From loitering munitions and multisensor platforms to large autogyro cargo drones -- this year's Singapore Airshow hosted an array of unmanned aerial vehicles and tech that could benefit Japan at a time when the Self-Defense Forces are planning to replace their aging aircraft and helicopters with UAVs. The airshow, which wrapped up earlier this week, highlighted the growing international demand for unmanned systems as they become increasingly indispensable for modern militaries, particularly against the backdrop of the war in Ukraine, where they have played a significant role on the battlefield. Japan's defense establishment is well aware of how drones are transforming warfare, which is why Tokyo is envisaging a growing role for unmanned systems in the SDF, especially in the air and maritime domains, as the country faces an increasingly tense regional security environment.

U.S. forces carried out more strikes against anti-ship cruise missiles and a drone in the Red Sea Thursday evening, Central Command said. CENTCOM forces conducted two self-defense strikes against six mobile anti-ship cruise missiles that were prepared to launch towards the Red Sea between 6 and 7:15 p.m. local time. Earlier in the evening, CENTCOM forces shot down a drone over the southern Red Sea in self-defense, CENTCOM said. "CENTCOM forces determined that the missiles and UAV presented an imminent threat to merchant vessels and to the U.S. Navy ships in the region," the command said. "These actions will protect freedom of navigation and make international waters safer and more secure for U.S. Navy and merchant vessels."

Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing. Our approach effectively applies a Long-Short Term Memory (LSTM) neural network for the purpose of time series classification. We performed experiments to test the effects of changing the timestamp sampling method and addressing the imbalance in the class distribution. Through these experiments, we identified the top-performing sampling and class imbalance fixing methods. Averaging the macro f-scores across 10 folds of data, we found that the majority quadrotor class was predicted well (98.16%), and, despite an extreme class imbalance, the model could also predicted a majority of fixed-wing flights correctly (73.15%). Hexarotor instances were often misclassified as quadrotors due to the similarity of multirotors in general (42.15%). However, results remained relatively stable across certain methods, which prompted us to analyze and report on their tradeoffs. The supplemental material for this paper, including the code and data for running all the experiments and generating the results tables, is available at https://osf.io/mnsgk/.

Unmanned Aircraft Vehicles (UAVs) are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and preliminary solutions for successful implementation of an autonomous UAV mission. Specifically, we identify challenges that have to be overcome and propose possible technical solutions for the challenges identified. We also derive analytical expressions for the success probability of an autonomous UAV mission, and describe a machine learning model to train the UAV.

Japan Airlines has kicked off an unmanned drone service to deliver goods and medical supplies in a remote part of Japan that's prone to heavy rains and landslides. The carrier is working with local authorities in the town of Setouchi, a tiny inlet in Okayama Prefecture that's home to 8,000 residents. A FAZER R G2 drone will be deployed by Amami Island Drones for the work, JAL said Thursday. People living in the area normally rely on ships for their daily logistic needs. But those vessels are often stranded by rough waves and have to cancel their scheduled runs.

This research addresses the pressing challenge of enhancing processing times and detection capabilities in Unmanned Aerial Vehicle (UAV)/drone imagery for global wildfire detection, despite limited datasets. Proposing a Segmented Neural Network (SegNet) selection approach, we focus on reducing feature maps to boost both time resolution and accuracy significantly advancing processing speeds and accuracy in real-time wildfire detection. This paper contributes to increased processing speeds enabling real-time detection capabilities for wildfire, increased detection accuracy of wildfire, and improved detection capabilities of early wildfire, through proposing a new direction for image classification of amorphous objects like fire, water, smoke, etc. Employing Convolutional Neural Networks (CNNs) for image classification, emphasizing on the reduction of irrelevant features vital for deep learning processes, especially in live feed data for fire detection. Amidst the complexity of live feed data in fire detection, our study emphasizes on image feed, highlighting the urgency to enhance real-time processing. Our proposed algorithm combats feature overload through segmentation, addressing challenges arising from diverse features like objects, colors, and textures. Notably, a delicate balance of feature map size and dataset adequacy is pivotal. Several research papers use smaller image sizes, compromising feature richness which necessitating a new approach. We illuminate the critical role of pixel density in retaining essential details, especially for early wildfire detection. By carefully selecting number of filters during training, we underscore the significance of higher pixel density for proper feature selection. The proposed SegNet approach is rigorously evaluated using real-world dataset obtained by a drone flight and compared to state-of-the-art literature.

Inverted landing is a routine behavior among a number of animal fliers. However, mastering this feat poses a considerable challenge for robotic fliers, especially to perform dynamic perching with rapid body rotations (or flips) and landing against gravity. Inverted landing in flies have suggested that optical flow senses are closely linked to the precise triggering and control of body flips that lead to a variety of successful landing behaviors. Building upon this knowledge, we aimed to replicate the flies' landing behaviors in small quadcopters by developing a control policy general to arbitrary ceiling-approach conditions. First, we employed reinforcement learning in simulation to optimize discrete sensory-motor pairs across a broad spectrum of ceiling-approach velocities and directions. Next, we converted the sensory-motor pairs to a two-stage control policy in a continuous augmented-optical flow space. The control policy consists of a first-stage Flip-Trigger Policy, which employs a one-class support vector machine, and a second-stage Flip-Action Policy, implemented as a feed-forward neural network. To transfer the inverted-landing policy to physical systems, we utilized domain randomization and system identification techniques for a zero-shot sim-to-real transfer. As a result, we successfully achieved a range of robust inverted-landing behaviors in small quadcopters, emulating those observed in flies.

A series of airstrikes carried out by the United States and the United Kingdom on Saturday destroyed or damaged 17 of 18 Houthi targets in Yemen, Department of Defense (DoD) officials told Fox News on Tuesday. The targets included underground weapons storage facilities, missile storage facilities, one-way attack unmanned aerial systems, air defense systems, radars, and a helicopter, said DoD spokesperson U.S. Army Major Pete Nguyen. The coalition airstrikes targeted Yemen's Iran-backed Houthis, and came days after a British cargo ship was hit by a Houthi missile. "More broadly, since the first coalition strikes on Jan. 11, we assess that we've destroyed or degraded more than 150 missiles and launchers, including anti-ship land attack and surface-to-air missiles, plus numerous communication capabilities, unmanned aerial vehicles, unmanned surface vessels, coastal radars, air surveillance capabilities, rotary wing aircraft, underground facilities including weapon storage areas, and command and control buildings," Nguyen said. Gen. Pat Ryder said the strikes have degraded "a significant amount of capability" for the Houthis.

The emergence of tethered drones represents a major advancement in unmanned aerial vehicles (UAVs) offering solutions to key limitations faced by traditional drones. This article explores the potential of tethered drones with a particular focus on their ability to tackle issues related to battery life constraints and data latency commonly experienced by battery operated drones. Through their connection to a ground station via a tether, autonomous tethered drones provide continuous power supply and a secure direct data transmission link facilitating prolonged operational durations and real time data transfer. These attributes significantly enhance the effectiveness and dependability of drone missions in scenarios requiring extended surveillance, continuous monitoring and immediate data processing needs. Examining the advancements, operational benefits and potential future progressions associated with tethered drones, this article shows their increasing significance across various sectors and their pivotal role in pushing the boundaries of current UAV capabilities. The emergence of tethered drone technology not only addresses existing obstacles but also paves the way for new innovations within the UAV industry.

Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.