Vehicle detection and recognition in drone images is a complex problem that has been used for different safety purposes. The main challenge of these images is captured at oblique angles and poses several challenges like non-uniform illumination effect, degradations, blur, occlusion, loss of visibility, etc. Additionally, weather conditions play a crucial role in causing safety concerns and add another high level of challenge to the collected data. Over the past few decades, various techniques have been employed to detect and track vehicles in different weather conditions. However, detecting vehicles in heavy snow is still in the early stages because of a lack of available data. Furthermore, there has been no research on detecting vehicles in snowy weather using real images captured by unmanned aerial vehicles (UAVs). This study aims to address this gap by providing the scientific community with data on vehicles captured by UAVs in different settings and under various snow cover conditions in the Nordic region. The data covers different adverse weather conditions like overcast with snowfall, low light and low contrast conditions with patchy snow cover, high brightness, sunlight, fresh snow, and the temperature reaching far below -0 degrees Celsius. The study also evaluates the performance of commonly used object detection methods such as Yolo v8, Yolo v5, and fast RCNN. Additionally, data augmentation techniques are explored, and those that enhance the detectors' performance in such scenarios are proposed. The code and the dataset will be available at https://nvd.ltu-ai.dev

DJI has unveiled its new flagship consumer drone, the Mavic 3 Pro, with a triple-camera setup that includes a new 70mm lens designed for "powerful subject framing." It also includes a new 10-bit D-Log M color mode, improvements in the tele cameras, and ProRes capture on the Mavic 3 Pro Cine option. Like the Mavic 3, it's available in regular and Cine models, with the latter having advanced features for filmmakers like Apple ProRes capture (ProRes 422 HQ, ProRes 422, and ProRes 422 LT), a 1TB SSD drive and a 10Gbps lightspeed data cable. However, you'll pay a premium of nearly $1,000 to get those. The new 70mm camera has a 1/1.3-inch sensor that's the same size as on the Mini 3 Pro. Though considerably smaller than the 4/3 chip on the main Hasselblad camera, DJI says the camera is designed for a "range of different scenarios from framing intriguing buildings to cars in commercial shoots."

In Libya, Bayraktar TB2 drones helped the official government in Tripoli stamp down an uprising by warlord Khalifa Haftar. In Nagorno-Karabakh, they played a decisive role in Azerbaijan's victory over Armenia, after which autocrat Ilham Aliyev celebrated by presenting footage of drone strikes on video screens in the capital city of Baku. The Ukrainians mainly deployed them in the first months of the war, before Russian air defenses adapted their strategy. The fact that the drones are supplied primarily to countries that are close to the Erdoğan government is conspicuous. Baykar is the flagship of Turkey's defense industry, which has grown tenfold since Erdoğan came to power in 2003.

Medical robots controlled by edible computer chips could deliver drugs inside the body, say researchers. Similar robots could also be used to deliver drugs or vital nutrients to at-risk animals and then naturally biodegrade. Soft robots that can operate inside the human body are a busy area of research, but they tend to be remotely controlled from outside the body with magnets.

Autonomous Nano Aerial Vehicles have been increasingly popular in surveillance and monitoring operations due to their efficiency and maneuverability. Once a target location has been reached, drones do not have to remain active during the mission. It is possible for the vehicle to perch and stop its motors in such situations to conserve energy, as well as maintain a static position in unfavorable flying conditions. In the perching target estimation phase, the steady and accuracy of a visual camera with markers is a significant challenge. It is rapidly detectable from afar when using a large marker, but when the drone approaches, it quickly disappears as out of camera view. In this paper, a vision-based target poses estimation method using multiple markers is proposed to deal with the above-mentioned problems. First, a perching target with a small marker inside a larger one is designed to improve detection capability at wide and close ranges. Second, the relative poses of the flying vehicle are calculated from detected markers using a monocular camera. Next, a Kalman filter is applied to provide a more stable and reliable pose estimation, especially when the measurement data is missing due to unexpected reasons. Finally, we introduced an algorithm for merging the poses data from multi markers. The poses are then sent to the position controller to align the drone and the marker's center and steer it to perch on the target. The experimental results demonstrated the effectiveness and feasibility of the adopted approach. The drone can perch successfully onto the center of the markers with the attached 25mm-diameter rounded magnet.

Abstract--Despite recent progress in trajectory planning for multiple robots and a single tethered robot, trajectory planning for multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this paper, a complete approach is presented to address this problem. First, a multi-robot tether-aware representation of homotopy is proposed to efficiently evaluate the feasibility and safety of a potential path in terms of (1) the cable length required to reach a target following the path, and (2) the risk of entanglements with the cables of other robots. Top-down view of a workspace to illustrate an entanglement situation. The efficiency of the proposed homotopy representation is compared against the existing single and multiple tethered robot planning approaches. While there exists abundant literature on multi-robot path I. NMANNED vehicles such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs) and unmanned robot planning problem focus on the single robot case and surface vehicles (USVs) have been widely adopted use a representation of homotopy to identify the path or cable in industrial applications due to reduced safety hazards for configuration [8]-[11]. Feasible paths are found by searching humans and potential cost saving [1], [2]. For autonomous tethered robots, it is robots efficiently. Moreover, slow graph expansion requires important to consider the risk of the tether being entangled offline construction of the graph prior to online planning. Each robot is attached to one end of a slack and online trajectory generation framework for non-entangling flexible cable that is allowed to lie on the ground.

A "Ukrainian" drone has been found outside Moscow, an official has said, adding that the discovery had forced local authorities to call off a Victory Day parade for security reasons. Moscow has accused Ukraine of being behind a number of drone attacks on military infrastructure deep inside Russian territory. On Monday, Igor Sukhin, head of the Bogorodsky city district outside the capital Moscow, said that a local resident had found a "Ukrainian" drone in a forest. "This is not the first drone that appeared in the Moscow region," Sukhin said on the messaging app Telegram. A similar drone was in February found in the town of Kolomna approximately 100km (about 60 miles) southeast of Moscow, he added.

Russia's Black Sea Fleet has warded off a drone attack on the Crimean port of Sevastopol, the Moscow-installed governor of the city says. "An attempted attack on Sevastopol was repelled from 3:30am [00:30 GMT]," Mikhail Razvojayev said on Telegram on Monday. "A surface drone [naval] was destroyed by the anti-sabotage forces, the second one exploded on its own," he said, adding that no damage was reported. Passenger ferry service were suspended in the port city, Russia's Interfax news agency reported, citing Sevastopol transport authorities. No reason was given, but the agency said traffic had been suspended in the past due to drone attacks or storms.

Makram Chahine, a PhD student in electrical engineering and computer science and an MIT CSAIL affiliate, leads a drone used to test liquid neural networks. In the vast, expansive skies where birds once ruled supreme, a new crop of aviators is taking flight. These pioneers of the air are not living creatures, but rather a product of deliberate innovation: drones. Rather, they're avian-inspired marvels that soar through the sky, guided by liquid neural networks to navigate ever-changing and unseen environments with precision and ease. Inspired by the adaptable nature of organic brains, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have introduced a method for robust flight navigation agents to master vision-based fly-to-target tasks in intricate, unfamiliar environments.

The drone has been used for various purposes, including military applications, aerial photography, and pesticide spraying. However, the drone is vulnerable to external disturbances, and malfunction in propellers and motors can easily occur. To improve the safety of drone operations, one should detect the mechanical faults of drones in real-time. This paper proposes a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. The dataset includes various operating conditions of drones, such as flight directions (front, back, right, left, clockwise, counterclockwise) and faults on propellers and motors. The drone sounds were then mixed with noises recorded in five different spots on the university campus, with a signal-to-noise ratio (SNR) varying from 10 dB to 15 dB. Using the acquired dataset, we train a DNN classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms. We employ multitask learning (MTL) and incorporate the direction classification task as an auxiliary task to make the classifier learn more general audio features. The test over unseen data reveals that the proposed multitask model can successfully classify faults in drones and outperforms single-task models even with less training data.