Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum due to their operational advantages. An increasing number of scenarios now require autonomous control of multiple UAVs, as autonomous operation can significantly reduce labor costs. Additionally, obtaining optimal flight paths can lower energy consumption, thereby extending battery life for other critical operations. Many of these scenarios, however, involve obstacles such as power lines and trees, which complicate Path Planning. This paper presents an evolutionary computation-based system employing genetic algorithms to address this problem in environments with obstacles. The proposed approach aims to ensure complete coverage of areas with fixed obstacles, such as in field exploration tasks, while minimizing flight time regardless of map size or the number of UAVs in the swarm. No specific goal points or prior information beyond the provided map is required. The experiments conducted in this study used five maps of varying sizes and obstacle densities, as well as a control map without obstacles, with different numbers of UAVs. The results demonstrate that this method can determine optimal paths for all UAVs during full map traversal, thus minimizing resource consumption. A comparative analysis with other state-of-the-art approach is presented to highlight the advantages and potential limitations of the proposed method.

The FBI has launched an investigation into mysterious glowing lights that have been spotted over New Jersey for the last few weeks. Eyewitnesses reported unexplained'car-sized' drones over the Trump National Golf Club in Bedminster and the Picatinny Arsenal Military Base in Rockaway, among other locations throughout northern New Jersey. Video footage revealed the drones featured green and red lights on their wings and multiple eyewitness described them as large as a small car. However, the flying objects are larger than drones used by hobbyists, raising questions about their proximity to those specific locations. The Federal Aviation Administration (FAA) was first alerted about the strange activity in Morris County, where the military base is located, on November 18, but sights also surfaced in nearby Menham, Chester and Morristown.

Fox News' Stephanie Bennett reports the latest on the unidentified drones from London. The Federal Aviation Administration (FAA) confirmed Tuesday it issued two flight restrictions following questionable drone activity in the area of President-elect Trump's New Jersey golf club. On Nov. 18, the FAA first received reports of drone activity within Morris County, the border of which lies about two miles north of Trump National Golf Club Bedminster in Somerset County. Upon request from "federal security partners," the agency issued two TFRs, or temporary flight restrictions, and several reports of drone sightings continued into this week in Central Jersey. One restriction covers an area near Solberg-Hunterdon County Airport that consists of airspace above Trump Bedminster.

An Israeli drone strike has killed one person in southern Lebanon, according to Lebanese health authorities, as Prime Minister Benjamin Netanyahu promised to enforce the ceasefire with Hezbollah "with an iron fist". Lebanon's Ministry of Public Health and state media said Israeli forces carried out several new drone and artillery strikes in southern Lebanon on Tuesday, putting further strain on a tenuous 6-day-old ceasefire with Hezbollah. "An Israeli enemy drone strike on the town of Shebaa killed one person," a Health Ministry statement said. The state-run National News Agency (NNA) described the man who was killed as a "shepherd". The new attacks come as Israeli officials threatened to expand attacks on Lebanon if the ceasefire with the Lebanese armed group Hezbollah collapses.

A Florida man allegedly broke into a woman's home, stabbed her while she was sleeping and attempted to flee from deputies. A Florida man is facing charges after he allegedly broke into a woman's home, stabbed her while she was sleeping and attempted to run away from deputies. Bonnier Jose Sarmiento Lanza, 33, on Sunday broke into a woman's home on New York Drive in Tice, Florida, and stabbed her multiple times while she was sleeping, according to the Lee County Sheriff's Office. Lanza also hit another person inside the home before fleeing the scene. Bonnier Jose Sarmiento Lanza, 33, is charged with two counts of burglary with battery and aggravated battery with a deadly weapon.

A squadron of six Chinese Chengdu J-10 jets took off towards an overcast sky in front of thousands of spectators at an airfield in southern China's coastal city of Zhuhai in mid-November. Flying low in a close V-shaped formation, the jets circled back and as they approached a cluster of buildings near the spectators, trails of red, blue, yellow and white smoke suddenly poured from each plane, bringing a cheer from onlookers that was almost as loud as the roar from the warplanes' engines. Seconds later, the J-10s broke their close formation to show off a series of even more impressive acrobatic manoeuvres. But the aerial show by the seasoned pilots was far from the only demonstration of prowess at the China International Aviation & Aerospace Exhibition, better known as Airshow China or the Zhuhai Airshow, which is held biennially and named after the city in southern China where it is held. A wide array of new equipment and aircraft available to the Chinese military – known as the People's Liberation Army (PLA) – was unveiled for the first time at the airshow, held from November 12 to 17.

Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings. These inaccuracies hinder aggregation of diverse data from multiple sources in a unified georeferenced frame for SAR operators. In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses. Besides, LiDAR works best in the vicinity of 3D structures. Hence, we refine coarse GNSS measurements by registering LiDAR maps against CityGML and digital elevation map (DEM) models as a prior for allocentric mapping. An intuitive plausibility score selects the best hypothesis based on occupancy using a 2D height map. Afterwards, we integrate the registration results in a continuous-time spline-based pose graph optimizer with LiDAR odometry and further sensing modalities to obtain globally consistent, georeferenced trajectories and maps. We evaluate the viability of our approach on multiple flights captured at two distinct testing sites. Our method successfully reduced GNSS offset errors from up-to 16 m to below 0.5 m on multiple flights. Furthermore, we obtain globally consistent maps w.r.t. prior 3D geospatial models.

The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using uncrewed aerial vehicles (UAVs), since, during the robot's movement, the human operator is often out of the robot's field of view. Furthermore, in multi-robot scenarios, distributed training is also an open problem. In this sense, this work proposes an action recognition and control approach based on Long Short-Term Memory (LSTM) Deep Neural Networks with two layers in association with three densely connected layers and Federated Learning (FL) embedded in multiple drones. The FL enabled our approach to be trained in a distributed fashion, i.e., access to data without the need for cloud or other repositories, which facilitates the multi-robot system's learning. Furthermore, our multi-robot approach results also prevented occlusion situations, with experiments with real robots achieving an accuracy greater than 96%.

The network of services, including delivery, farming, and environmental monitoring, has experienced exponential expansion in the past decade with Unmanned Aerial Vehicles (UAVs). Yet, UAVs are not robust enough against cyberattacks, especially on the Controller Area Network (CAN) bus. The CAN bus is a general-purpose vehicle-bus standard to enable microcontrollers and in-vehicle computers to interact, primarily connecting different Electronic Control Units (ECUs). In this study, we focus on solving some of the most critical security weaknesses in UAVs by developing a novel graph-based intrusion detection system (IDS) leveraging the Uncomplicated Application-level Vehicular Communication and Networking (UAVCAN) protocol. First, we decode CAN messages based on UAVCAN protocol specification; second, we present a comprehensive method of transforming tabular UAVCAN messages into graph structures. Lastly, we apply various graph-based machine learning models for detecting cyber-attacks on the CAN bus, including graph convolutional neural networks (GCNNs), graph attention networks (GATs), Graph Sample and Aggregate Networks (GraphSAGE), and graph structure-based transformers. Our findings show that inductive models such as GATs, GraphSAGE, and graph-based transformers can achieve competitive and even better accuracy than transductive models like GCNNs in detecting various types of intrusions, with minimum information on protocol specification, thus providing a generic robust solution for CAN bus security for the UAVs. We also compared our results with baseline single-layer Long Short-Term Memory (LSTM) and found that all our graph-based models perform better without using any decoded features based on the UAVCAN protocol, highlighting higher detection performance with protocol-independent capability.

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory requirements as it uses only the relative range and bearing of swarm agents in local proximity obtained through onboard sensors on the UAV. Thus, our method is able to stabilize and control the flock of a general shape above a steep terrain without any explicit communication between swarm members. To implement proximal control in a three-dimensional manner, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions between robots. The performance of the proposed approach was tested in real-world conditions by experiments with a team of nine UAVs. Experiments also present the usage of our approach on UAVs that are independent of external positioning systems such as the Global Navigation Satellite System (GNSS). Relying only on a relative visual localization through the ultraviolet direction and ranging (UVDAR) system, previously proposed by our group, the experiments verify that our system can be applied in GNSS-denied environments. The degree achieved of alignment and cohesiveness was evaluated using the metrics of order and steady-state value.