Hezbollah has released drone footage that it claims shows the group's surveillance of Israel's port city of Haifa and its surroundings. The video includes aerial views of sites in Haifa including sea and airports.

Dynamic elements: A 50cm x 50cm x 20cm drone constituent is a software component (running on some arrives on the hand left side of the surveillance area piece of hardware) that takes as input images provided (with orientation = (10, 25, 3)) at a distance from a camera and generates as outputs data representing of 450m from the system, moving with a straight bounding boxes of objects detected in the image along with trajectory, in the direction of the system, at a constant their classification. The ML constituent, figure 3, contains speed of 1m/s. Sun is visible (on the left hand side of three main software components (the pre/post-processing the image).

Lightweight autonomous unmanned aerial vehicles (UAV) are emerging as a central component of a broad range of applications. However, autonomous navigation necessitates the implementation of perception algorithms, often deep neural networks (DNN), that process the input of sensor observations, such as that from cameras and LiDARs, for control logic. The complexity of such algorithms clashes with the severe constraints of these devices in terms of computing power, energy, memory, and execution time. In this paper, we propose NaviSplit, the first instance of a lightweight navigation framework embedding a distributed and dynamic multi-branched neural model. At its core is a DNN split at a compression point, resulting in two model parts: (1) the head model, that is executed at the vehicle, which partially processes and compacts perception from sensors; and (2) the tail model, that is executed at an interconnected compute-capable device, which processes the remainder of the compacted perception and infers navigation commands. Different from prior work, the NaviSplit framework includes a neural gate that dynamically selects a specific head model to minimize channel usage while efficiently supporting the navigation network. In our implementation, the perception model extracts a 2D depth map from a monocular RGB image captured by the drone using the robust simulator Microsoft AirSim. Our results demonstrate that the NaviSplit depth model achieves an extraction accuracy of 72-81% while transmitting an extremely small amount of data (1.2-18 KB) to the edge server. When using the neural gate, as utilized by NaviSplit, we obtain a slightly higher navigation accuracy as compared to a larger static network by 0.3% while significantly reducing the data rate by 95%. To the best of our knowledge, this is the first exemplar of dynamic multi-branched model based on split DNNs for autonomous navigation.

Quadrotors are among the most agile flying robots. Despite recent advances in learning-based control and computer vision, autonomous drones still rely on explicit state estimation. On the other hand, human pilots only rely on a first-person-view video stream from the drone onboard camera to push the platform to its limits and fly robustly in unseen environments. To the best of our knowledge, we present the first vision-based quadrotor system that autonomously navigates through a sequence of gates at high speeds while directly mapping pixels to control commands. Like professional drone-racing pilots, our system does not use explicit state estimation and leverages the same control commands humans use (collective thrust and body rates). We demonstrate agile flight at speeds up to 40km/h with accelerations up to 2g. This is achieved by training vision-based policies with reinforcement learning (RL). The training is facilitated using an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use the inner edges of the gates as a sensor abstraction. This simple yet robust, task-relevant representation can be simulated during training without rendering images. During deployment, a Swin-transformer-based gate detector is used. Our approach enables autonomous agile flight with standard, off-the-shelf hardware. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments.

As autonomous vehicles continue to revolutionize transportation, addressing challenges posed by adverse weather conditions, particularly during winter, becomes paramount for ensuring safe and efficient operations. One of the most important aspects of a road safety inspection during adverse weather is when a limited lane width can reduce the capacity of the road and raise the risk of serious accidents involving autonomous vehicles. In this research, a method for improving driving challenges on roads in winter conditions, with a model that segments and estimates the width of the road from the perspectives of Uncrewed aerial vehicles and autonomous vehicles. The proposed approach in this article is needed to empower self-driving cars with up-to-date and accurate insights, enhancing their adaptability and decision-making capabilities in winter landscapes.

Current formations commonly rely on invariant hierarchical structures, such as predetermined leaders or enumerated formation shapes. These structures could be unidirectional and sluggish, constraining their adaptability and agility when encountering cluttered environments. To surmount these constraints, this work proposes an omnidirectional affine formation approach with hierarchical reorganizations. We first delineate the critical conditions requisite for facilitating hierarchical reorganizations within formations, which informs the development of the omnidirectional affine criterion. Central to our approach is the fluid leadership and authority redistribution, for which we develop a minimum time-driven leadership evaluation algorithm and a power transition control algorithm. These algorithms facilitate autonomous leader selection and ensure smooth power transitions, enabling the swarm to adapt hierarchically in alignment with the external environment. Furthermore, we deploy a power-centric topology switching mechanism tailored for the dynamic reorganization of in-team connections. Finally, simulations and experiments demonstrate the performance of the proposed method. The formation successfully performs several hierarchical reorganizations, with the longest reorganization taking only 0.047s. This swift adaptability allows five aerial robots to carry out complex tasks, including executing swerving movements and navigating through hoops at velocities up to 1.9m/s.

Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in complex, highly textured environments. In such scenarios, drones seamlessly integrate into the background, creating camouflage effects that adversely affect the detection quality. To address this issue, we introduce a novel deep learning architecture called YOLO-FEDER FusionNet. Unlike conventional approaches, YOLO-FEDER FusionNet combines generic object detection methods with the specialized strength of camouflage object detection techniques to enhance drone detection capabilities. Comprehensive evaluations of YOLO-FEDER FusionNet show the efficiency of the proposed model and demonstrate substantial improvements in both reducing missed detections and false alarms.

The Defense Ministry is planning to test the use of lasers to counter drones, sources familiar with the matter have said. Under the plan, a laser system will be installed on vehicles of the Ground Self-Defense Force, including high mobility vehicles, and tests on its response capabilities will be conducted, the sources said. In March, the ministry signed a contract to buy lasers to be mounted on GSDF high mobility vehicles from Kawasaki Heavy Industries for about 1.5 billion and a 1.9 billion deal to purchase lasers for trucks from Mitsubishi Heavy Industries.

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users in diverse scenarios like festivals, large sporting events, natural and man-made disasters due to their advantages in terms of versatility and maneuverability. However, the limited resources of UAVs (e.g., energy budget and different service requirements) can pose challenges for adopting UAVs for such applications. Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAVs. In this work, we introduce an optimization model with the aim of maximizing the throughput and UAVs coverage through optimal path planning of UAVs and multi-RIS phase configurations. The formulated optimization is challenging to solve using standard linear programming techniques, limiting its applicability in real-time decision-making. Therefore, we introduce a two-step solution using deep reinforcement learning and particle swarm optimization. We conduct extensive simulations and compare our approach to two competitive solutions presented in the recent literature. Our simulation results demonstrate that our adopted approach is 20 \% better than the brute-force approach and 30\% better than the baseline solution in terms of QoS.

Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these systems while maintaining low cost, rigor, and external validity. This paper introduces the Multi-drone Sensing Experimentation Testbed (M-SET), a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence. M-SET addresses the limitations of existing testbeds that fail to emulate collisions, thus lacking realism in outdoor environments. By integrating a collision avoidance method based on a potential field algorithm, M-SET ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm. Extensive evaluation demonstrates accurate energy consumption estimation and a low risk of collisions, providing a robust proof-of-concept. New insights show that M-SET has significant potential to support ambitious research with minimal cost, simplicity, and high sensing quality.