Ukraine's Air Force claimed it shot down 93 of 141 drones Russia launched in attacks overnight. The Air Force also said that 47 of the drones were "lost" while two returned to Russia. Russia said it destroyed 31 Ukrainian drones which had primarily targeted industrial sites in Russia's Tatarstan region, located about 1,000km (about 600 miles) from the Ukrainian border. No victims or damage have been reported. The governor of Russia's Bryansk region, Alexander Bogomaz, said 14 Ukrainian drones were neutralised in the region, which borders Ukraine.

For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major challenges, especially when dynamic robots must balance competing objectives like formation control and obstacle avoidance in the presence of stochastic dynamics and sensor uncertainty. In this paper, we propose a distributed, multi-agent receding-horizon feedback motion planning approach using Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC) that is able to reason about both model and measurement uncertainty to achieve robust multi-agent formation control while navigating cluttered obstacle fields and avoiding inter-robot collisions. Our approach relies not only on the underlying PAC-NMPC algorithm but also on a terminal cost-function derived from gyroscopic obstacle avoidance. Through numerical simulation, we show that our distributed approach performs on par with a centralized formulation, that it offers improved performance in the case of significant measurement noise, and that it can scale to more complex dynamical systems.

Multi-UAV air combat is a complex task involving multiple autonomous UAVs, an evolving field in both aerospace and artificial intelligence. This paper aims to enhance adversarial performance through collaborative strategies. Previous approaches predominantly discretize the action space into predefined actions, limiting UAV maneuverability and complex strategy implementation. Others simplify the problem to 1v1 combat, neglecting the cooperative dynamics among multiple UAVs. To address the high-dimensional challenges inherent in six-degree-of-freedom space and improve cooperation, we propose a hierarchical framework utilizing the Leader-Follower Multi-Agent Proximal Policy Optimization (LFMAPPO) strategy. Specifically, the framework is structured into three levels. The top level conducts a macro-level assessment of the environment and guides execution policy. The middle level determines the angle of the desired action. The bottom level generates precise action commands for the high-dimensional action space. Moreover, we optimize the state-value functions by assigning distinct roles with the leader-follower strategy to train the top-level policy, followers estimate the leader's utility, promoting effective cooperation among agents. Additionally, the incorporation of a target selector, aligned with the UAVs' posture, assesses the threat level of targets. Finally, simulation experiments validate the effectiveness of our proposed method.

Drones are increasingly used in forestry to capture high-resolution remote sensing data. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. Inside dense forests, reliance on the Global Navigation Satellite System (GNSS) for localization is not feasible. Additionally, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open-source methods and validating its performance for data collection inside forests. The autonomous flight capability was evaluated through multiple test flights in two boreal forest test sites. The tree parameter estimation capability was studied by conducting diameter at breast height (DBH) estimation using onboard stereo camera data and photogrammetric methods. The prototype conducted flights in selected challenging forest environments, and the experiments showed excellent performance in forest reconstruction with a miniaturized stereoscopic photogrammetric system. The stem detection algorithm managed to identify 79.31 % of the stems. The DBH estimation had a root mean square error (RMSE) of 3.33 cm (12.79 %) and a bias of 1.01 cm (3.87 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 cm (5.74 %), and the bias was 0.13 cm (0.64 %). When considering the overall performance in terms of DBH accuracy, autonomy, and forest complexity, the proposed approach was superior compared to methods proposed in the scientific literature. Results provided valuable insights into autonomous forest reconstruction using drones, and several further development topics were proposed.

In a series of calls with reporters on Monday morning, incoming Trump administration officials outlined dozens of executive orders the president-elect planned to take when he officially takes office, including 10 focused on what one official described as "common sense immigration policy". Officials said that Trump plans to end birthright citizenship, meaning that the children of undocumented migrants living in the US will no longer automatically be considered US citizens. Birthright citizenship, however, is enshrined in the US constitution and would require a two-thirds vote in both chambers of Congress to change. The official provided no further detail on how Trump plans to accomplish this. As part of the national emergency designation at the border, Trump will also direct the Department of Defense to "seal the border" and surge additional resources and personnel, including counter-drone capabilities.

--Egomotion estimation is crucial for applications such as autonomous navigation and robotics, where accurate and real-time motion tracking is required. However, traditional methods relying on inertial sensors are highly sensitive to external conditions, and suffer from drifts leading to large inaccuracies over long distances. Vision-based methods, particularly those util-ising event-based vision sensors, provide an efficient alternative by capturing data only when changes are perceived in the scene. In this work, we propose a fully event-based pipeline for egomotion estimation that processes the event stream directly within the event-based domain. This method eliminates the need for frame-based intermediaries, allowing for low-latency and energy-efficient motion estimation. We construct a shallow spiking neural network using a synaptic gating mechanism to convert precise event timing into bursts of spikes. These spikes encode local optical flow velocities, and the network provides an event-based readout of egomotion. We evaluate the network's performance on a dedicated chip, demonstrating strong potential for low-latency, low-power motion estimation. Additionally, simulations of larger networks show that the system achieves state-of-the-art accuracy in egomotion estimation tasks with event-based cameras, making it a promising solution for real-time, power-constrained robotics applications. The estimation of egomotion plays an important role in applications such as autonomous navigation, robotics and Augmented Reality (AR).

Amazon's drones won't be making any deliveries in the foreseeable future. According to Bloomberg, the company has paused all commercial drone deliveries in Texas and Arizona after a previously undisclosed event in which two of Amazon's MK30 drones had crashed at the Pendleton, Oregon airport it uses for testing. MK30 is the company's next-gen drone model, which is lighter and has a longer range than its predecessor, the MK27. The incidents took place in December, with one of the drones even catching fire after it fell. Amazon reportedly determined that its drones crashed due a software issue that's linked to the light rain drizzling at the time the tests were being conducted.

At least four people have been killed in a Russian drone and missile attack on the Ukrainian capital. Timur Tkachenko, head of Kyiv's military administration, said the attack at dawn on Saturday took place in the city's centrally located Shevchenkivskyi district. "Stay in shelters," said Kyiv Mayor Vitali Klitschko, in a post on Telegram on Saturday morning. He had warned of a "ballistic missile threat" against the capital hours earlier and said air defences were in operation around the city. Windows were shattered in Shevchenkivskyi district, including those at the entrance of the Lukianivska metro station, which was closed down, according to the mayor.

Three people have been killed in Kyiv overnight, according to the head of the Ukrainian military. Explosions were heard across the Ukrainian capital as air raid sirens sounded and air defence crews responded to a Russian ballistic missile attack. Ukraine has continued its drone attacks against Russian oil infrastructure, hitting depots in the Tula and Kaluga regions south of Moscow overnight. Images said to depict the burning depots have been shared online as Kaluga's regional governor claimed several drones had been shot down. The governors of Russia's Bryansk and Smolensk regions reported that air defence units had shot down a total of 14 Ukrainian drones with no reports of casualties.

UK Prime Minister Keir Starmer has signed a 100-year partnership agreement with Ukraine to provide support across various sectors, including healthcare and military technology, while pledging to provide security guarantees if an end to Russia's war comes. During Starmer's first visit to Kyiv since becoming prime minister, the British leader told a news conference on Thursday that the United Kingdom would examine "the practical ways to get a just and lasting peace … that guarantees your security, your independence and your right to choose your own future". "We will work with you and all of our allies on steps that would be robust enough to guarantee Ukraine's security," Starmer said. "Those conversations will continue for many months ahead." While Starmer was speaking with Ukrainian President Volodymyr Zelenskyy at the presidential palace, loud blasts and air raid sirens were heard over Kyiv as air defence systems took aim at a Russian drone attack. The British leader said the Russian attack served as a reminder of the situation on the ground.