Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in security applications, robust localization algorithms independent of GNSS are needed to provide dependable operations of autonomous UAVs also in interfered conditions. Typical non-GNSS visual localization approaches rely on known starting pose, work only on a small-sized map, or require known flight paths before a mission starts. We consider the problem of localization with no information on initial pose or planned flight path. We propose a solution for global visual localization on a map at scale up to 100 km2, based on matching orthoprojected UAV images to satellite imagery using learned season-invariant descriptors. We show that the method is able to determine heading, latitude and longitude of the UAV at 12.6-18.7 m lateral translation error in as few as 23.2-44.4 updates from an uninformed initialization, also in situations of significant seasonal appearance difference (winter-summer) between the UAV image and the map. We evaluate the characteristics of multiple neural network architectures for generating the descriptors, and likelihood estimation methods that are able to provide fast convergence and low localization error. We also evaluate the operation of the algorithm using real UAV data and evaluate running time on a real-time embedded platform. We believe this is the first work that is able to recover the pose of an UAV at this scale and rate of convergence, while allowing significant seasonal difference between camera observations and map.

The application of autonomous UAVs to infrastructure inspection tasks provides benefits in terms of operation time reduction, safety, and cost-effectiveness. This paper presents trajectory planning for three-dimensional autonomous multi-UAV volume coverage and visual inspection of infrastructure based on the Heat Equation Driven Area Coverage (HEDAC) algorithm. The method generates trajectories using a potential field and implements distance fields to prevent collisions and to determine UAVs' camera orientation. It successfully achieves coverage during the visual inspection of complex structures such as a wind turbine and a bridge, outperforming a state-of-the-art method by allowing more surface area to be inspected under the same conditions. The presented trajectory planning method offers flexibility in various setup parameters and is applicable to real-world inspection tasks. Conclusively, the proposed methodology could potentially be applied to different autonomous UAV tasks, or even utilized as a UAV motion control method if its computational efficiency is improved.

Ukraine's drone strikes on two air bases deep inside Russia mark a new chapter in this war, but their significance--whether they escalate the conflict or alter the war's course in some other way--is unclear. Much depends on Moscow's reaction, and Kyiv's response to that, in the next several days. For now, it's worth probing some possibilities, though first let's lay out the implications of these strikes, regardless of their consequences. The strikes followed several days of massive Russian air and missile attacks on Ukrainian civilian targets, mainly power plants, shutting off heat and electricity as Ukraine's winter is getting brutal. The Russians launched those attacks from the airfields that the Ukrainians subsequently hit.

State Department spokesman Ned Price will hold a briefing Tuesday afternoon after drones struck inside Russia's border with Ukraine in the second day of attacks. The briefing is scheduled to begin at 1:45 p.m. ET. Watch the event live in the player above. Ukrainian officials did not formally confirm carrying out drone strikes inside Russia, and they have maintained ambiguity over previous high-profile attacks. But Ukrainian presidential adviser Mikhail Podolyak taunted Moscow in comments on Twitter, and Britain's Defense Ministry said Russia was likely to consider the attacks on Russian bases more than 500 kilometers (300 miles) from the border with Ukraine as "some of the most strategically significant failures of force protection since its invasion of Ukraine."

A third Russian airfield is ablaze from a drone attack, a day after Ukraine demonstrated an apparent new ability to penetrate hundreds of kilometres deep into Russian airspace with attacks on two Russian air bases. A drone struck an airfield in the Russian region of Kursk bordering Ukraine, setting fire to an oil storage tank. Russia said three of its military personnel were killed in what it said were Ukrainian drone attacks on two Russian air bases hundreds of kilometres from the frontlines in Ukraine. Kyiv did not directly claim responsibility. Ukraine's military intelligence chief said Russia had enough high-precision missiles to conduct several more big air raids on Ukraine before it runs out of stock.

One evening in February last year, a 23-year-old Uber driver named Niradi Srikanth was getting ready to start another shift, ferrying passengers around the south Indian city of Hyderabad. He pointed the phone at his face to take a selfie to verify his identity. The process usually worked seamlessly. But this time he was unable to log in. Srikanth suspected it was because he had recently shaved his head.

A small drone descends from the skies and hovers in front of my face. The police are conducting routine checks in the neighborhood. I feel as if the drone's camera is drilling into me. I try to turn my back to it, but the drone follows me like a heat-seeking missile. It asks me to please put my hands up, and scans my face and body.

Warnings that Russia was planning a fresh wave of hits have been circulating for several days. They eventually arrived just hours after a series of explosions at two military airfields deep inside Russia, which Moscow blamed on Ukrainian drones intercepted by Russian air-defences.

As aerial robots are tasked to navigate environments of increased complexity, embedding collision tolerance in their design becomes important. In this survey we review the current state-of-the-art within the niche field of collision-tolerant micro aerial vehicles and present different design approaches identified in the literature, as well as methods that have focused on autonomy functionalities that exploit collision resilience. Subsequently, we discuss the relevance to biological systems and provide our view on key directions of future fruitful research.

The Age-of-Information (AoI) metric has been widely studied in the theoretical communication networks and queuing systems literature. However, experimental evaluation of its applicability to complex real-world time-sensitive systems is largely lacking. In this work, we develop, implement, and evaluate an AoI-based application layer middleware that enables the customization of WiFi networks to the needs of time-sensitive applications. By controlling the storage and flow of information in the underlying WiFi network, our middleware can: (i) prevent packet collisions; (ii) discard stale packets that are no longer useful; and (iii) dynamically prioritize the transmission of the most relevant information. To demonstrate the benefits of our middleware, we implement a mobility tracking application using a swarm of UAVs communicating with a central controller via WiFi. Our experimental results show that, when compared to WiFi-UDP/WiFi-TCP, the middleware can improve information freshness by a factor of 109x/48x and tracking accuracy by a factor of 4x/6x, respectively. Most importantly, our results also show that the performance gains of our approach increase as the system scales and/or the traffic load increases.