The ``Last Mile Challenge'' has long been considered an important, yet unsolved, challenge for autonomous vehicles, public service robots, and delivery robots. A central issue in this challenge is the ability of robots to navigate constrained and cluttered environments that have high agency (e.g., doorways, hallways, corridor intersections), often while competing for space with other robots and humans. We refer to these environments as ``Social Mini-Games'' (SMGs). Traditional navigation approaches designed for MRN do not perform well in SMGs, which has led to focused research on dedicated SMG solvers. However, publications on SMG navigation research make different assumptions (on centralized versus decentralized, observability, communication, cooperation, etc.), and have different objective functions (safety versus liveness). These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. Such ad-hoc representation of the field also presents a barrier to new researchers wanting to start research in this area. SMG navigation research requires its own taxonomy, definitions, and evaluation protocols to guide effective research moving forward. This survey is the first to catalog SMG solvers using a well-defined and unified taxonomy and to classify existing methods accordingly. It also discusses the essential properties of SMG solvers, defines what SMGs are and how they appear in practice, outlines how to evaluate SMG solvers, and highlights the differences between SMG solvers and general navigation systems. The survey concludes with an overview of future directions and open challenges in the field. Our project is open-sourced at https://socialminigames.github.io/.

The demand for lightweight and high-strength composite structures is rapidly growing in aerospace and robotics, particularly for optimized drone frames. However, conventional composite manufacturing methods struggle to achieve complex 3D architectures for weight savings and rely on assembling separate components, which introduce weak points at the joints. Additionally, maintaining continuous fiber reinforcement remains challenging, limiting structural efficiency. In this study, we demonstrate the lightweight Face Centered Cubic (FFC) lattice structured conceptualization of drone frames for weight reduction and complex topology fabrication through 3D Fiber Tethering (3DFiT) using continuous single tow fiber ensuring precise fiber alignment, eliminating weak points associated with traditional composite assembly. Mechanical testing demonstrates that the fabricated drone frame exhibits a high specific strength of around four to eight times the metal and thermoplastic, outperforming other conventional 3D printing methods. The drone frame weighs only 260 g, making it 10% lighter than the commercial DJI F450 frame, enhancing structural integrity and contributing to an extended flight time of three minutes, while flight testing confirms its stability and durability under operational conditions. The findings demonstrate the potential of single tow lattice truss-based drone frames, with 3DFiT serving as a scalable and efficient manufacturing method.

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Nato members are sending troops, artillery, and air defence systems to secure its eastern flank after what Poland called an unprecedented Russian drone incursion into its airspace. In the early hours of Wednesday, three Russian drones were shot down after crossing into Polish airspace. Other drones crashed to the ground and were later found across eastern Poland. Poland has requested a UN Security Council session about the incident, which will take place on Friday at 19:00 GMT. In response to the drone incursion, the Netherlands and the Czech Republic said they would send defences to Poland, while Lithuania would receive a German brigade and greater warning of Russian attacks on Ukraine that could cross over.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Poland's Prime Minister Donald Tusk has pledged to push ahead with a "great modernisation programme" for his country's military, a day after Polish and NATO forces shot down drones violating the country's airspace during a Russian aerial attack on neighbouring Ukraine. The Polish Air Navigation Services Agency announced on Thursday that Poland had introduced air traffic restrictions along its eastern borders with Belarus and Ukraine.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Russia's Putin hails war advances; Ukraine retakes parts of Donetsk John Psaropoulos is an independent journalist based in Athens and has been Al Jazeera's correspondent in Southeast Europe since 2012. Ukraine reclaimed 62sq km (24sq miles) of territory last month, its commander in chief revealed on Monday, contradicting Russian President Vladimir Putin's recent claim to be advancing "in all directions".

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Russian forces launched a "massive" attack across Ukraine, with 415 drones and 40 cruise and ballistic missiles, Ukrainian President Volodmyr Zelenskyy said in a post on X on Wednesday. Ukrainian forces destroyed more than 380 Russian drones, including at least 250 Shahed drones, Zelenskyy added in a later post.

-- This paper presents a methodology to predict metric depth from monocular RGB images and an inertial measurement unit (IMU). T o enable collision avoidance during autonomous flight, prior works either leverage heavy sensors (e.g., LiDARs or stereo cameras) or data-intensive and domain-specific fine-tuning of monocular metric depth estimation methods. In contrast, we propose several lightweight zero-shot rescaling strategies to obtain metric depth from relative depth estimates via the sparse 3D feature map created using a visual-inertial navigation system. These strategies are compared for their accuracy in diverse simulation environments. The best performing approach, which leverages monotonic spline fitting, is deployed in the real-world on a compute-constrained quadrotor . We obtain on-board metric depth estimates at 15 Hz and demonstrate successful collision avoidance after integrating the proposed method with a motion primitives-based planner . I. INTRODUCTION First Person View (FPV) drone pilots leverage a single forward-facing camera video stream transmitted over a radio feed and sensors embedded in the flight controller (e.g., IMU) to aggressively maneuver through dense clutter (e.g., through tree branches, under bridges, etc.).

Bio-inspired design is often used in autonomous UAV navigation due to the capacity of biological systems for flight and obstacle avoidance despite limited sensory and computational capabilities. In particular, honeybees mainly use the sensory input of optic flow, the apparent motion of objects in their visual field, to navigate cluttered environments. In our work, we train a Reinforcement Learning agent to navigate a tunnel with obstacles using only optic flow as sensory input. We inspect the attention patterns of trained agents to determine the regions of optic flow on which they primarily base their motor decisions. We find that agents trained in this way pay most attention to regions of discontinuity in optic flow, as well as regions with large optic flow magnitude. The trained agents appear to navigate a cluttered tunnel by avoiding the obstacles that produce large optic flow, while maintaining a centered position in their environment, which resembles the behavior seen in flying insects. This pattern persists across independently trained agents, which suggests that this could be a good strategy for developing a simple explicit control law for physical UAVs.

This paper introduces SI-EDTL, a two-stage swarm intelligence ensemble deep transfer learning model for detecting multiple vehicles in UAV images. It combines three pre-trained Faster R-CNN feature extractor models (InceptionV3, ResNet50, GoogLeNet) with five transfer classifiers (KNN, SVM, MLP, C4.5, Naïve Bayes), resulting in 15 different base learners. These are aggregated via weighted averaging to classify regions as Car, Van, Truck, Bus, or background. Hyperparameters are optimized with the whale optimization algorithm to balance accuracy, precision, and recall. Implemented in MATLAB R2020b with parallel processing, SI-EDTL outperforms existing methods on the AU-AIR UAV dataset.