Drone
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Conformal Koopman for Embedded Nonlinear Control with Statistical Robustness: Theory and Real-World Validation
2026-03-23 05:04:32Koki Hirano, Hiroyasu TsukamotoAbstract
We propose a fully data-driven, Koopman-based framework for statistically robust control of discrete-time nonlinear systems with linear embeddings. Establishing a connection between the Koopman operator and contraction theory, it offers distribution-free probabilistic bounds on the state tracking error under Koopman modeling uncertainty. Conformal prediction is employed here to rigorously derive a bound on the state-dependent modeling uncertainty throughout the trajectory, ensuring safety and robustness without assuming a specific error prediction structure or distribution. Unlike prior approaches that merely combine conformal prediction with Koopman-based control in an open-loop setting, our method establishes a closed-loop control architecture with formal guarantees that explicitly account for both forward and inverse modeling errors. Also, by expressing the tracking error bound in terms of the control parameters and the modeling errors, our framework offers a quantitative means to formally enhance the performance of arbitrary Koopman-based control. We validate our method both in numerical simulations with the Dubins car and in real-world experiments with a highly nonlinear flapping-wing drone. The results demonstrate that our method indeed provides formal safety guarantees while maintaining accurate tracking performance under Koopman modeling uncertainty.
Abstract (translated)
本文提出了一种完全数据驱动、基于Koopman算子的框架，用于对具有线性嵌入的离散时间非线性系统进行统计鲁棒控制。通过建立Koopman算子与收缩理论之间的联系，该框架在Koopman建模不确定性下提供了分布无关的状态跟踪误差概率边界。本文采用共形预测方法，严格推导了沿轨迹的状态相关建模误差边界，无需假设特定的误差预测结构或分布即可确保安全性与鲁棒性。与以往仅以开环方式结合共形预测与基于Koopman的控制方法不同，本文方法建立了具有形式化保证的闭环控制架构，明确考虑了前向与逆建模误差。此外，通过将跟踪误差边界表示为控制参数与建模误差的函数，本框架为定量提升任意基于Koopman的控制性能提供了形式化手段。我们在杜宾斯车模型的数值仿真以及高度非线性的扑翼无人机实物实验中验证了该方法。结果表明，在Koopman建模不确定性下，本文方法确实能提供形式化安全保证，同时保持精确的跟踪性能。
URL
https://arxiv.org/abs/2603.21580
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Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning
2026-03-21 23:57:33Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar, Charles Kamhoua, Munindar P. SinghAbstract
Unmanned Aerial Vehicles (UAVs) are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Denial-of-Service (DoS) attacks to overwhelm the resources of mission drones (MDs). How can we defend UAV mission systems against DoS attacks? We adopt cyber deception as a defense strategy, in which honey drones (HDs) are proposed to bait and divert attacks. The attack and deceptive defense hinge upon radio signal strength: The attacker selects victim MDs based on their signals, and HDs attract the attacker from afar by emitting stronger signals, despite this reducing battery life. We formulate an optimization problem for the attacker and defender to identify their respective strategies for maximizing mission performance while minimizing energy consumption. To address this problem, we propose a novel approach, called HT-DRL. HT-DRL identifies optimal solutions without a long learning convergence time by taking the solutions of hypergame theory into the neural network of deep reinforcement learning. This achieves a systematic way to intelligently deceive attackers. We analyze the performance of diverse defense mechanisms under different attack strategies. Further, the HT-DRL-based HD approach outperforms existing non-HD counterparts up to two times better in mission performance while incurring low energy consumption.
Abstract (translated)
无人机（UAVs）在监视、救援或投送等任务关键型系统中具有重要价值。此类系统自然会招致网络攻击，包括旨在耗尽任务无人机（MDs）资源的拒绝服务（DoS）攻击。我们如何防御无人机任务系统免受DoS攻击？本研究采用网络欺骗作为防御策略，通过部署蜜罐无人机（HDs）引诱并转移攻击。攻击行为与欺骗防御均依赖于无线电信号强度：攻击者依据信号强度选择受害的任务无人机，而蜜罐无人机则通过发射更强信号从远处吸引攻击者，尽管这会缩短电池寿命。我们构建了一个优化问题，用于确定攻击方与防御方各自在最大化任务性能的同时最小化能耗的最优策略。针对该问题，我们提出一种名为HT-DRL（超博弈理论与深度强化学习结合）的新方法。HT-DRL通过将超博弈理论的解融入深度强化学习的神经网络，无需长时间学习收敛即可识别最优解，从而实现系统化智能欺骗攻击者的方法。我们分析了不同攻击策略下多种防御机制的性能。此外，基于HT-DRL的蜜罐方案在任务性能上比现有非蜜罐方案提升近两倍，同时保持低能耗。
URL
https://arxiv.org/abs/2603.20981
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Bounded Coupled AI Learning Dynamics in Tri-Hierarchical Drone Swarms
2026-03-20 07:23:32Oleksii BychkovAbstract
Modern autonomous multi-agent systems combine heterogeneous learning mechanisms operating at different timescales. An open question remains: can one formally guarantee that coupled dynamics of such mechanisms stay within the admissible operational regime? This paper studies a tri-hierarchical swarm learning system where three mechanisms act simultaneously: (1) local Hebbian online learning at individual agent level (fast timescale, 10-100 ms); (2) multi-agent reinforcement learning (MARL) for tactical group coordination (medium timescale, 1-10 s); (3) meta-learning (MAML) for strategic adaptation (slow timescale, 10-100 s). Four results are established. The Bounded Total Error Theorem shows that under contractual constraints on learning rates, Lipschitz continuity of inter-level mappings, and weight stabilization, total suboptimality admits a component-wise upper bound uniform in time. The Bounded Representation Drift Theorem gives a worst-case estimate of how Hebbian updates affect coordination-level embeddings during one MARL cycle. The Meta-Level Compatibility Theorem provides sufficient conditions under which strategic adaptation preserves lower-level invariants. The Non-Accumulation Theorem proves that error does not grow unboundedly over time.
Abstract (translated)
URL
https://arxiv.org/abs/2603.20333
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Student views in AI Ethics and Social Impact
2026-03-19 12:23:14Tudor-Dan Mihoc, Manuela-Andreea Petrescu, Emilia-Loredana PopAbstract
An investigation, from a gender perspective, of how students view the ethical implications and societal effects of artificial intelligence is conducted, examining concepts that could have a big influence on how artificial intelligence may be taught in the future. For this, we conducted a survey on a cohort of 230 second year computer science students to reveal their opinions. The results revealed that AI, from the students' perspective, will significantly impact daily life, particularly in areas such as medicine, education, or media. Men are more aware of potential changes in Computer Science, autonomous driving, image and video processing, and chatbot usage, while women mention more the impact on social media. Both men and women perceive potential threats in the same manner, with men more aware of war, AI controlled drones, terrain recognition, and information war. Women seem to have a stronger tendency towards ethical considerations and helping others.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18827
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From Noise to Signal: When Outliers Seed New Topics
2026-03-18 23:54:18Evangelia Zve, Gauvain Bourgne, Benjamin Icard, Jean-Gabriel GanasciaAbstract
Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduce a temporal taxonomy of news-document trajectories that defines how documents relate to topic formation over time. It distinguishes anticipatory outliers, which precede the topics they later join, from documents that either reinforce existing topics or remain isolated. By capturing these trajectories, the taxonomy links weak-signal detection with temporal topic modeling and clarifies how individual articles anticipate, initiate, or drift within evolving clusters. We implement it in a cumulative clustering setting using document embeddings from eleven state-of-the-art language models and evaluate it retrospectively on HydroNewsFr, a French news corpus on the hydrogen economy. Inter-model agreement reveals a small, high-consensus subset of anticipatory outliers, increasing confidence in these labels. Qualitative case studies further illustrate these trajectories through concrete topic developments.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18358
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AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse
2026-03-18 07:45:09Aayam Bansal, Ishaan GangwaniAbstract
Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real-world deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm-Bench, a benchmark suite and evaluation protocol that systematically stress-tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm-Bench spans three task families: cooperative perception, multi-agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness-aware fusion. Our experiments reveal that communication-dependent tasks degrade catastrophically: stale memory and bandwidth collapse cause over 96% performance drops in navigation, while content corruption (stale or conflicting data) reduces perception F1 by over 85%. Vulnerability depends on the interaction between impairment type and task design; perception fusion is robust to packet loss but amplifies corrupted data. Redundant message coding more than doubles navigation performance under 80% packet loss. We release AgentComm-Bench as a practical evaluation protocol and recommend that cooperative embodied AI work report performance under multiple impairment conditions.
Abstract (translated)
具身AI的多智能体协作方法几乎普遍在理想化通信条件下进行评估：零延迟、无丢包、无限带宽。然而，机器人在无线链路下的实际部署、拥堵网络中的自动驾驶车辆，或 contested spectrum 中的无人机集群都无法提供此类保障。我们推出 AgentComm-Bench 基准套件与评估协议，该系统在六个通信受损维度下对协作式具身AI进行系统性压力测试：延迟、丢包率、带宽崩溃、异步更新、陈旧记忆与冲突传感器证据。AgentComm-Bench 涵盖三类任务家族：协作感知、多智能体航点导航与协作区域搜索，并评估五种通信策略，其中包括我们提出的一种基于冗余消息编码与感知陈旧性的融合轻量级方法。实验表明，依赖通信的任务性能急剧下降：陈旧记忆与带宽崩溃导致导航性能下降超过96%，而内容损坏（陈旧或冲突数据）使感知F1值降低超过85%。脆弱性取决于受损类型与任务设计的交互——感知融合对丢包具有鲁棒性，但会放大损坏数据的影响。在80%丢包率下，冗余消息编码使导航性能提升超两倍。我们发布 AgentComm-Bench 作为实用评估协议，并建议协作式具身AI研究应报告多种通信受损条件下的性能表现。
URL
https://arxiv.org/abs/2603.20285
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Agile Interception of a Flying Target using Competitive Reinforcement Learning
2026-03-17 09:11:23Timoth\'ee Gavin (ENAC-LAB, LAAS-RIS), Simon Lacroix (LAAS-RIS), Murat Bronz (ENAC)Abstract
This article presents a solution to intercept an agile drone by another agile drone carrying a catching net. We formulate the interception as a Competitive Reinforcement Learning problem, where the interceptor and the target drone are controlled by separate policies trained with Proximal Policy Optimization (PPO). We introduce a high-fidelity simulation environment that integrates a realistic quadrotor dynamics model and a low-level control architecture implemented in JAX, which allows for fast parallelized execution on GPUs. We train the agents using low-level control, collective thrust and body rates, to achieve agile flights both for the interceptor and the target. We compare the performance of the trained policies in terms of catch rate, time to catch, and crash rate, against common heuristic baselines and show that our solution outperforms these baselines for interception of agile targets. Finally, we demonstrate the performance of the trained policies in a scaled real-world scenario using agile drones inside an indoor flight arena.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16279
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A PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression
2026-03-16 14:40:48Yuming Han, Jooho Kim, Anish ShakyaAbstract
Existing remote sensing image compression methods still explore to balance high compression efficiency with the preservation of fine details and task-relevant information. Meanwhile, high-resolution drone imagery offers valuable structural details for urban monitoring and disaster assessment, but large-area datasets can easily reach hundreds of gigabytes, creating significant challenges for storage and long-term management. In this paper, we propose a PPO-based bitrate allocation Conditional Diffusion Compression (PCDC) framework. PCDC integrates a conditional diffusion decoder with a PPO-based block-wise bitrate allocation strategy to achieve high compression ratios while maintaining strong perceptual performance. We also release a high-resolution drone image dataset with richer structural details at a consistent low altitude over residential neighborhoods in coastal urban areas. Experimental results show compression ratios of 19.3x on DIV2K and 21.2x on the drone image dataset. Moreover, downstream object detection experiments demonstrate that the reconstructed images preserve task-relevant information with negligible performance loss.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15365
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From Horizontal to Rotated: Cross-View Object Geo-Localization with Orientation Awareness
2026-03-16 05:56:06Chenlin Fu, Ao Gong, Yingying ZhuAbstract
Cross-View object geo-localization (CVOGL) aims to precisely determine the geographic coordinates of a query object from a ground or drone perspective by referencing a satellite map. Segmentation-based approaches offer high precision but require prohibitively expensive pixel-level annotations, whereas more economical detection-based methods suffer from lower accuracy. This performance disparity in detection is primarily caused by two factors: the poor geometric fit of Horizontal Bounding Boxes (HBoxes) for oriented objects and the degradation in precision due to feature map scaling. Motivated by these, we propose leveraging Rotated Bounding Boxes (RBoxes) as a natural extension of the detection-based paradigm. RBoxes provide a much tighter geometric fit to oriented objects. Building on this, we introduce OSGeo, a novel geo-localization framework, meticulously designed with a multi-scale perception module and an orientation-sensitive head to accurately regress RBoxes. To support this scheme, we also construct and release CVOGL-R, the first dataset with precise RBox annotations for CVOGL. Extensive experiments demonstrate that our OSGeo achieves state-of-the-art performance, consistently matching or even surpassing the accuracy of leading segmentation-based methods but with an annotation cost that is over an order of magnitude lower.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14856
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UAVBench and UAVIT-1M: Benchmarking and Enhancing MLLMs for Low-Altitude UAV Vision-Language Understanding
Abstract
Multimodal Large Language Models (MLLMs) have made significant strides in natural images and satellite remote sensing images. However, understanding low-altitude drone scenarios remains a challenge. Existing datasets primarily focus on a few specific low-altitude visual tasks, which cannot fully assess the ability of MLLMs in real-world low-altitude UAV applications. Therefore, we introduce UAVBench, a comprehensive benchmark, and UAVIT-1M, a large-scale instruction tuning dataset, designed to evaluate and improve MLLMs' abilities in low-altitude vision-language tasks. UAVBench comprises 43 test units and 966k high-quality data samples across 10 tasks at the image-level and region-level. UAVIT-1M consists of approximately 1.24 million diverse instructions, covering 789k multi-scene images and about 2,000 types of spatial resolutions with 11 distinct tasks. UAVBench and UAVIT-1M feature pure real-world visual images and rich weather conditions, and involve manual verification to ensure high quality. Our in-depth analysis of 11 state-of-the-art MLLMs using UAVBench reveals that open-source MLLMs cannot generate accurate conversations about low-altitude visual content, lagging behind closed-source MLLMs. Extensive experiments demonstrate that fine-tuning open-source MLLMs on UAVIT-1M significantly addresses this gap. Our contributions pave the way for bridging the gap between current MLLMs and low-altitude UAV real-world application demands. (Project page: this https URL)
Abstract (translated)
URL
https://arxiv.org/abs/2603.14336
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AeroGen: Agentic Drone Autonomy through Single-Shot Structured Prompting & Drone SDK
Abstract
Designing correct UAV autonomy programs is challenging due to joint navigation, sensing and analytics requirements. While LLMs can generate code, their reliability for safety-critical UAVs remains uncertain. This paper presents AeroGen, an open-loop framework that enables consistently correct single-shot AI-generated drone control programs through structured guardrail prompting and integration with the AeroDaaS drone SDK. AeroGen encodes API descriptions, flight constraints and operational world rules directly into the system context prompt, enabling generic LLMs to produce constraint-aware code from user prompts, with minimal example code. We evaluate AeroGen across a diverse benchmark of 20 navigation tasks and 5 drone missions on urban, farm and inspection environments, using both imperative and declarative user prompts. AeroGen generates about 40 lines of AeroDaaS Python code in about 20s per mission, in both real-world and simulations, showing that structured prompting with a well-defined SDK improves robustness, correctness and deployability of LLM-generated drone autonomy programs.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14236
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VFM-Loc: Zero-Shot Cross-View Geo-Localization via Aligning Discriminative Visual Hierarchies
2026-03-14 09:23:59Jun Lu, Zehao Sang, Haoqi Wei, Xiangyun Liu, Kun Zhu, Haitao Guo, Zhihui Gong, Lei DingAbstract
Cross-View Geo-Localization (CVGL) in remote sensing aims to locate a drone-view query by matching it to geo-tagged satellite images. Although supervised methods have achieved strong results on closeset benchmarks, they often fail to generalize to unconstrained, real-world scenarios due to severe viewpoint differences and dataset bias. To overcome these limitations, we present VFM-Loc, a training-free framework for zero-shot CVGL that leverages the generalizable visual representations from vision foundational models (VFMs). VFM-Loc identifies and matches discriminative visual clues across different viewpoints through a progressive alignment strategy. First, we design a hierarchical clue extraction mechanism using Generalized Mean pooling and Scale-Weighted RMAC to preserve distinctive visual clues across scales while maintaining hierarchical confidence. Second, we introduce a statistical manifold alignment pipeline based on domain-wise PCA and Orthogonal Procrustes analysis, linearly aligning heterogeneous feature distributions in a shared metric space. Experiments demonstrate that VFM-Loc exhibits strong zero-shot accuracy on standard benchmarks and surpasses supervised methods by over 20% in Recall@1 on the challenging LO-UCV dataset with large oblique angles. This work highlights that principled alignment of pre-trained features can effectively bridge the cross-view gap, establishing a robust and training-free paradigm for real-world CVGL. The relevant code is made available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13855
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Rooftop Wind Field Reconstruction Using Sparse Sensors: From Deterministic to Generative Learning Methods
2026-03-13 15:23:18Yihang Zhou, Chao Lin, Hideki Kikumoto, Ryozo Ooka, Sibo ChengAbstract
Real-time rooftop wind-speed distribution is important for the safe operation of drones and urban air mobility systems, wind control systems, and rooftop utilization. However, rooftop flows show strong nonlinearity, separation, and cross-direction variability, which make flow field reconstruction from sparse sensors difficult. This study develops a learning-from-observation framework using wind-tunnel experimental data obtained by Particle Image Velocimetry (PIV) and compares Kriging interpolation with three deep learning models: UNet, Vision Transformer Autoencoder (ViTAE), and Conditional Wasserstein GAN (CWGAN). We evaluate two training strategies, single wind-direction training (SDT) and mixed wind-direction training (MDT), across sensor densities from 5 to 30, test robustness under sensor position perturbations of plus or minus 1 grid, and optimize sensor placement via Proper Orthogonal Decomposition with QR decomposition. Results show that deep learning methods can reconstruct rooftop wind fields from sparse sensor data effectively. Compared with Kriging interpolation, the deep learning models improved SSIM by up to 32.7%, FAC2 by 24.2%, and NMSE by 27.8%. Mixed wind-direction training further improved performance, with gains of up to 173.7% in SSIM, 16.7% in FAC2, and 98.3% in MG compared with single-direction training. The results also show that sensor configuration, optimization, and training strategy should be considered jointly for reliable deployment. QR-based optimization improved robustness by up to 27.8% under sensor perturbations, although with metric-dependent trade-offs. Training on experimental rather than simulated data also provides practical guidance for method selection and sensor placement in different scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13077
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UE5-Forest: A Photorealistic Synthetic Stereo Dataset for UAV Forestry Depth Estimation
2026-03-13 04:27:24Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard GreenAbstract
Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-based pruning. We present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5). One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes and captured by a simulated stereo rig whose intrinsics -- 63 mm baseline, 2.8 mm focal length, 3.84 mm sensor width -- replicate the ZED Mini camera mounted on our drone. Orbiting each tree at up to 2 m across three elevation bands (horizontal, +45 degrees, -45 degrees) yields 5,520 rectified 1920 x 1080 stereo pairs with pixel-perfect disparity labels. We provide a statistical characterisation of the dataset -- covering disparity distributions, scene diversity, and visual fidelity -- and a qualitative comparison with real-world Canterbury Tree Branches imagery that confirms the photorealistic quality and geometric plausibility of the rendered data. The dataset will be publicly released to provide the community with a ready-to-use benchmark and training resource for stereo-based forestry depth estimation.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15304
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Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors
Abstract
Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited and highly experimental forms: (i) hybrid workflows where an embedded device performs sensing and classical processing while offloading a narrowly scoped quantum subroutine to a remote quantum processing unit (QPU) or nearby quantum appliance, and (ii) early-stage "embedded QPU" concepts in which a compact quantum co-processor is integrated with classical control hardware. A practical bridge is quantum-inspired machine learning and optimisation on classical embedded processors and FPGAs. This paper analyses feasibility from a circuits-and-systems perspective aligned with the academic community, formalises two implementation pathways, identifies the dominant barriers (latency, data encoding overhead, NISQ noise, tooling mismatch, and energy), and maps them to concrete engineering directions in interface design, control electronics, power management, verification, and security. We also argue that responsible deployment requires adversarial evaluation and governance practices that are increasingly necessary for edge AI systems.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12540
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Increasing intelligence in AI agents can worsen collective outcomes
2026-03-12 16:31:28Neil F. JohnsonAbstract
When resources are scarce, will a population of AI agents coordinate in harmony, or descend into tribal chaos? Diverse decision-making AI from different developers is entering everyday devices -- from phones and medical devices to battlefield drones and cars -- and these AI agents typically compete for finite shared resources such as charging slots, relay bandwidth, and traffic priority. Yet their collective dynamics and hence risks to users and society are poorly understood. Here we study AI-agent populations as the first system of real agents in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. We show empirically and mathematically that when resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. Meanwhile, some individuals profit handsomely. When resources are abundant, the same ingredients drive overload to near zero, though tribe formation makes the overload slightly worse. The crossover is arithmetical: it is where opposing tribes that form spontaneously first fit inside the available capacity. More sophisticated AI-agent populations are not better: whether their sophistication helps or harms depends entirely on a single number -- the capacity-to-population ratio -- that is knowable before any AI-agent ships.
Abstract (translated)
当资源稀缺时，一群人工智能代理会协调合作还是陷入部落式的混乱？来自不同开发商的多种决策AI正进入日常设备——从手机和医疗设备到战场无人机和汽车——这些AI代理通常为有限共享资源（如充电插口、中继带宽和交通优先级）而竞争。然而，它们集体行为的动力学以及对用户和社会的风险尚不明确。 本文研究了一群AI代理作为首个系统，其中四个决定集体行为的关键变量可以独立调整：天性（固有的大型语言模型多样性）、教养（个体强化学习）、文化（自发部落形成），和资源稀缺性。我们通过实证和数学方法证明，在资源稀缺的情况下，AI模型的多样性和强化学习会增加危险的系统过载风险，尽管部落形成能减轻这种风险。与此同时，一些个体能够从中获利颇丰。当资源充足时，相同的因素将过载降至几乎为零，尽管部落形成使这一状况略有恶化。 转折点是算术式的：它是在自发形成的对立部落第一次适应可用容量的地方。更复杂的AI代理群体并不一定更好：它们是否有助于或损害取决于一个单一数字——即容量与人口比例，在任何AI代理问世之前都是已知的。
URL
https://arxiv.org/abs/2603.12129
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Flight through Narrow Gaps with Morphing-Wing Drones
2026-03-12 15:27:33Julius Wanner, Hoang-Vu Phan, Charbel Toumieh, Dario FloreanoAbstract
The size of a narrow gap traversable by a fixed-wing drone is limited by its wingspan. Inspired by birds, here, we enable the traversal of a gap of sub-wingspan width and height using a morphing-wing drone capable of temporarily sweeping in its wings mid-flight. This maneuver poses control challenges due to sudden lift loss during gap-passage at low flight speeds and the need for precisely timed wing-sweep actuation ahead of the gap. To address these challenges, we first develop an aerodynamic model for general wing-sweep morphing drone flight including low flight speeds and post-stall angles of attack. We integrate longitudinal drone dynamics into an optimal reference trajectory generation and Nonlinear Model Predictive Control framework with runtime adaptive costs and constraints. Validated on a 130 g wing-sweep-morphing drone, our method achieves an average altitude error of 5 cm during narrow-gap passage at forward speeds between 5 and 7 m/s, whilst enforcing fully swept wings near the gap across variable threshold distances. Trajectory analysis shows that the drone can compensate for lift loss during gap-passage by accelerating and pitching upwards ahead of the gap to an extent that differs between reference trajectory optimization objectives. We show that our strategy also allows for accurate gap passage on hardware whilst maintaining a constant forward flight speed reference and near-constant altitude.
Abstract (translated)
固定翼无人机可通过的狭窄缝隙尺寸受其翼展限制。受鸟类启发，这里我们开发了一种能够在飞行过程中暂时收起机翼以穿越宽度和高度均小于翼展的缝隙的变形机翼无人机。这种操作在低速通过缝隙时由于突然失去升力而带来了控制挑战，并且需要在接近缝隙前精确地调整机翼展开时间。为应对这些挑战，我们首先开发了一种适用于一般翼展变化无人机飞行的气动模型，包括低速和失速迎角的情况。 我们将纵向无人机动力学集成到一个具有运行时自适应成本和约束条件的最佳参考轨迹生成及非线性模型预测控制框架中。在一台130克重、具备翼展变形能力的无人机上进行验证后，我们的方法能够在5至7米/秒的前向速度下穿过狭窄缝隙时实现平均高度误差为5厘米的目标，并且在整个过程中保持机翼完全收起状态，同时跨越不同阈值距离。 轨迹分析表明，无人机能够通过在接近缝隙之前加速并向上调整姿态来补偿飞行中失去的升力，具体补偿程度取决于参考轨迹优化目标的不同。我们还展示了，在硬件上以恒定前向速度和几乎恒定高度的情况下，我们的策略同样允许准确地穿越狭窄缝隙。
URL
https://arxiv.org/abs/2603.12059
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Lightweight 3D LiDAR-Based UAV Tracking: An Adaptive Extended Kalman Filtering Approach
2026-03-10 15:19:34Nivand Khosravi, Meysam Basiri, Rodrigo VenturaAbstract
Accurate relative positioning is crucial for swarm aerial robotics, enabling coordinated flight and collision avoidance. Although vision-based tracking has been extensively studied, 3D LiDAR-based methods remain underutilized despite their robustness under varying lighting conditions. Existing systems often rely on bulky, power-intensive sensors, making them impractical for small UAVs with strict payload and energy constraints. This paper presents a lightweight LiDAR-based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework. Our approach effectively addresses the challenges posed by sparse, noisy, and nonuniform point cloud data generated by non-repetitive scanning 3D LiDARs, ensuring reliable tracking while remaining suitable for small drones with strict payload constraints. Unlike conventional filtering techniques, the proposed method dynamically adjusts the noise covariance matrices using innovation and residual statistics, thereby enhancing tracking accuracy under real-world conditions. Additionally, a recovery mechanism ensures continuity of tracking during temporary detection failures caused by scattered LiDAR returns or occlusions. Experimental validation was performed using a Livox Mid-360 LiDAR mounted on a DJI F550 UAV in real-world flight scenarios. The proposed method demonstrated robust UAV tracking performance under sparse LiDAR returns and intermittent detections, consistently outperforming both standard Kalman filtering and particle filtering approaches during aggressive maneuvers. These results confirm that the framework enables reliable relative positioning in GPS-denied environments without the need for multi-sensor arrays or external infrastructure.
Abstract (translated)
精确的相对定位对于集群无人机至关重要，它能够实现协调飞行和碰撞避免。尽管基于视觉的追踪技术已经得到了广泛研究，但由于其在不同光照条件下表现出的强大鲁棒性，3D 激光雷达（LiDAR）方法仍被很大程度上忽视。现有的系统常常依赖于体积庞大且耗电量高的传感器，这使得它们对于受到负载和能量限制的小型无人机来说并不实用。本文提出了一种轻量级的基于 LiDAR 的无人机追踪系统，该系统采用了自适应扩展卡尔曼滤波（AEKF）框架。我们的方法有效地解决了由非重复扫描 3D 激光雷达产生的稀疏、噪声大且分布不均点云数据带来的挑战，确保了可靠的追踪，并适用于严格负载限制的小型无人机。 与传统的滤波技术不同，所提出的方法能够根据创新和残差统计动态调整噪声协方差矩阵，从而在实际条件下提高追踪精度。此外，恢复机制保证了在激光雷达回波分散或遮挡导致的临时检测失败期间跟踪的连续性。 实验验证是在真实的飞行场景中通过将Livox Mid-360 激光雷达安装在 DJI F550 无人机上来完成的。所提出的方法显示出了出色的无人机追踪性能，在稀疏激光雷达返回和间歇性检测下依然稳健，且在剧烈机动期间持续优于标准卡尔曼滤波器和粒子滤波方法。 这些结果证实了该框架能够在无需多传感器阵列或外部基础设施的情况下为 GPS 信号缺失的环境提供可靠的相对定位。
URL
https://arxiv.org/abs/2603.09783
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WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors
2026-03-10 05:09:43Khuzema Habib, Pranav Deshakulkarni Manjunath, Kasra Torshizi, Troi Williams, Pratap TokekarAbstract
Local wind conditions strongly influence drone performance: headwinds increase flight time, crosswinds and wind shear hinder agility in cluttered spaces, while tailwinds reduce travel time. Although adaptive controllers can mitigate turbulence, they remain unaware of the surrounding geometry that generates it, preventing proactive avoidance. Existing methods that model how wind interacts with the environment typically rely on computationally expensive fluid dynamics simulations, limiting real-time adaptation to new environments and conditions. To bridge this gap, we present WESPR, a fast framework that predicts how environmental geometry affects local wind conditions, enabling proactive path planning and control adaptation. Our lightweight pipeline integrates geometric perception and local weather data to estimate wind fields, compute cost-efficient paths, and adjust control strategies-all within 10 seconds. We validate WESPR on a Crazyflie drone navigating turbulent obstacle courses. Our results show a 12.5-58.7% reduction in maximum trajectory deviation and a 24.6% improvement in stability compared to a wind-agnostic adaptive controller.
Abstract (translated)
本地风况强烈影响无人机性能：逆风会增加飞行时间，侧风和风切变则在复杂环境中妨碍灵活性，而顺风则缩短了旅行时间。尽管自适应控制器可以减轻湍流的影响，但它们无法感知生成湍流的周围几何结构，从而无法进行主动避让。目前，模拟风与环境相互作用的方法通常依赖于计算成本高昂的流体动力学仿真，这限制了实时适应新环境和条件的能力。为填补这一空白，我们推出了WESPR（Wind Environment Simulation and Prediction for Robotics），这是一个快速框架，用于预测环境几何结构如何影响局部风况，从而实现主动路径规划和控制策略调整。 我们的轻量级流程结合了几何感知和本地气象数据来估算风场、计算成本效益的路径，并调整控制策略——所有这些都在10秒内完成。我们在Crazyflie无人机导航湍流障碍赛道上验证了WESPR的效果。结果表明，与没有考虑风况的自适应控制器相比，使用WESPR后轨迹最大偏差减少了12.5%至58.7%，稳定性提高了24.6%。
URL
https://arxiv.org/abs/2603.09194
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ImpedanceDiffusion: Diffusion-Based Global Path Planning for UAV Swarm Navigation with Generative Impedance Control
2026-03-10 00:06:55Faryal Batool, Yasheerah Yaqoot, Muhammad Ahsan Mustafa, Roohan Ahmed Khan, Aleksey Fedoseev, Dzmitry TsetserukouAbstract
Safe swarm navigation in cluttered indoor environment requires long-horizon planning, reactive obstacle avoidance, and adaptive compliance. We propose ImpedanceDiffusion, a hierarchical framework that leverages image-conditioned diffusion-based global path planning with Artificial Potential Field (APF) tracking and semantic-aware variable impedance control for aerial drone swarms. The diffusion model generates geometric global trajectories directly from RGB images without explicit map construction. These trajectories are tracked by an APF-based reactive layer, while a VLM-RAG module performs semantic obstacle classification with 90% retrieval accuracy to adapt impedance parameters for mixed obstacle environments during execution. Two diffusion planners are evaluated: (i) a top-view long-horizon planner using single-pass inference and (ii) a first-person-view (FPV) short-horizon planner deployed via a two-stage inference pipeline. Both planners achieve a 100% trajectory generation rate across twenty static and dynamic experimental configurations and are validated via zero-shot sim-to-real deployment on Crazyflie 2.1 drones through the hierarchical APF-impedance control stack. The top-view planner produces smoother trajectories that yield conservative tracking speeds of 1.0-1.2 m/s near hard obstacles and 0.6-1.0 m/s near soft obstacles. In contrast, the FPV planner generates trajectories with greater local clearance and typically higher speeds, reaching 1.4-2.0 m/s near hard obstacles and up to 1.6 m/s near soft obstacles. Across 20 experimental configurations (100 total runs), the framework achieved a 92% success rate while maintaining stable impedance-based formation control with bounded oscillations and no in-flight collisions, demonstrating reliable and adaptive swarm navigation in cluttered indoor environments.
Abstract (translated)
在拥挤的室内环境中进行安全的无人机群导航需要长期规划、反应式避障和自适应顺应控制。我们提出了ImpedanceDiffusion，这是一种分层框架，它结合了基于图像条件扩散模型的全局路径规划以及人工势场（APF）跟踪与语义感知变阻抗控制，专门用于空中无人机群。 该扩散模型可以直接从RGB图像生成几何全局轨迹，无需显式构建地图。这些轨迹由一个基于APF的反应层进行追踪，同时VLM-RAG模块执行语义障碍分类，其检索准确率为90%，并根据混合障碍环境在执行过程中调整阻抗参数。 我们评估了两种扩散规划器：（i）一种俯视视角长期规划器，采用单次推理；以及（ii）一种第一人称视角（FPV）短期规划器，通过两阶段推理管道部署。这两种规划器在二十种静态和动态实验配置中均实现了100%的轨迹生成率，并且通过分层APF-阻抗控制堆栈进行了零样本仿真到现实转换，在Crazyflie 2.1无人机上得到了验证。 俯视视角规划器产生了更平滑的轨迹，当接近硬障碍物时，其保守跟踪速度为1.0至1.2米/秒；当接近软障碍物时，则为0.6至1.0米/秒。相比之下，FPV规划器生成了具有更大局部安全距离的路径，并通常以更高的速度运行，在接近硬障碍物时达到1.4到2.0米/秒，在接近软障碍物时则高达1.6米/秒。 在二十种实验配置（总计一百次试验）中，该框架实现了92%的成功率，并保持了基于阻抗的稳定编队控制和有界振荡，且没有发生空中碰撞。这证明了它能够可靠并适应性地进行拥挤室内环境中的群组导航。
URL
https://arxiv.org/abs/2603.09031
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