Palm-sized autonomous nano-drones, i.e., sub-50g in weight, recently entered the drone racing scenario, where they are tasked to avoid obstacles and navigate as fast as possible through gates. However, in contrast with their bigger counterparts, i.e., kg-scale drones, nano-drones expose three orders of magnitude less onboard memory and compute power, demanding more efficient and lightweight vision-based pipelines to win the race. This work presents a map-free vision-based (using only a monocular camera) autonomous nano-drone that combines a real-time deep learning gate detection front-end with a classic yet elegant and effective visual servoing control back-end, only relying on onboard resources. Starting from two state-of-the-art tiny deep learning models, we adapt them for our specific task, and after a mixed simulator-real-world training, we integrate and deploy them aboard our nano-drone. Our best-performing pipeline costs of only 24M multiply-accumulate operations per frame, resulting in a closed-loop control performance of 30 Hz, while achieving a gate detection root mean square error of 1.4 pixels, on our ~20k real-world image dataset. In-field experiments highlight the capability of our nano-drone to successfully navigate through 15 gates in 4 min, never crashing and covering a total travel distance of ~100m, with a peak flight speed of 1.9 m/s. Finally, to stress the generalization capability of our system, we also test it in a never-seen-before environment, where it navigates through gates for more than 4 min.

Tehran, Iran – Iran's army and Islamic Revolutionary Guard Corps (IRGC) have been showcasing and testing new defensive and offensive weapons in large-scale military exercises for the past three months. The country is preparing for another tumultuous year amid threats by the United States and Israel to bomb Iranian nuclear facilities, critical energy infrastructure, and military sites. Iran is also promising a third iteration of its major military strikes on Israel, in retaliation for Israeli attacks amid the devastating war on Gaza. The exercises – Eqtedar, Zolfaqar and Great Prophet – have been held across Iran, the Sea of Oman and the northern Indian Ocean. The weapons tested show Iran intends to maintain its defiance of Israel and the West, refusing to negotiate with US President Donald Trump under his "maximum pressure" policy and continuing to advance its nuclear programme.

Animation shows traditional counter-drone technology vs. Epirus' Leonidas system that can take out entire swarms of drones at once. A new high-powered microwave system that can knock swarms of drones out of the sky at once is going to "touch every aspect of warfare," according to Epirus founder, Joe Lonsdale. "It's kind of like a Star Trek shield," Lonsdale, founder of Epirus and a co-founder of fast-rising defense technology company Palantir, explained of its Leonidas counter-drone system. "It's able to turn them off from very far away." "This is going to touch every aspect of warfare over the next decade," said Lonsdale.

We present a hierarchical safe auto-taxiing framework to enhance the automated ground operations of multiple unmanned aircraft systems (multi-UAS). The auto-taxiing problem becomes particularly challenging due to (i) unknown disturbances, such as crosswind affecting the aircraft dynamics, (ii) taxiway incursions due to unplanned obstacles, and (iii) spatiotemporal conflicts at the intersections between multiple entry points in the taxiway. To address these issues, we propose a hierarchical framework, i.e., SAFE-TAXI, combining centralized spatiotemporal planning with decentralized MPC-CBF-based control to safely navigate the aircraft through the taxiway while avoiding intersection conflicts and unplanned obstacles (e.g., other aircraft or ground vehicles). Our proposed framework decouples the auto-taxiing problem temporally into conflict resolution and motion planning, respectively. Conflict resolution is handled in a centralized manner by computing conflict-aware reference trajectories for each aircraft. In contrast, safety assurance from unplanned obstacles is handled by an MPC-CBF-based controller implemented in a decentralized manner. We demonstrate the effectiveness of our proposed framework through numerical simulations and experimentally validate it using Night Vapor, a small-scale fixed-wing test platform.

We offer a new in-depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost-efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)-Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR-Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR-Tools, assessing their computational effectiveness and real-world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q-Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets.

Autonomous visual navigation is an essential element in robot autonomy. Reinforcement learning (RL) offers a promising policy training paradigm. However existing RL methods suffer from high sample complexity, poor sim-to-real transfer, and limited runtime adaptability to navigation scenarios not seen during training. These problems are particularly challenging for drones, with complex nonlinear and unstable dynamics, and strong dynamic coupling between control and perception. In this paper, we propose a novel framework that integrates 3D Gaussian Splatting (3DGS) with differentiable deep reinforcement learning (DDRL) to train vision-based drone navigation policies. By leveraging high-fidelity 3D scene representations and differentiable simulation, our method improves sample efficiency and sim-to-real transfer. Additionally, we incorporate a Context-aided Estimator Network (CENet) to adapt to environmental variations at runtime. Moreover, by curriculum training in a mixture of different surrounding environments, we achieve in-task generalization, the ability to solve new instances of a task not seen during training. Drone hardware experiments demonstrate our method's high training efficiency compared to state-of-the-art RL methods, zero shot sim-to-real transfer for real robot deployment without fine tuning, and ability to adapt to new instances within the same task class (e.g. to fly through a gate at different locations with different distractors in the environment).

Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications (e.g., multimedia and Metaverse services) that demand high computational resources and strict quality of service (QoS) guarantees in terms of latency and task queue management. Given their limited energy and processing capabilities, IoTDs rely on UAVs and HAPS to offload tasks for distributed processing, forming a multi-tier MEC system. This paper tackles the overall energy minimization problem in MEC-enabled air-ground integrated networks (MAGIN) by jointly optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization is challenging due to the nonconvex, nonlinear nature of this hierarchical system, which renders traditional methods ineffective. We reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents, and propose a novel variant of multi-agent proximal policy optimization with a Beta distribution (MAPPO-BD) to solve it. Extensive simulations show that MAPPO-BD outperforms baseline schemes, achieving superior energy savings and efficient resource management in MAGIN while meeting queue delay and edge computing constraints.

Efficient and safe trajectory planning plays a critical role in the application of quadrotor unmanned aerial vehicles. Currently, the inherent trade-off between constraint compliance and computational efficiency enhancement in UAV trajectory optimization problems has not been sufficiently addressed. To enhance the performance of UAV trajectory optimization, we propose a spatial-temporal iterative optimization framework. Firstly, B-splines are utilized to represent UAV trajectories, with rigorous safety assurance achieved through strict enforcement of constraints on control points. Subsequently, a set of QP-LP subproblems via spatial-temporal decoupling and constraint linearization is derived. Finally, an iterative optimization strategy incorporating guidance gradients is employed to obtain high-performance UAV trajectories in different scenarios. Both simulation and real-world experimental results validate the efficiency and high-performance of the proposed optimization framework in generating safe and fast trajectories. Our source codes will be released for community reference at https://hitsz-mas.github.io/STORM

European Commission (EC) President Ursula von der Leyen has proposed a five-part plan to mobilise some 800bn euros ( 842bn) to beef up Europe's defence and provide "immediate" military support to Ukraine after the United States suspended aid. "A new era is upon us," the president said in a letter presenting the plan to 27 European Union (EU) leaders on Tuesday, two days before a summit aimed at cementing joint action on Ukraine and Europe's long-term security begins in Brussels. "Europe faces a clear and present danger on a scale that none of us has seen in our adult lifetime," she wrote. European leaders are under huge pressure to increase defence spending as US President Donald Trump's return to power has delivered a rude wake-up call that they cannot blindly rely on Washington. The joint borrowing would go towards building pan-European capability domains like air and missile defence, artillery systems, missiles and ammunition, drones and anti-drone systems or to address other needs from cyber- to military mobility, the EC said.

Ukraine's military said it destroyed 46 of 83 Russian drones launched at the country overnight. Another 31 drones were reportedly "lost" and failed to reach their targets. A drone attack on Ukraine's Black Sea port of Odesa damaged energy infrastructure in the area, triggering power and heat supply interruptions in the city, regional Governor Oleh Kiper wrote. Odesa's Mayor Gennadiy Trukhanov said the attack knocked out three boiler plants. A Ukrainian drone attack targeted an industrial enterprise in Russia's southern city of Syzran, the governor of the Samara region said on Tuesday, adding that there were no casualties.