Aerial Robotics

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Aerial robotics develops flying machines—from multirotors and fixed-wing aircraft to flapping-wing and hybrid robots—and the methods that let them sense, move, and interact with the world. Work combines onboard perception and state estimation with model-based planning and control, including model predictive control, visual servoing, and robust control; reinforcement learning and vision-language-action models are increasingly used for navigation, tracking, and manipulation. Researchers also study cooperative swarms, aerial transport, landing, and contact tasks, evaluating systems in simulation and benchmarks as well as on real robots with limited power, computing, and sensing. A central challenge is making learning-based autonomy generalize from simulation to changing real conditions while preserving safety, reliability, and real-time performance.

Latest papers

343 papers38 papers in the last four weeks, down 24% on the four weeks before. 0.2% of all new papers.

Apr 28, 2026

Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty

This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric uncertainty. We consider a planar two-vehicle chain connected by rigid links, modeled with input-rate actuation to enforce slew-rate and magnitude limits on thrust and torque. Robustness to uncertainty in link mass, length, and inertia is achieved by propagating first-order parametric state sensitivities along the horizon and using them to compute online constraint-tightening margins. We robustify an inter-link separation constraint, implemented via a smooth cosine embedding, and thrust-magnitude bounds. The method is implemented in MATLAB and evaluated with boundary-hugging maneuvers and Monte-Carlo uncertainty sampling. Results show improved constraint margins under uncertainty with tracking performance comparable to nominal NMPC.

Giuseppe Silano, Quentin Sablé, Marco Tognon +2

Ricerca sul Sistema Energetico S.p.A., Milan, Italy, and Czech Technical University, Prague, Czechia · University of Twente, Enschede, The Netherlands · University of Rennes, CNRS, Inria, IRISA, 35042, France +2

Aerial Robotics Nonlinear MPC Robust Control Multi-Robot Coordination Model Predictive Control

Apr 27, 2026

LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception

Vision-language-action (VLA) models have shown strong semantic grounding and task generalization in manipulation, but aerial deployment remains difficult because drones require low-latency closed-loop guidance under strict onboard compute and communication constraints. We present LiteVLA-H, a compact 256M-parameter VLA system designed for dual-rate operation on an NVIDIA Jetson AGX Orin: a fast outer-loop guidance mode for short action-token outputs and a slower semantic mode for scene understanding, hazard description, and operator-facing narration. The central empirical observation is that, in this compact edge regime, end-to-end latency is dominated by multimodal pre-fill rather than by the marginal cost of decoding a few extra tokens. This motivates a scheduler that issues reactive action tokens at 50.65,ms (19.74,Hz) while still supporting sentence-level semantic outputs at 149.90--164.57\ms (6.08--6.67,Hz) on the same embedded platform. To specialize the model without collapsing its descriptive competence, we use a knowledge-preserving fine-tuning recipe that mixes reactive flight data, aerial semantic data, and generic caption/VQA supervision. Beyond reporting current latency measurements, we position the system against recent state-of-the-art architectures, including AnywhereVLA, FutureVLA, and ReMem-VLA, showing that the measured action branch reaches a higher edge inference rate under our deployment conditions while retaining periodic semantic awareness.

Justin williams, Kishor Datta Gupta, Roy George +1

Aerial Robotics Efficient VLM Inference Vision-Language-Action Models

Apr 24, 2026

A Lightweight Toggleable Adhesion Prototype for Multirotor UAV Landing on Tilting Platforms

Autonomous multirotor landings on uncrewed surface vessels (USVs) are critical for persistent maritime operations but remain challenging due to wave-induced tilt, wind disturbances, and limited landing area. Many existing approaches exhibit small pose tolerance for reliable landing. This paper presents a lightweight toggleable adhesion mechanism to improve landing reliability. The system uses a motor-driven corkscrew that engages hook-and-loop material on the landing surface, enabling active adhesion during landing and controlled release during takeoff. We evaluate a prototype using a modified Crazyflie 2.0 and a custom tilting platform at fixed angles representative of extreme wave conditions. Using only a simple vertical PID controller, the proposed approach increases landing success from an average of 40% (baseline) to 80% across platform tilts up to 43 degrees using appropriately selected actuation settings.

Teighin Nordholt, Melissa Greeff

Robora Lab (www.roboralab.com), Queen’s University · affiliated with Ingenuity Labs Research Institute

Aerial Robotics UAV Landing on Moving Platforms

Apr 23, 2026

Characterizing pitch and roll torque coupling in insect-sized flapping-wing robots using a microfabricated gimbal

Sub-gram flapping-wing flying insect robots (FIRs) are challenging to model because of mechanical complexity in their wings, unsteady aerodynamic flow, and the difficulty of making precise measurements at a small scale. Coupling effects between roll and pitch torque actuation have not previously been measured because a two-axis sensor that is sensitive enough has not been realized. To address this shortcoming, we introduce a microfabricated gimbal design capable of precisely and simultaneously measuring roll and pitch torques as well as thrust. We then used it to measure the extent to which a pitch torque command affects roll torque and vice versa on a 180 mg piezo-actuated flapping-wing flying platform. Our results show a high coefficient of determination in the linear regression for both pitch (0.95) and roll (0.98) and low cross-correlation coefficients (-0.001 and -0.085, respectively) across the full range of simultaneous torque commands, indicating negligible cross-axis coupling. Similarly, thrust force deviates by a maximum of only 5.8% from the mean thrust value. These results validate the assumption that pitch and toll can be considered independently in control and will inform future models of how inputs affect the aerodynamics of resonant flapping-wing systems.

Aaron Weber, Daksh Dhingra, Sawyer B. Fuller

Department of Mechanical Engineering, University of Washington, Seattle, USA · Humanoid, Boston, USA · Department of Mechanical Engineering and Paul G. Allen School of Computer Science, University of Washington, Seattle, USA

Aerial Robotics

Apr 23, 2026

A Case Study in Recovery of Drones using Discrete-Event Systems

Discrete-event systems and supervisory control theory provide a rigorous framework for specifying correct-by-construction behavior. However, their practical application to swarm robotics remains largely underexplored. In this paper, we investigate a topological recovery method based on discrete-event-systems within a swarm robotics context. We propose a hybrid architecture that combines a high-level discrete event systems supervisor with a low-level continuous controller, allowing lost drones to safely recover from fault or attack events and re-enter a controlled region. The method is demonstrated using ten simulated UAVs in the py-bullet-drones framework. We show recovery performance across four distinct scenarios, each with varying initial state estimates. Additionally, we introduce a secondary recovery supervisor that manages the regrouping process for a drone after it has re-entered the operational region.

Liam P. Burns, Dayse M. Cavalcanti, Felipe G. Cabral +4

Department of Electrical and Computer Engineering and Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, K7L 3N6, Canada · Automation and Systems Engineering Program, Federal University of Santa Catarina, Florianópolis, SC, 88.040-900, Brazil

Dynamical Systems Aerial Robotics Multi-Robot Systems Swarm Robotics Robot Failure Recovery Multi-Robot Coordination

Apr 22, 2026

Impact-Aware Model Predictive Control for UAV Landing on a Heaving Platform

Landing UAVs on heaving marine platforms is challenging because relative vertical motion can generate large impact forces and cause rebound on touchdown. To address this, we develop an impact-aware Model Predictive Control (MPC) framework that models landing as a velocity-level rigid-body impact governed by Newton's restitution law. We embed this as a linear complementarity problem (LCP) within the MPC dynamics to predict the discontinuous post-impact velocity and suppress rebound. In simulation, restitution-aware prediction reduces pre-impact relative velocity and improves landing robustness. Experiments on a heaving-deck testbed show an 86.2% reduction in post-impact deflection compared to a tracking MPC.

Jess Stephenson, Melissa Greeff

Robora Lab, Ingenuity Labs Research Institute, Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada

Aerial Robotics UAV Landing on Moving Platforms Model Predictive Control

Apr 21, 2026

Scheduling Analysis of UAV Flight Control Workloads on PREEMPT_RT Linux Using a Raspberry Pi 5

Modern UAV architectures increasingly aim to unify high-level autonomy and low-level flight control on a single General-Purpose Operating System (GPOS). However, complex multi-core System-on-Chips (SoCs) introduce significant timing indeterminism due to shared resource contention. This paper performs an architectural analysis of the PREEMPT RT Linux kernel on a Raspberry Pi 5, specifically isolating the impact of kernel activation paths (deferred execution SoftIRQs versus real-time direct activation) on a 250 Hz control loop. Results show that under heavy stress, the standard kernel is unsuitable, exhibiting worst-case latencies exceeding 9 ms. In contrast, PREEMPT RT reduced the worst-case latency by nearly 88 percent to under 225 microseconds, enforcing a direct wake-up path that mitigates OS noise. These findings demonstrate that while PREEMPT RT resolves scheduling variance, the residual jitter on modern SoCs is primarily driven by hardware memory contention.

Luiz Giacomossi, Håkan Forsberg, Ivan Tomasic +2

Mälardalen University Västerås, Sweden · ReTiS Lab, Scuola Superiore Sant’Anna Pisa, Italy

Aerial Robotics Real-Time Scheduling

Apr 21, 2026

AeroBridge-TTA: Test-Time Adaptive Language-Conditioned Control for UAVs

Language-guided unmanned aerial vehicles (UAVs) often fail not from bad reasoning or perception, but from execution mismatch: the gap between a planned trajectory and the controller's ability to track it when the real dynamics differ from training (mass changes, drag shifts, actuator delay, wind). We propose AeroBridge-TTA, a language-conditioned control pipeline that targets this gap with test-time adaptation. It has three parts: a language encoder that maps the command into a subgoal, an adaptive policy conditioned on the subgoal and a learned latent, and a test-time adaptation (TTA) module that updates the latent online from observed transitions. On five language-conditioned UAV tasks under 13 mismatch conditions with the same domain randomization, AeroBridge-TTA ties a strong PPO-MLP baseline in-distribution and wins all 5 out-of-distribution (OOD) conditions, +22.0 pts on average (62.7% vs. 40.7%); the +8.5 pt overall gain comes entirely from the OOD regime. A same-weights ablation that only changes the step size

α

shows the latent update itself is responsible for a

4.6\times

OOD lift.

Lingxue Lyu

University of Pennsylvania Philadelphia, Pennsylvania, United States

Robotic Control Aerial Robotics Language-Conditioned Robot Control Adaptive Control

Apr 21, 2026

AI-Enabled Image-Based Hybrid Vision/Force Control of Tendon-Driven Aerial Continuum Manipulators

This paper presents an AI-enabled cascaded hybrid vision/force control framework for tendon-driven aerial continuum manipulators based on constant-strain modeling in

SE(3)

as a coupled system. The proposed controller is designed to enable autonomous, physical interaction with a static environment while stabilizing the image feature error. The developed strategy combines the cascaded fast fixed-time sliding mode control and a radial basis function neural network to cope with the uncertainties in the image acquired by the eye-in-hand monocular camera and the measurements from the force sensing apparatus. This ensures rapid, online learning of the vision- and force-related uncertainties without requiring offline training. Furthermore, the features are extracted via a state-of-the-art graph neural network architecture employed by a visual servoing framework using line features, rather than relying on heuristic geometric line extractors, to concurrently contribute to tracking the desired normal interaction force during contact and regulating the image feature error. A comparative study benchmarks the proposed controller against established rigid-arm aerial manipulation methods, evaluating robustness across diverse scenarios and feature extraction strategies. The simulation and experimental results showcase the effectiveness of the proposed methodology under various initial conditions and demonstrate robust performance in executing manipulation tasks.

Shayan Sepahvand, Farrokh Janabi-Sharifi, Farhad Aghili

Department of Mechanical, Industrial, and Mechatronics Engineering, Toronto Metropolitan University, Toronto, Canada · Department of Mechanical, Industrial, and Aerospace Engineering (MIAE), Concordia University, Montreal, Quebec H3G 1M8, Canada

Robotic Control Aerial Robotics Continuum Robot Control Vision-Based Robot Control Aerial Manipulation

Apr 20, 2026

Task-Adaptive Admittance Control for Human-Quadrotor Cooperative Load Transportation with Dynamic Cable-Length Regulation

The collaboration between humans and robots is critical in many robotic applications, especially in those requiring physical human-robot interaction (pHRI). Previous research in pHRI has largely focused on robotic manipulators, employing impedance or admittance control to maintain operational safety. Conversely, research in human-quadrotor cooperative load transportation (CLT) is still in its infancy. This letter introduces a novel admittance controller designed for safe and effective human-quadrotor CLT using a quadrotor equipped with an actively-controlled winch. The proposed method accounts for the system's coupled dynamics, allowing the quadrotor and its cable to dynamically adapt to contact forces during CLT tasks, thereby enhancing responsiveness. We experimentally validated the task-adaptive capability of the controller across the entire CLT process, including in-place loading/unloading and load transporting tasks. To this end, we compared the system performances against a conventional approach, using both variable and fixed cable lengths under low- and high-stiffness conditions. Results demonstrate that the proposed method outperforms the conventional approach in terms of system responsiveness and motion smoothness, leading to improved CLT capabilities.

Shuai Li, Ton T. H. Duong, Damiano Zanotto

Dept. of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA

Aerial Robotics Admittance Control Human-Robot Collaboration Quadrotor Control

Apr 20, 2026

Thrust Regulation Through Wing Linkage Modulation on the Aerobat Platform: Piezoelectric Slip-Stick Actuated Regulator Development

Aerobat is a bat-inspired flapping-wing robot with a wing gait generate by the computational structure, a planar linkage of carbon fiber links driven by a single motor. This design minimizes weight but couples both wings to a shared input motor, eliminating independent thrust control and preventing asymmetric maneuvers. This thesis investigates thrust regulation by modifying the effective length of the first radius link

R_1

in the computational structure. Static experiments using FDM-printed

R_1

links at three lengths (28.58, 29.33, and 30.08 mm) across 3,4, and 5 Hz flapping frequencies demonstrated that a 1.5 mm length increase produced a 37% increase in peak lift force and shifted peak force timing within the downstroke. An additional experiment using a string-actuated regulator mechanism was performed. Further actuation methods were evaluated: sub-gram micro-servo and piezoelectric slip-stick. After both the string-tension and micro-servo actuation methods failed due to structural member compliance and motor fragility respectively, a TULA-50 piezoelectric slip-stick actuator was selected. Multiple force-amplifying mechanisms were prototyped, resulting in a direct-drive variable-length mechanism. This final mechanism was demonstrated in a preliminary bench-top test, though insufficient force output prevented dynamic testing during flapping. This work establishes linkage-length modulation via embedded slip-stick actuation as a viable approach to independent wing thrust control.

Luca Ciampaglia

Northeastern University

Aerial Robotics Mechanism Design

Apr 20, 2026

Autonomous Unmanned Aircraft Systems for Enhanced Search and Rescue of Drowning Swimmers: Image-Based Localization and Mission Simulation

Drowning is an omnipresent risk associated with any activity on or in the water, and rescuing a drowning person is particularly challenging because of the time pressure, making a short response time important. Further complicating water rescue are unsupervised and extensive swimming areas, precise localization of the target, and the transport of rescue personnel. Technical innovations can provide a remedy: We propose an Unmanned Aircraft System (UAS), also known as a drone-in-a-box system, consisting of a fleet of Unmanned Aerial Vehicles (UAVs) allocated to purpose-built hangars near swimming areas. In an emergency, the UAS can be deployed in addition to Standard Rescue Operation (SRO) equipment to locate the distressed person early by performing a fully automated Search and Rescue (S&R) operation and dropping a flotation device. In this paper, we address automatically locating distressed swimmers using the image-based object detection architecture You Only Look Once (YOLO). We present a dataset created for this application and outline the training process. We evaluate the performance of YOLO versions 3, 5, and 8 and architecture sizes (nano, extra-large) using Mean Average Precision (mAP) metrics [email protected] and [email protected]:.95. Furthermore, we present two Discrete-Event Simulation (DES) approaches to simulate response times of SRO and UAS-based water rescue. This enables estimation of time savings relative to SRO when selecting the UAS configuration (type, number, and location of UAVs and hangars). Computational experiments for a test area in the Lusatian Lake District, Germany, show that UAS assistance shortens response time. Even a small UAS with two hangars, each containing one UAV, reduces response time by a factor of five compared to SRO.

Sascha Emanuel Zell, Toni Schneidereit, Armin Fügenschuh +1

Chair of Engineering Mathematics and Numerical Optimization Brandenburg University of Technology Cottbus–Senftenberg Platz der Deutschen Einheit 1 03046 Cottbus, Germany · Chair of Applied Mathematics Brandenburg University of Technology Cottbus–Senftenberg Platz der Deutschen Einheit 1 03046 Cottbus, Germany

Aerial Robotics Object Detection Aerial Object Detection

Apr 18, 2026

Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems

This paper investigates the control problem of dual-arm unmanned aerial manipulator systems (DAUAMs). Strong coupling between the dual-arm and the multirotor platform, together with unmodeled dynamics and external disturbances, poses significant challenges to stable and accurate operation. An adaptive event-triggered control scheme with neural network-based approximation is proposed to address these issues while explicitly considering communication constraints. First, a dynamic model of the DAUAM system is derived, and a command-filter-based backstepping framework with error compensation is constructed. Then, a neural network is employed to approximate external frictions, and an event-triggered mechanism is designed to reduce the transmission frequency of control updates, thereby alleviating communication and energy burdens. Lyapunov-based analysis shows that all closed-loop signals remain bounded and that the tracking error converges to a neighborhood of the desired trajectory within a fixed time. Finally, experiments on a self-built DAUAM platform demonstrate that the proposed approach achieves accurate trajectory tracking.

Yang Wang, Hai Yu, Wei He +3

Institute of Robotics and Automatic Information System, College of Artificial Intelligence, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China. · Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.

Robot Trajectory Tracking Aerial Robotics Neural Network Control Feedback Control Quadrotor Control Event-Triggered Control+1

Apr 16, 2026

Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots

Data-driven Model Predictive Control (MPC) has lately been the core research subject in the field of control theory. The combination of an optimal control framework with deep learning paradigms opens up the possibility to accurately track control tasks without the need for complex analytical models. However, the system dynamics are often nuanced and the neural model lacks the potential to understand physical properties such as inertia and conservation of energy. In this work, we propose a novel energy-based regularization loss function which is applied to the training of a neural model that learns the residual dynamics of an omnidirectional aerial robot. Our energy-based regularization encourages the neural network to cause control corrections that stabilize the energy of the system. The residual dynamics are integrated into the MPC framework and improve the positional mean absolute error (MAE) over three real-world experiments by 23% compared to an analytical MPC. We also compare our method to a standard neural MPC implementation without regularization and primarily achieve a significantly increased flight stability implicitly due to the energy regularization and up to 15% lower MAE. Our code is available under: https://github.com/johanneskbl/jsk_aerial_robot/tree/develop/neural_MPC.

Johannes Kübel, Henrik Krauss, Jinjie Li +1

DRAGON Lab, Department of Mechanical Engineering, The University of Tokyo, Tokyo 113-8656, Japan · Department of Advanced Interdisciplinary Studies, The University of Tokyo, Tokyo 153-8904, Japan

Aerial Robotics Residual Dynamics Learning Model Predictive Control

Apr 14, 2026

See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones

Autonomous drone delivery systems are rapidly advancing, but ensuring safe and reliable package drop-offs remains highly challenging in cluttered urban and suburban environments where accurately identifying suitable package drop zones is critical. Existing approaches typically rely on either geometry-based analysis or semantic segmentation alone, but these methods lack the integrated semantic reasoning required for robust decision-making. To address this gap, we propose See&Say, a novel framework that combines geometric safety cues with semantic perception, guided by a Vision-Language Model (VLM) for iterative refinement. The system fuses monocular depth gradients with open-vocabulary detection masks to produce safety maps, while the VLM dynamically adjusts object category prompts and refines hazard detection across time, enabling reliable reasoning under dynamic conditions during the final delivery phase. When the primary drop-pad is occupied or unsafe, the proposed See&Say also identifies alternative candidate zones for package delivery. We curated a dataset of urban delivery scenarios with moving objects and human activities to evaluate the approach. Experimental results show that See&Say outperforms all baselines, achieving the highest accuracy and IoU for safety map prediction as well as superior performance in alternative drop zone evaluation across multiple thresholds. These findings highlight the promise of VLM-guided segmentation-depth fusion for advancing safe and practical drone-based package delivery.

Mahyar Ghazanfari, Peng Wei

George Washington University

Robot Safety Aerial Robotics VLMs for Robotics

Apr 10, 2026

Soft Electroadhesive Feet for Micro Aerial Robots Perching on Smooth and Curved Surfaces

Electroadhesion (EA) provides electrically switchable adhesion and is a promising mechanism for perching micro aerial robots on smooth surfaces. However, practical implementations of soft and stretchable EA pads for aerial perching remain limited. This work presents (i) an efficient workflow for fabricating soft, stretchable electroadhesive pads with sinusoidal wave and concentric-circle electrodes in multiple sizes, (ii) a controlled experimental comparison of normal and shear adhesion under inactive (0 kV) and active (4.8 kV) conditions using an Instron-based setup, and (iii) a perching demonstration using a Crazyflie quadrotor equipped with electroadhesive feet on flat and curved substrates. Experimental results show that shear adhesion dominates, reaching forces on the order of 3 N with partial pad contact, while normal adhesion is comparatively small and strongly dependent on substrate properties. The Crazyflie prototype demonstrates repeatable attachment on smooth plastic surfaces, including curved geometries, as well as rapid detachment when the voltage is removed. These results highlight the potential of soft electroadhesive feet for lightweight and reliable perching in micro aerial vehicles (MAVs).

Chen Liu, Sonu Feroz, Ketao Zhang

Centre for Advanced Robotics at Queen Mary University of London, UK

Aerial Robotics Soft Robotics

Apr 9, 2026

AgiPIX: Bridging Simulation and Reality in Indoor Aerial Inspection

Autonomous indoor flight for critical asset inspection presents fundamental challenges in perception, planning, control, and learning. Despite rapid progress, there is still a lack of a compact, active-sensing, open-source platform that is reproducible across simulation and real-world operation. To address this gap, we present Agipix, a co-designed open hardware and software platform for indoor aerial autonomy and critical asset inspection. Agipix features a compact, hardware-synchronized active-sensing platform with onboard GPU-accelerated compute that is capable of agile flight; a containerized ROS~2-based modular autonomy stack; and a photorealistic digital twin of the hardware platform together with a reliable UI. These elements enable rapid iteration via zero-shot transfer of containerized autonomy components between simulation and real flights. We demonstrate trajectory tracking and exploration performance using onboard sensing in industrial indoor environments. All hardware designs, simulation assets, and containerized software are released openly together with documentation.

Sasanka Kuruppu Arachchige, Juan Jose Garcia, Changda Tian +4

Computing Sciences department at Tampere University, Finland · U2IS at ENSTA, Paris · Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece.

Aerial Robotics Robotic Inspection Robotics Simulation Sim-to-Real Transfer

Apr 9, 2026

MotionScape: A Motion-Stratified UAV Video Benchmark for World Modeling and Future Video Generation

Unmanned aerial vehicles (UAVs) are increasingly crucial for low-altitude autonomy and complex environment understanding. World models enable UAVs to anticipate how future states may evolve under potential actions, providing predictive support for autonomous decision-making. For video world models, future video generation serves as a means of simulating future visual states, providing a direct basis for evaluating their predictive capability. However, existing UAV video resources typically focus on specific control signals or simulated environments. Standardized benchmarks for multi-condition UAV-view future video generation are still limited. To bridge this gap, we introduce MotionScape, a real-world UAV-view benchmark comprising 228 high-resolution video clips totaling 62,700 frames, with semantic annotations of weather and illumination conditions, scene environment, and camera-viewpoint motion. The clips are stratified into low-, medium-, and high-motion strata using optical-flow-based motion intensity. MotionScape supports Text2World, Image2World, and Video2World evaluation under a unified future-generation protocol. Technical validation with representative baseline models assesses performance across conditioning settings and motion strata, providing a unified platform for evaluating the future visual simulation capabilities of UAV world models.

Zile Guo, Zhan Chen, Xiaoxuan Liu +5

Aerospace Information Research Institute, Chinese Academy of Sciences · Key Laboratory of Target Cognition and Application Technology, Chinese Academy of Sciences · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences +1

Aerial Robotics Video Generation World Model Evaluation Video World Models

Apr 9, 2026

Bird-Inspired Spatial Flapping Wing Mechanism via Coupled Linkages with Single Actuator

Spatial single-loop mechanisms such as Bennett linkages offer a unique combination of one-degree-of-freedom actuation and nontrivial spatial trajectories, making them attractive for lightweight bio-inspired robotic design. However, although they appear simple and elegant, the geometric task-based synthesis is rather complicated and often avoided in engineering tasks due to the mathematical complexity involved. This paper presents a bird-inspired flapping-wing mechanism built from two coupled spatial four-bars, driven by a single motor. One linkage is actuated to generate the desired spatial sweeping stroke, while the serially coupled linkage remains unactuated and passively switches between extended and folded wing configurations over the stroke cycle. We introduce a simplified kinematic methodology for constructing Bennett linkages from quadrilaterals that contain a desired surface area and further leverage mechanically induced passive state switching. This architecture realizes a coordinated sweep-and-fold wing motion with a single actuation input, reducing weight and control complexity. A 3D-printed prototype is assembled and tested, demonstrating the intended spatial stroke and passive folding behavior.

Daniel Huczala, Sun-Pill Jung, Frank C. Park

Robotics Laboratory, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea · Biorobotics Laboratory, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

Mechanism Design Aerial Robotics

Apr 7, 2026

eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces

Electric vertical takeoff and landing (eVTOL) aircraft operating in high-density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential (MVP) algorithm affect eVTOL energy consumption. Using a physics-based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft. The main finding is that MVP-based deconfliction is energy-efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right-skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi-aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4-5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety-critical reserve planning. Together, these results validate MVP's suitability for energy-constrained eVTOL operations and provide quantitative guidance for reserve energy determination in Advanced Air Mobility.

Alex Zongo, Peng Wei

Department of Mechanical and Aerospace Engineering, George Washington University, Washington, DC, 20052, USA

Air Traffic Management Aerial Robotics Collision Avoidance

Apr 7, 2026

Force Polytope-Based Cant-Angle Selection for Tilting Hexarotor UAVs

From a maneuverability perspective, the main advantage of tilting multirotor UAVs lies in the dynamic variability of the feasible executable wrench, which represents a key asset for physical interaction tasks. Accordingly, cant-angle selection should be optimized to ensure high performance while avoiding abrupt variations and preserving real-world feasibility. In this context, this work proposes a lightweight control framework for star-shaped interdependent cant-tilting hexarotor UAVs performing interaction tasks. The method uses an offline-computed look-up table of zero-moment force polytopes to identify feasible cant angles for a desired control force and select the optimal one by balancing efficiency and smoothness. The framework is integrated with a geometric full-pose controller and validated through Monte Carlo simulations in MATLAB/Simulink and compared against a baseline strategy. The results show a significant reduction in computation time, together with improved pose-tracking performance and competitive actuation efficiency. A final physics-based simulation of a complete wall inspection task in Simscape further confirms the feasibility of the proposed strategy in interacting scenarios.

Alberto Piccina, Massimiliano Bertoni, Angelo Cenedese +1

Department of Management and Engineering, University of Padova, Vicenza, Italy · Department of Information Engineering, University of Padova, Padova, Italy · Department of Industrial Engineering, University of Padova, Padova, Italy

Aerial Robotics Aerial Manipulation

Apr 7, 2026

Dynamic Control Allocation for Dual-Tilt UAV Platforms

This paper focuses on dynamic control allocation for a hexarotor UAV platform, considering a trajectory tracking task as as case study. It is assumed that the platform is dual-tilting, meaning that it is able to tilt each propeller independently during flight, along two orthogonal axis. We present a hierarchical control structure composed of a high-level controller generating the required wrench for the tracking task, and a control allocation law ensuring that the actuators produce such wrench. The allocator imposes desired first-order dynamics on the actuators set, and exploits system redundancy to optimize the actuators state with respect to a given objective function. Unlike other studies on the subject, we explicitly model actuator saturation and provide theoretical insights on its effect on control performances. We also investigate the role of propeller tilt angles, by imposing asymmetric shapes in the objective function. Numerical simulations are presented to validate the allocation strategy.

Marcello Sorge, Federico Ciresola, Giulia Michieletto +1

Department of Information Engineering University of Padova Padova, Italy · Department of Management and Engineering, University of Padova Padova, Italy · Department of Industrial Engineering, University of Padova, Padova, Italy

Aerial Robotics Feedback Control

Apr 7, 2026

MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems

Modular Aerial Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations, providing high adaptability to diverse mission scenarios, fault conditions, and payload capacities. However, existing control algorithms for MARS rely on simplified quasi-static models and rule-based allocation, which generate discontinuous and unbounded motor commands. This leads to attitude error accumulation as the number of drone units scales, ultimately causing severe oscillations during docking, separation, and waypoint tracking. To address these limitations, we first design a compact mechanical system that enables passive docking, detection-free passive locking, and magnetic-assisted separation using a single micro servo. Second, we introduce a force-torque-equivalent and polytope-constraint virtual quadrotor that explicitly models feasible wrench sets. Together, these abstractions capture the full MARS dynamics and enable existing quadrotor controllers to be applied across different configurations. We further optimize the yaw angle that maximizes control authority to enhance agility. Third, building on this abstraction, we design a two-stage predictive-allocation pipeline: a constrained predictive tracker computes virtual inputs while respecting force/torque bounds, and a dynamic allocator maps these inputs to individual modules with balanced objectives to produce smooth, trackable motor commands. Simulations across over 10 configurations and real-world experiments demonstrate stable docking, locking, and separation, as well as effective control performance. To our knowledge, this is the first real-world demonstration of MARS achieving agile flight and transport with 40 deg peak pitch while maintaining an average position error of 0.0896 m. The video is available at: https://youtu.be/yqjccrIpz5o

Rui Huang, Zhiqian Cai, Siyu Tang +6

Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore · Engineering Design and Innovation Centre, National University of Singapore, Singapore 117583, Singapore

Aerial Robotics Modular Robotics Quadrotor Control

Apr 6, 2026

GaussFly: Contrastive Reinforcement Learning for Visuomotor Policies in 3D Gaussian Fields

Learning visuomotor policies for Autonomous Aerial Vehicles (AAVs) relying solely on monocular vision is an attractive yet highly challenging paradigm. Existing end-to-end learning approaches directly map high-dimensional RGB observations to action commands, which frequently suffer from low sample efficiency and severe sim-to-real gaps due to the visual discrepancy between simulation and physical domains. To address these long-standing challenges, we propose GaussFly, a novel framework that explicitly decouples representation learning from policy optimization through a cohesive real-to-sim-to-real paradigm. First, to achieve a high-fidelity real-to-sim transition, we reconstruct training scenes using 3D Gaussian Splatting (3DGS) augmented with explicit geometric constraints. Second, to ensure robust sim-to-real transfer, we leverage these photorealistic simulated environments and employ contrastive representation learning to extract compact, noise-resilient latent features from the rendered RGB images. By utilizing this pre-trained encoder to provide low-dimensional feature inputs, the computational burden on the visuomotor policy is significantly reduced while its resistance against visual noise is inherently enhanced. Extensive experiments in simulated and real-world environments demonstrate that GaussFly achieves superior sample efficiency and asymptotic performance compared to baselines. Crucially, it enables robust and zero-shot policy transfer to unseen real-world environments with complex textures, effectively bridging the sim-to-real gap.

Yuhang Zhang, Mingsheng Li, Yujing Shang +4

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore · College of Design and Engineering, National University of Singapore, Singapore 119077, Singapore +1

Visuomotor Policy Learning Visual RL Sim-to-Real Transfer Aerial Robotics

Apr 6, 2026

EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation

Autonomous Unmanned Aerial Vehicles (UAVs) must reliably detect thin obstacles such as wires, poles, and branches to navigate safely in real-world environments. These structures remain difficult to perceive because they occupy few pixels, often exhibit weak visual contrast, and are strongly affected by class imbalance. Existing segmentation methods primarily target coarser obstacles and do not fully exploit the complementary multimodal cues needed for thin-structure perception. We present EDFNet, a modular early-fusion segmentation framework that integrates RGB, depth, and edge information for thin-obstacle perception in cluttered aerial scenes. We evaluate EDFNet on the Drone Depth and Obstacle Segmentation (DDOS) dataset across sixteen modality-backbone configurations using U-Net and DeepLabV3 in pretrained and non-pretrained settings. The results show that early RGB-Depth-Edge fusion provides a competitive and well-balanced baseline, with the most consistent gains appearing in boundary-sensitive and recall-oriented metrics. The pretrained RGBDE U-Net achieves the best overall performance, with the highest Thin-Structure Evaluation Score (0.244), mean IoU (0.219), and boundary IoU (0.234), while maintaining competitive runtime performance (19.62 FPS) on our evaluation hardware. However, performance on the rarest ultra-thin categories remains low across all models, indicating that reliable ultra-thin segmentation is still an open challenge. Overall, these findings position early RGB-Depth-Edge fusion as a practical and modular baseline for thin-obstacle segmentation in UAV navigation.

Negar Fathi

University of Nebraska–Lincoln, Lincoln, NE, USA

Thin-Structure Segmentation Multimodal Semantic Segmentation Image Segmentation UAV Navigation Aerial Robotics Boundary-Aware Segmentation

Apr 3, 2026

Learning Families of Fixed-Wing Aircraft Control Policies with Hypernetwork-Conditioned Reinforcement Learning

This paper investigates whether it is more effective to condition reinforcement learning policies across changing dynamics using standard input augmentation or hypernetwork-based parameter modulation. We use a small fixed-wing uncrewed aircraft under actuator failures as a demanding test case, as these failures introduce distinctly different closed-loop dynamics. We evaluate parameter-efficient hypernetwork formulations based on Feature-wise Linear Modulation (FiLM) and Low-Rank Adaptation (LoRA), trained using proximal policy optimization. High-fidelity six-degree-of-freedom simulations demonstrate that hypernetwork-conditioned policies improve robustness compared to standard multilayer perceptron policies trained with augmented inputs. Most notably, the hypernetwork policies generalize effectively to time-varying actuator failure modes not encountered during training, indicating that modulating internal parameters provides significant advantages over standard input augmentation for this class of highly coupled dynamics.

Dennis Marquis, Mazen Farhood

Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24061, USA

Fault-Tolerant Control Robust RL Aerial Robotics Hypernetworks

Apr 2, 2026

UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models

Embodied visual tracking is crucial for Unmanned Aerial Vehicles (UAVs) executing complex real-world tasks. In dynamic urban scenarios with complex semantic requirements, Vision-Language-Action (VLA) models show great promise due to their cross-modal fusion and continuous action generation capabilities. To benchmark multimodal tracking in such environments, we construct a dedicated evaluation benchmark and a large-scale dataset encompassing over 890K frames, 176 tasks, and 85 diverse objects. Furthermore, to address temporal feature redundancy and the lack of spatial geometric priors in existing VLA models, we propose an improved VLA tracking model, UAV-Track VLA. Built upon the

π_{0.5}

architecture, our model introduces a temporal compression net to efficiently capture inter-frame dynamics. Additionally, a parallel dual-branch decoder comprising a spatial-aware auxiliary grounding head and a flow matching action expert is designed to decouple cross-modal features and generate fine-grained continuous actions. Systematic experiments in the CARLA simulator validate the superior end-to-end performance of our method. Notably, in challenging long-distance pedestrian tracking tasks, UAV-Track VLA achieves a 61.76% success rate and 269.65 average tracking frames, significantly outperforming existing baselines. Furthermore, it demonstrates robust zero-shot generalization in unseen environments and reduces single-step inference latency by 33.4% (to 0.0571s) compared to the original

π_{0.5}

, enabling highly efficient, real-time UAV control. Data samples and demonstration videos are available at: https://github.com/Hub-Tian/UAV-Track_VLA.

Qiyao Zhang, Shuhua Zheng, Jianli Sun +6

Beijing Institute of Technology, Beijing, China · Flying Intelligence Team (Virtual Research Community) · Institute of Automation, Chinese Academy of Sciences, Beijing, China +4

Visual Object Tracking Vision-Language-Action Models Efficient VLA Models Aerial Robotics

Apr 1, 2026

Real Time Local Wind Inference for Robust Autonomous Navigation

This thesis presents a solution that enables aerial robots to reason about surrounding wind flow fields in real time using on board sensors and embedded flight hardware. The core novelty of this research is the fusion of range measurements with sparse in situ wind measurements to predict surrounding flow fields. We aim to address two fundamental questions: first, the sufficiency of topographical data for accurate wind prediction in dense urban environments; and second, the utility of learned wind models for motion planning with an emphasis on energy efficiency and obstacle avoidance. Drawing on tools from deep learning, fluid mechanics, and optimal control, we establish a framework for local wind prediction using navigational LiDAR, and then incorporate local wind model priors into a receding-horizon optimal controller to study how local wind knowledge affects energy use and robustness during autonomous navigation. Through simulated demonstrations in diverse urban wind scenarios we evaluate the predictive capabilities of the wind predictor, and quantify improvements to autonomous urban navigation in terms of crash rates and energy consumption when local wind information is integrated into the motion planning. Sub-scale free flight experiments in an open-air wind tunnel demonstrate that these algorithms can run in real time on an embedded flight computer with sufficient bandwidth for stable control of a small aerial robot. Philosophically, this thesis contributes a new paradigm for localized wind inference and motion planning in unknown windy environments. By enabling robots to rapidly assess local wind conditions without prior environmental knowledge, this research accelerates the introduction of aerial robots into increasingly challenging environments.

Spencer Folk

Mechanical Engineering and Applied Mechanics, University of Pennsylvania

Aerial Robotics Robot Navigation Spatiotemporal Forecasting

Mar 31, 2026

Design and Aerodynamic Modeling of MetaMorpher: A Hybrid Rotary andFixed-Wing Morphing UAV

In this paper, we present a generalized, comprehensive nonlinear mathematical model and conceptual design for the MetaMorpher, a metamorphic Unmanned Aerial Vehicle (UAV) designed to bridge the gap between vertical takeoff and landing agility and fixed-wing cruising efficiency. Building on the successful design of the spincopter platform, this work introduces a simplified mechanical architecture using lightweight materials and a novel wing-folding strategy. Unlike traditional rigid-body approximations, we derive a nonlinear flight dynamics model that enables arbitrary force distributions across a segmented wing structure. This modularity allows for testing different airfoils, mass distributions, and chord lengths in a single environment. As part of this work, various flight modes were specifically tested and analyzed in the Simulink environment. The results show that the model behaves predictably under different structural configurations, demonstrating its reliability as a tool for rapid design evaluation.

Anja Bosak, Dorian Erić, Ana Milas +1

All authors are with the University of Zagreb, Faculty of Electrical Engineering and Computing, LARICS Laboratory for Robotics and Intelligent Control Systems, Unska 3, 10000 Zagreb, Croatia

Aerial Robotics Dynamical Systems

Mar 30, 2026

A Self-Rotating Tri-Rotor UAV for Field of View Expansion and Autonomous Flight

Unmanned Aerial Vehicles (UAVs) perception relies on onboard sensors like cameras and LiDAR, which are limited by the narrow field of view (FoV). We present Self-Perception INertial Navigation Enabled Rotorcraft (SPINNER), a self-rotating tri-rotor UAV for the FoV expansion and autonomous flight. Without adding extra sensors or energy consumption, SPINNER significantly expands the FoV of onboard camera and LiDAR sensors through continuous spin motion, thereby enhancing environmental perception efficiency. SPINNER achieves full 3-dimensional position and roll--pitch attitude control using only three brushless motors, while adjusting the rotation speed via anti-torque plates design. To address the strong coupling, severe nonlinearity, and complex disturbances induced by spinning flight, we develop a disturbance compensation control framework that combines nonlinear model predictive control (MPC) with incremental nonlinear dynamic inversion. Experimental results demonstrate that SPINNER maintains robust flight under wind disturbances up to 4.8 ,m/s and achieves high-precision trajectory tracking at a maximum speed of 2.0,m/s. Moreover, tests in parking garages and forests show that the rotational perception mechanism substantially improves FoV coverage and enhances perception capability of SPINNER.

Xiaobin Zhou, Zihao Zheng, Aoxu Jin +2

School of Robotics and Automation, Nanjing University, Suzhou, 215163, China.

Aerial Robotics UAV Navigation Active Perception