PRAGUE – Eugene Nayshtetik and his five co-workers shuttered their company developing medical and biotech startups to join the defense forces days after Russia invaded Ukraine. Within two months, their commanders agreed it would be more useful if they swapped their military gear for computers. With the government's blessing, Nayshtetik and his team of engineers moved to neighboring Poland where they raised initial funding from a Polish company, Air Res Aviation, to develop a new drone for the Ukrainian military. Jerzy Nowak, president and co-owner of Air Res Aviation, said his company's initial investment in the drone project amounted to around $200,000. This could be due to a conflict with your ad-blocking or security software.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The French government on Tuesday approved a key budget bill presented as the country's biggest military spending spree in more than 50 years, underscoring the impact of Russia's war in Ukraine. The bill foresees $450 billion in military spending or the period covering 2024-2030 - up by more than a third relative to the previous timeframe. Defense Minister Sébastien Lecornu said bill's political, budgetary, military and technological drive is comparable to the huge push in the 1960s that saw France develop nuclear weapons, making the country one of the world's major military powers.

Three days before Christmas 2022, Amazon Prime Air was set to deliver its first commercial package by drone to a residential customer in Lockeford, California. It was supposed to be a celebration, a culmination of tens of thousands of test flights, years of dealing with Federal Aviation Administration paperwork, a decade of development, and $2 billion of investment. Early that morning, about 40 people--including FAA officials, Amazon engineers, public relations staff, and Prime Air chief pilot Jim Mullin--waited outside a steel frame warehouse on a flat, 20-acre parcel of land flanked by vineyards. Inside the warehouse, a flight crew had loaded the drone--a six-propeller, roughly 80-pound carbon-fiber MK27-2--with a lithium-ion battery and a box containing an Exploding Kittens card game. But when the operator in charge tried to load the flight package, the software wouldn't boot up, says a former employee who asked to remain anonymous out of fear of retaliation: "That's when panic started to set in, and the higher-ups went into war-room mode."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. military said Tuesday that a drone strike carried out by the American-led coalition in northwestern Syria has killed a senior member of the Islamic State group who was in charge of planning attacks in Europe. Khalid Aydd Ahmad al-Jabouri was identified as the member killed in the strike, according to a statement from U.S. Central Command (CENTCOM). The military said the killing "will temporarily disrupt the organization's ability to plot external attacks."

The importance of ground Mobile Robots (MRs) and Unmanned Aerial Vehicles (UAVs) within the research community, industry, and society is growing fast. Many of these agents are nowadays equipped with communication systems that are, in some cases, essential to successfully achieve certain tasks. In this context, we have begun to witness the development of a new interdisciplinary research field at the intersection of robotics and communications. This research field has been boosted by the intention of integrating UAVs within the 5G and 6G communication networks. This research will undoubtedly lead to many important applications in the near future. Nevertheless, one of the main obstacles to the development of this research area is that most researchers address these problems by oversimplifying either the robotics or the communications aspect. This impedes the ability of reaching the full potential of this new interdisciplinary research area. In this tutorial, we present some of the modelling tools necessary to address problems involving both robotics and communication from an interdisciplinary perspective. As an illustrative example of such problems, we focus in this tutorial on the issue of communication-aware trajectory planning.

This paper develops a high-density air corridor traffic flow model for Uncrewed Aircraft System (UAS) operation in urban low altitude airspace. To maximize throughput with safe separation guarantees, we define an airspace spatiotemporal planning problem. For the spatial planning, we propose a multi-floor UAS coordination structure divided into a finite number of air corridors safely wrapping buildings and obstacles. We use the USGS Lidar data to map buildings and in turn generate air corridors by modeling UAS coordination as ideal fluid flow with the streamlines obtained by solving the Laplace partial differential equation (PDE). Proper boundary conditions for the differential equations are imposed to direct air corridors along the floors desired motion direction. For temporal planning, we use 4-dimensional path-finding through the corridor network with A* search to maximize airspace usability given each UAS initial and destination waypoint pair.

Remote identification of Unmanned Aerial Vehicles (UAVs) is becoming increasingly important since more UAVs are being widely used for different needs in urban areas. For example, in the US and in the EU, identification and position broadcasting is already a requirement for the use of drones. However, the current solutions do not validate the position of the UAV but its identity, while trusting the given position. Therefore, a more advanced solution enabling the proof of location is needed to avoid spoofing. We propose the combination of a permissioned blockchain managed by public authorities together with UWB-based communication to approach this. Specifically, we leverage the identity management tools from Hyperledger Fabric, an open-source permissioned blockchain framework, and ultra-wideband (UWB) ranging, leading to situated communication (i.e., simultaneous communication and localization). This approach allows us to prove both the UAV identity and also the location it broadcasts through interaction with ground infrastructure in known locations. Our initial experiments show that the proposed approach is viable and UWB transceivers can be used for UAVs to validate both their identity and position with ground infrastructure deployed in known locations.

Unmanned aerial vehicles (UAVs) have numerous applications, but their efficient and optimal flight can be a challenge. Reinforcement Learning (RL) has emerged as a promising approach to address this challenge, yet there is no standardized library for testing and benchmarking RL algorithms on UAVs. In this paper, we introduce PyFlyt, a platform built on the Bullet physics engine with native Gymnasium API support. PyFlyt provides modular implementations of simple components, such as motors and lifting surfaces, allowing for the implementation of UAVs of arbitrary configurations. Additionally, PyFlyt includes various task definitions and multiple reward function settings for each vehicle type. We demonstrate the effectiveness of PyFlyt by training various RL agents for two UAV models: quadrotor and fixed-wing. Our findings highlight the effectiveness of RL in UAV control and planning, and further show that it is possible to train agents in sparse reward settings for UAVs. PyFlyt fills a gap in existing literature by providing a flexible and standardised platform for testing RL algorithms on UAVs. We believe that this will inspire more standardised research in this direction.

In recent years, the robotics community has witnessed the development of several software stacks for ground and articulated robots, such as Navigation2 and MoveIt. However, the same level of collaboration and standardization is yet to be achieved in the field of aerial robotics, where each research group has developed their own frameworks. This work presents Aerostack2, a framework for the development of autonomous aerial robotics systems that aims to address the lack of standardization and fragmentation of efforts in the field. Built on ROS 2 middleware and featuring an efficient modular software architecture and multi-robot orientation, Aerostack2 is a versatile and platform-independent environment that covers a wide range of robot capabilities for autonomous operation. Its major contributions include providing a logical level for specifying missions, reusing components and sub-systems for aerial robotics, and enabling the development of complete control architectures. All major contributions have been tested in simulation and real flights with multiple heterogeneous swarms. Aerostack2 is open source and community oriented, democratizing the access to its technology by autonomous drone systems developers.

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios including schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, such as flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches.