Measuring an overall autonomy score for a robotic system requires the combination of a set of relevant aspects and features of the system that might be measured in different units, qualitative, and/or discordant. In this paper, we build upon an existing non-contextual autonomy framework that measures and combines the Autonomy Level and the Component Performance of a system as overall autonomy score. We examine several methods of combining features, showing how some methods find different rankings of the same data, and we employ the weighted product method to resolve this issue. Furthermore, we introduce the non-contextual autonomy coordinate and represent the overall autonomy of a system with an autonomy distance. We apply our method to a set of seven Unmanned Aerial Systems (UAS) and obtain their absolute autonomy score as well as their relative score with respect to the best system.

A massive sinkhole opened up at a soccer field in Alton, Illinois, on Wednesday. A 100-foot-wide sinkhole opened beneath a soccer field in Illinois on Wednesday as a result of a collapse at a nearby underground mine, officials said. The sinkhole formed at around 10 a.m. at Gordon Moore Park in Alton. Surveillance video from the City of Alton shows the moment the sinkhole opens and swallows a light pole on the field in a cloud of dust. Drone video shows the aftermath of the crater in the center of the field.

Russia has suffered multiple diplomatic and judicial blows during the past week over its war on Ukraine, despite President Vladimir Putin's high-profile visits to North Korea and Vietnam and Moscow's claims that it is founding a "Eurasian security architecture that will replace the discredited Euro-Atlantic security arrangements". Putin signed a "comprehensive strategic treaty" with North Korean leader Kim Jong Un on June 19, incorporating what he said was a defensive alliance. South Korea's government condemned the agreement. Its national security adviser, Chang Ho-jin, declared that Seoul would reconsider lifting a ban on arms supplies directly to Ukraine. Until now, South Korea has only sold weapons to Ukraine's allies.

Animal populations worldwide are rapidly declining, and a technology that can accurately count endangered species could be vital for monitoring population changes over several years. This research focused on fine-tuning object detection models for drone images to create accurate counts of animal species. Hundreds of images taken using a drone and large, openly available drone-image datasets were used to fine-tune machine learning models with the baseline YOLOv8 architecture. We trained 30 different models, with the largest having 43.7 million parameters and 365 layers, and used hyperparameter tuning and data augmentation techniques to improve accuracy. While the state-of-the-art YOLOv8 baseline had only 0.7% accuracy on a dataset of safari animals, our models had 95% accuracy on the same dataset. Finally, we deployed the models on the Jetson Orin Nano for demonstration of low-power real-time species detection for easy inference on drones.

But on June 14, the US House of Representatives passed a bill that would completely ban DJI's drones from being sold in the US. The bill is now being discussed in the Senate as part of the annual defense budget negotiations. While its market dominance has attracted scrutiny for years, it's increasingly clear that DJI's commercial products are so good and affordable they are also being used on active battlefields to scout out the enemy or carry bombs. As the US worries about the potential for conflict between China and Taiwan, the military implications of DJI's commercial drones are becoming a top policy concern. DJI has managed to set the gold standard for commercial drones because it is built on decades of electronic manufacturing prowess and policy support in Shenzhen.

In the early morning hours of May 15, the cargo vessel Borkum stopped off the Spanish coast, lingering in the waters a short distance from Cartagena. At the port, protesters waved Palestinian flags and called on authorities to inspect the ship based on suspicions that it carried weapons bound for Israel. Leftist members of the European Parliament sent a letter to Spanish President Pedro Sánchez requesting that the ship be prevented from docking. "Allowing a ship loaded with weapons destined for Israel is to allow the transit of arms to a country currently under investigation for genocide against the Palestinian people," the group of nine MEPs warned. Before the Spanish government could take a stand, the Borkum cancelled its planned stopover and continued to the Slovenian port of Koper.

In the rapidly evolving field of computer science education, novel approaches to teaching fundamental concepts are crucial for engaging a diverse student body. Given the growing demand for a computing-skilled workforce, it is essential to adapt educational methods to capture the interest of a broader audience than what current computing education typically targets. Engaging educational experiences have been shown to have a positive impact on learning outcomes and examination performance, especially within computing education. Moreover, physical computing devices have been shown to correlate with increased student motivation when students are studying computer science.

In this work uses innovative multi-channel load-sensing techniques to deploy unmanned aerial vehicles (UAVs) for surveillance. The research aims to improve the quality of data transmission methods and improve the efficiency and reliability of surveillance systems by exploiting the mobility and adaptability of UAVs does the proposed protocol intelligently distribute network traffic across multiple channels, considering the load of each channel, While addressing challenges such as load balancing, this study investigates the effectiveness of the protocol by simulations or practical tests on The expected results have improved UAV-based surveillance systems, more flexible and efficient networks for applications such as security, emergency response and the environment alignment of monitoring -Offering infrastructures, which contribute to efficient and reliable monitoring solutions.

The inspection and monitoring of infrastructure assets typically requires identifying visual anomalies in scenes periodically photographed over time. Images collected manually or with robots such as unmanned aerial vehicles from the same scene at different instances in time are typically not perfectly aligned. Supervised segmentation methods can be applied to identify known problems, but unsupervised anomaly detection approaches are required when unknown anomalies occur. Current unsupervised pixel-level anomaly detection methods have mainly been developed for industrial settings where the camera position is known and constant. However, we find that these methods fail to generalize to the case when images are not perfectly aligned. We term the problem of unsupervised anomaly detection between two such imperfectly aligned sets of images as Scene Anomaly Detection (Scene AD). We present a novel network termed OmniAD to address the Scene AD problem posed. Specifically, we refine the anomaly detection method reverse distillation to achieve a 40% increase in pixel-level anomaly detection performance. The network's performance is further demonstrated to improve with two new data augmentation strategies proposed that leverage novel view synthesis and camera localization to improve generalization. We validate our approach with qualitative and quantitative results on a new dataset, ToyCity, the first Scene AD dataset with multiple objects, as well as on the established single object-centric dataset, MAD. https://drags99.github.io/OmniAD/

Autonomous drone navigation faces a critical challenge in achieving accurate landings on dynamic platforms, especially under unpredictable conditions such as wind turbulence. Our research introduces TornadoDrone, a novel Deep Reinforcement Learning (DRL) model that adopts bio-inspired mechanisms to adapt to wind forces, mirroring the natural adaptability seen in birds. This model, unlike traditional approaches, derives its adaptability from indirect cues such as changes in position and velocity, rather than direct wind force measurements. TornadoDrone was rigorously trained in the gym-pybullet-drone simulator, which closely replicates the complexities of wind dynamics in the real world. Through extensive testing with Crazyflie 2.1 drones in both simulated and real windy conditions, TornadoDrone demonstrated a high performance in maintaining high-precision landing accuracy on moving platforms, surpassing conventional control methods such as PID controllers with Extended Kalman Filters. The study not only highlights the potential of DRL to tackle complex aerodynamic challenges but also paves the way for advanced autonomous systems that can adapt to environmental changes in real-time. The success of TornadoDrone signifies a leap forward in drone technology, particularly for critical applications such as surveillance and emergency response, where reliability and precision are paramount.