant influence on the overall performance of the system. We propose the use of a novel state estimation filter based on the IMM filter and a new measurement model. Simulated experiments show that the proposed solution provides significant improvements in estimation accuracy over the commonly employed KF approaches when considering general trajectories. Based on these results, we employ the proposed filtering and guidance methods to implement a complete autonomous interception system, which is thoroughly evaluated in realistic simulations and tested in real-world experiments with a maneuvering target going far beyond the performance of any state-of-the-art solution.

I last interviewed Ebrahim Raisi, the ultra-hard-line President of Iran, during his début appearance at the United Nations, in 2022. He spoke belligerently and with such speed that the interpreter struggled to keep up. He was the same on the U.N. dais, where he furiously waved a photo of General Qassem Soleimani and demanded that Donald Trump be tried for ordering his assassination--a "savage, illegal, immoral crime"--in a U.S. drone strike, in 2020. Back home, Iran was in turmoil after nationwide protests erupted in response to the death, in police custody, of a twenty-two-year-old named Mahsa Amini. She had been arrested for improper hijab; too much hair was showing.

The Iran-backed Houthi rebels in Yemen claimed on Tuesday they shot down an American drone over the impoverished Arab country. The U.S. military did not immediately acknowledge the claim. If confirmed, this would be the second MQ-9 Reaper drone downed by the Houthis over the past week as they press their campaign over the Israel-Hamas war in the Gaza Strip. Last Friday, the Houthis claimed downing an American drone over the province of Marib, hours after footage circulated online of what appeared to be the wreckage of an MQ-9 Reaper. And early Saturday, a vessel also came under attack in the Red Sea.

At least 11 people were killed and dozens injured after Russia bombed a busy lakeside resort on the edge of Ukraine's second-largest city of Kharkiv and attacked villages in the surrounding area. At least 13 people were injured after the Ukrainian military shelled areas of Russia's southern Belgorod region, according to Belgorod's regional Governor Vyacheslav Gladkov. The General Staff of the Armed Forces of Ukraine said Russian attacks in the Kharkiv area "slowed down a bit" but that forces "continue their attempts to break through our defences near Vovchansk, Starytsya and Lyptsi". Russia's Ministry of Defence, which claimed earlier to have seized Starytsya, said its units "continued to advance into the depth of the enemy's defences". Officials said Russia shot down at least 103 Ukrainian drones, including 62 over Russian regions, as well as missiles that targeted Crimea, which Moscow seized and annexed from Ukraine in 2014.

In urban environments for delivery robots, particularly in areas such as campuses and towns, many custom features defy standard road semantic categorizations. Addressing this challenge, our paper introduces a method leveraging Salient Object Detection (SOD) to extract these unique features, employing them as pivotal factors for enhanced robot loop closure and localization. Traditional geometric feature-based localization is hampered by fluctuating illumination and appearance changes. Our preference for SOD over semantic segmentation sidesteps the intricacies of classifying a myriad of non-standardized urban features. To achieve consistent ground features, the Motion Compensate IPM (MC-IPM) technique is implemented, capitalizing on motion for distortion compensation and subsequently selecting the most pertinent salient ground features through moment computations. For thorough evaluation, we validated the saliency detection and localization performances to the real urban scenarios. Project page: https://sites.google.com/view/salient-ground-feature/home.

This paper explores the application of Artificial Intelligence (AI) techniques for generating the trajectories of fleets of Unmanned Aerial Vehicles (UAVs). The two main challenges addressed include accurately predicting the paths of UAVs and efficiently avoiding collisions between them. Firstly, the paper systematically applies a diverse set of activation functions to a Feedforward Neural Network (FFNN) with a single hidden layer, which enhances the accuracy of the predicted path compared to previous work. Secondly, we introduce a novel activation function, AdaptoSwelliGauss, which is a sophisticated fusion of Swish and Elliott activations, seamlessly integrated with a scaled and shifted Gaussian component. Swish facilitates smooth transitions, Elliott captures abrupt trajectory changes, and the scaled and shifted Gaussian enhances robustness against noise. This dynamic combination is specifically designed to excel in capturing the complexities of UAV trajectory prediction. This new activation function gives substantially better accuracy than all existing activation functions. Thirdly, we propose a novel Integrated Collision Detection, Avoidance, and Batching (ICDAB) strategy that merges two complementary UAV collision avoidance techniques: changing UAV trajectories and altering their starting times, also referred to as batching. This integration helps overcome the disadvantages of both - reduction in the number of trajectory manipulations, which avoids overly convoluted paths in the first technique, and smaller batch sizes, which reduce overall takeoff time in the second.