UAV Navigation

UAV: Unmanned Aerial Vehicle

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Overview

UAV navigation studies how drones estimate their position, understand nearby space, and choose motions to reach goals while avoiding obstacles and respecting vehicle limits. State estimation commonly uses visual-inertial or LiDAR-inertial odometry, sometimes combined with satellite imagery, semantic maps, or other sensors when GNSS is unavailable. Planners range from model predictive control, graph search, and trajectory optimization to reinforcement learning and vision-language-action models that follow natural-language goals; safety filters and control barrier functions are often used to constrain motion. A central challenge is making these systems reliable in unfamiliar, dynamic conditions while balancing safety, speed, energy use, onboard computation, and transfer from simulation to real flight.

Latest papers

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272 papers38 papers in the last four weeks, up 27% on the four weeks before. 0.2% of all new papers.

Mar 13, 2026

GoalSwarm: Multi-UAV Semantic Coordination for Open-Vocabulary Object Navigation

Cooperative visual semantic navigation is a foundational capability for aerial robot teams operating in unknown environments. However, achieving robust open-vocabulary object-goal navigation remains challenging due to the computational constraints of deploying heavy perception models onboard and the complexity of decentralized multi-agent coordination. We present GoalSwarm, a fully decentralized multi-UAV framework for zero-shot semantic object-goal navigation. Each UAV collaboratively constructs a shared, lightweight 2D top-down semantic occupancy map by projecting depth observations from aerial vantage points, eliminating the computational burden of full 3D representations while preserving essential geometric and semantic structure. The core contributions of GoalSwarm are threefold: (1) integration of zero-shot foundation model -- SAM3 for open vocabulary detection and pixel-level segmentation, enabling open-vocabulary target identification without task-specific training; (2) a Bayesian Value Map that fuses multi-viewpoint detection confidences into a per-pixel goal-relevance distribution, enabling informed frontier scoring via Upper Confidence Bound (UCB) exploration; and (3) a decentralized coordination strategy combining semantic frontier extraction, cost-utility bidding with geodesic path costs, and spatial separation penalties to minimize redundant exploration across the swarm.

MoniJesu Wonders James, Amir Atef Habel, Aleksey Fedoseev +1

The authors are with the Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia.

UAV NavigationObject-Goal NavigationMulti-Robot CoordinationMulti-Robot ExplorationSemantic Mapping
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Mar 13, 2026

Consistent and Efficient MSCKF-based LiDAR-Inertial Odometry with Inferred Cluster-to-Plane Constraints for UAVs

Robust and accurate navigation is critical for Unmanned Aerial Vehicles (UAVs) especially for those with stringent Size, Weight, and Power (SWaP) constraints. However, most state-of-the-art (SOTA) LiDAR-Inertial Odometry (LIO) systems still suffer from estimation inconsistency and computational bottlenecks when deployed on such platforms. To address these issues, this paper proposes a consistent and efficient tightly-coupled LIO framework tailored for UAVs. Within the efficient Multi-State Constraint Kalman Filter (MSCKF) framework, we build coplanar constraints inferred from planar features observed across a sliding window. By applying null-space projection to sliding-window coplanar constraints, we eliminate the direct dependency on feature parameters in the state vector, thereby mitigating overconfidence and improving consistency. More importantly, to further boost the efficiency, we introduce a parallel voxel-based data association and a novel compact cluster-to-plane measurement model. This compact measurement model losslessly reduces observation dimensionality and significantly accelerating the update process. Extensive evaluations demonstrate that our method outperforms most state-of-the-art (SOTA) approaches by providing a superior balance of consistency and efficiency. It exhibits improved robustness in degenerate scenarios, achieves the lowest memory usage via its map-free nature, and runs in real-time on resource-constrained embedded platforms (e.g., NVIDIA Jetson TX2).

Jinwen Zhu, Xudong Zhao, Fangcheng Zhu +4

Meituan UAV, Beijing, China · Dept. of Mechanical Engineering, Computer and Information Sciences, University of Delaware, Newark, DE

LiDAR OdometryRobot State EstimationUAV NavigationKalman Filtering
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Mar 12, 2026

Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization

Undocumented orphaned wells pose significant health and environmental risks to nearby communities by releasing toxic gases and contaminating water sources, with methane emissions being a primary concern. Traditional survey methods such as magnetometry often fail to detect older wells effectively. In contrast, aerial in-situ sensing using unmanned aerial vehicles (UAVs) offers a promising alternative for methane emission detection and source localization. This study presents a robust and efficient framework based on a multi-agent deep reinforcement learning (MARL) algorithm for the chemical plume source localization (CPSL) problem. The proposed approach leverages virtual anchor nodes to coordinate UAV navigation, enabling collaborative sensing of gas concentrations and wind velocities through onboard and shared measurements. Source identification is achieved by analyzing the historical trajectory of anchor node placements within the plume. Comparative evaluations against the fluxotaxis method demonstrate that the MARL framework achieves superior performance in both localization accuracy and operational efficiency.

Zhirun Li, Derek Hollenbeck, Ruikun Wu +4

Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA · Department of Mechanical Engineering, University of California, Merced, CA 95343 USA · Department of Electrical Engineering, Colorado School of Mines, Golden, CO 80401 USA +3

Multi-Agent Reinforcement LearningUAV Navigation
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Mar 11, 2026

OnFly: Onboard Zero-Shot Aerial Vision-Language Navigation toward Safety and Efficiency

Aerial vision-language navigation (AVLN) enables UAVs to follow natural-language instructions in complex 3D environments. However, existing zero-shot AVLN methods often suffer from unstable single-stream Vision-Language Model decision-making, unreliable long-horizon progress monitoring, and a trade-off between safety and efficiency. We propose OnFly, a fully onboard, real-time framework for zero-shot AVLN. OnFly adopts a shared-perception dual-agent architecture that decouples high-frequency target generation from low-frequency progress monitoring, thereby stabilizing decision-making. It further employs a hybrid keyframe-recent-frame memory to preserve global trajectory context while maintaining KV-cache prefix stability, enabling reliable long-horizon monitoring with termination and recovery signals. In addition, a semantic-geometric verifier refines VLM-predicted targets for instruction consistency and geometric safety using VLM features and depth cues, while a receding-horizon planner generates optimized collision-free trajectories under geometric safety constraints, improving both safety and efficiency. In simulation, OnFly improves task success from 26.4% to 67.8%, compared with the strongest state-of-the-art baseline, while fully onboard real-world flights validate its feasibility for real-time deployment. The code will be released at https://github.com/Robotics-STAR-Lab/OnFly

Guiyong Zheng, Yueting Ban, Mingjie Zhang +2

School of Artificial Intelligence, Sun Yat-Sen University, Zhuhai, China. · Southern University of Science and Technology, Shenzhen, China · The Hong Kong University of Science and Technology, Guangzhou, China.

Vision-Language NavigationAerial VLNUAV NavigationMemory-Augmented VLMsMotion Planning
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Mar 9, 2026

Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.

Manuel Boldrer, Michal Kamler, Afzal Ahmad +1

Department of Cybernetics, Czech Technical University in Prague, Karlovo namesti 13, 121 35 Prague 2, Czechia.

Multi-Robot CoordinationUAV NavigationSafe Robot NavigationMulti-Robot Motion Planning
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Mar 9, 2026

Edged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors

Conventional visual simultaneous localization and mapping (SLAM) algorithms often fail under rapid motion, low illumination, or abrupt lighting transitions due to motion blur and limited dynamic range. Event cameras mitigate these issues with high temporal resolution and high dynamic range (HDR), but their sparse, asynchronous outputs complicate feature extraction and integration with other sensors; e.g. inertial measurement units (IMUs) and standard cameras. We present Edged USLAM, a hybrid visual-inertial system that extends Ultimate SLAM (USLAM) with an edge-aware front-end and a lightweight depth module. The frontend enhances event frames for robust feature tracking and nonlinear motion compensation, while the depth module provides coarse, region-of-interest (ROI)-based scene depth to improve motion compensation and scale consistency. Evaluations across public benchmarks and real-world unmanned air vehicle (UAV) flights demonstrate that performance varies significantly by scenario. For instance, event-only methods like point-line event-based visual-inertial odometry (PL-EVIO) or learning-based pipelines such as deep event-based visual odometry (DEVO) excel in highly aggressive or extreme HDR conditions. In contrast, Edged USLAM provides superior stability and minimal drift in slow or structured trajectories, ensuring consistently accurate localization on real flights under challenging illumination. These findings highlight the complementary strengths of event-only, learning-based, and hybrid approaches, while positioning Edged USLAM as a robust solution for diverse aerial navigation tasks.

Şebnem Sarıözkan, Hürkan Şahin, Olaya Álvarez-Tuñón +1

Automatic Control Group (RAT), Paderborn University, 33098 Paderborn, Germany · EIVA A/S, and IT University of Copenhagen (ITU), Denmark

Visual-Inertial SLAMEvent-Based VisionUAV Navigation
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Mar 8, 2026

Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation

Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively demanding for non-expert operators. To address this, we propose a novel framework that shifts the collaboration paradigm from control takeover to active information elicitation. We introduce the Minimal Information Neuro-Symbolic Tree (MINT), a reasoning mechanism that explicitly structures knowledge gaps regarding obstacles and goals into a queryable format. By leveraging large language models, our system formulates optimal binary queries to resolve specific ambiguities with minimal human interaction. We demonstrate the efficacy of this approach through a comprehensive workflow integrating a vision-language model for perception, voice interfaces, and a low-level UAV control module in both high-fidelity NVIDIA Isaac simulations and real-world deployments. Experimental results show that our method achieves a significant improvement in the success rate for complex search-and-rescue tasks while significantly reducing the frequency of human interaction compared to exhaustive querying baselines.

Zeyu Fang, Beomyeol Yu, Cheng Liu +5

George Washington University, Washington, D.C., USA · Northeastern University, Boston, MA, USA

Human-Robot CollaborationLarge Language Model-Based Robot PlanningUAV Navigation
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Mar 8, 2026

Scale-Aware UAV-to-Satellite Cross-View Geo-Localization: A Semantic Geometric Approach

Cross-View Geo-Localization (CVGL) between UAV imagery and satellite images plays a crucial role in target localization and UAV self-positioning. However, most existing methods rely on the idealized assumption of scale consistency between UAV queries and satellite galleries, overlooking the severe scale ambiguity commonly encountered in real-world scenarios. This discrepancy leads to field-of-view misalignment and feature mismatch, significantly degrading CVGL robustness. To address this issue, we propose a geometric framework that recovers the absolute metric scale from monocular UAV images using semantic anchors. Specifically, small vehicles (SVs), characterized by relatively stable prior size distributions and high detectability, are exploited as metric references. A Decoupled Stereoscopic Projection Model is introduced to estimate the absolute image scale from these semantic targets. By decomposing vehicle dimensions into radial and tangential components, the model compensates for perspective distortions in 2D detections of 3D vehicles, enabling more accurate scale estimation. To further reduce intra-class size variation and detection noise, a dual-dimension fusion strategy with Interquartile Range (IQR)-based robust aggregation is employed. The estimated global scale is then used as a physical constraint for scale-adaptive satellite image cropping, improving UAV-to-satellite feature alignment. Experiments on augmented DenseUAV and UAV-VisLoc datasets demonstrate that the proposed method significantly improves CVGL robustness under unknown UAV image scales. Additionally, the framework shows strong potential for downstream applications such as passive UAV altitude estimation and 3D model scale recovery.

Yibin Ye, Shuo Chen, Kun Wang +5

College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China · Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha 410073, China · School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China

Cross-View Geo-LocalizationUAV Navigation
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Mar 7, 2026

FreeFly-Thinking : Aligning Chain-of-Thought Reasoning with Continuous UAV Navigation

Vision-Language Navigation aims to enable agents to understand natural language instructions and carry out appropriate navigation actions in real-world environments. Most work focuses on indoor settings, with little research in complex outdoor scenes. Current UAV Vision-and-Language Navigation models typically act as black boxes without explicit reasoning. We introduce FreeFly-thinking, an end-to-end VLN framework that converts the UAV agent's egocentric images and language instructions into a series of actions, inspired by environment of urban architecture proposed by OpenFly. We first construct a UAV dataset for navigation task, and then performing natural language chain of thought. We adopt a two-stage training strategy: Supervised fine-tuning and Reinforcement fine-tuning. Experiments on unseen test demonstrate a strong performance, presenting robustness and efficiency in UAV navigation issue.

Jiaxu Zhou, Shaobo Wang, Zhiyuan Yang +2

SJTU Paris Elite Institute of Technology, Shanghai Jiao Tong University, China · School of Physics and Astronomy, Shanghai Jiao Tong University, China · Tongji University, China

Aerial VLNUAV NavigationReasoning for VLA Models
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Mar 6, 2026

Fly360: Omnidirectional Obstacle Avoidance within Drone View

Obstacle avoidance in unmanned aerial vehicles (UAVs), as a fundamental capability, has gained increasing attention with the growing focus on spatial intelligence. However, current obstacle-avoidance methods mainly depend on limited field-of-view sensors and are ill-suited for UAV scenarios which require full-spatial awareness when the movement direction differs from the UAV's heading. This limitation motivates us to explore omnidirectional obstacle avoidance for panoramic drones with full-view perception. We first study an under explored problem setting in which a UAV must generate collision-free motion in environments with obstacles from arbitrary directions, and then construct a benchmark that consists of three representative flight tasks. Based on such settings, we propose Fly360, a two-stage perception-decision pipeline with a fixed random-yaw training strategy. At the perception stage, panoramic RGB observations are input and converted into depth maps as a robust intermediate representation. For the policy network, it is lightweight and used to output body-frame velocity commands from depth inputs. Extensive simulation and real-world experiments demonstrate that Fly360 achieves stable omnidirectional obstacle avoidance and outperforms forward-view baselines across all tasks. Our model is available at https://zxkai.github.io/fly360/

Xiangkai Zhang, Dizhe Zhang, WenZhuo Cao +5

Institute of Automation Chinese Academy of Sciences, Beijing, China · Insta360 Research, Shenzhen, China · Wuhan University,Wuhan, China.

UAV NavigationRobot Collision Avoidance
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Mar 2, 2026

Real-Time Thermal-Inertial Odometry on Embedded Hardware for High-Speed GPS-Denied Flight

We present a real-time monocular thermal-inertial odometry system designed for high-velocity, GPS-denied flight on embedded hardware. The system fuses measurements from a FLIR Boson+ 640 longwave infrared camera, a high-rate IMU, a laser range finder, a barometer, and a magnetometer within a fixed-lag factor graph. To sustain reliable feature tracks under motion blur, low contrast, and rapid viewpoint changes, we employ a lightweight thermal-optimized front-end with multi-stage feature filtering. Laser range finder measurements provide per-feature depth priors that stabilize scale during weakly observable motion. High-rate inertial data is first pre-filtered using a Chebyshev Type II infinite impulse response (IIR) filter and then preintegrated, improving robustness to airframe vibrations during aggressive maneuvers. To address barometric altitude errors induced at high airspeeds, we train an uncertainty-aware gated recurrent unit (GRU) network that models the temporal dynamics of static pressure distortion, outperforming polynomial and multi-layer perceptron (MLP) baselines. Integrated on an NVIDIA Jetson Xavier NX, the complete system supports closed-loop quadrotor flight at 30 m/s with drift under 2% over kilometer-scale trajectories. These contributions expand the operational envelope of thermal-inertial navigation, enabling reliable high-speed flight in visually degraded and GPS-denied environments.

Austin Stone, Mark Petersen, Cammy Peterson

Brigham Young University, Provo, UT 84602 USA.

Gated Recurrent UnitInertial OdometryUAV NavigationGNSS-Denied Localization
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Mar 2, 2026

Goal-Oriented Semantic Communication for ISAC-Enabled Robotic Obstacle Avoidance

We investigate an integrated sensing and communication (ISAC)-enabled BS for the unmanned aerial vehicle (UAV) obstacle avoidance task, and propose a goal-oriented semantic communication (GOSC) framework for the BS to transmit sensing and command and control (C&C) signals efficiently and effectively. Our GOSC framework establishes a closed loop for sensing-C&C generation-sensing and C&C transmission: For sensing, a Kalman filter (KF) is applied to continuously predict UAV positions, mitigating the reliance of UAV position acquisition on continuous sensing signal transmission, and enhancing position estimation accuracy through sensing-prediction fusion. Based on the refined estimation position provided by the KF, we develop a Mahalanobis distance-based dynamic window approach (MD-DWA) to generate precise C&C signals under uncertainty, in which we derive the mathematical expression of the minimum Mahalanobis distance required to guarantee collision avoidance. Finally, for efficient sensing and C&C signal transmission, we propose an effectiveness-aware deep Q-network (E-DQN) to determine the transmission of sensing and C&C signals based on their value of information (VoI). The VoI of sensing signals is quantified by the reduction in uncertainty entropy of UAV's position estimation, while the VoI of C&C signals is measured by their contribution to UAV navigation improvement. Extensive simulations validate the effectiveness of our proposed GOSC framework. Compared to the conventional ISAC transmission framework that transmits sensing and C&C signals at every time slot, GOSC achieves the same 100% task success rate while reducing the number of transmitted sensing and C&C signals by 92.4% and the number of transmission time slots by 85.5%.

Wenjie Liu, Yansha Deng, Henk Wymeersch

Department of Engineering, King’s College London, Strand, London WC2R 2LS, U.K. · Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.

Task-Oriented Semantic CommunicationIntegrated Sensing and CommunicationUAV NavigationRobot Collision Avoidance
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Feb 27, 2026

Geometric Look-Angle Shaping Strategy for Enclosed Inspection

This paper introduces inspection through GLASS, a Geometric Look-Angle Shaping Strategy for enclosed regions using unmanned aerial vehicles. In doing so, the vehicles guidance command is constructed through a bounded, geometry-consistent shaping of the look angle relative to a desired standoff path. By embedding a smooth, hyperbolic-tangent-type shaping function within a polar geometric framework, GLASS ensures global existence of the guidance dynamics. It avoids the far-field limitations inherent to conventional formulations. Lyapunov stability analysis establishes asymptotic convergence to a prescribed inspection standoff under explicit curvature feasibility conditions, along with analytical settling-time characteristics. The proposed strategy incorporates maximum turn-rate constraints without inducing singularities throughout the workspace. High-fidelity six-degree-of-freedom quadrotor simulations demonstrate the effectiveness of GLASS in representative enclosed inspection scenarios, highlighting a practically viable guidance framework for autonomous enclosed inspection missions.

Amit Shivam, Manuel C. R. M. Fernandes, Sergio Vinha +1

SYSTEC-ISR ARISE, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

UAV NavigationRobotic Inspection
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Feb 27, 2026

Autonomous Inspection of Power Line Insulators with UAV on an Unmapped Transmission Tower

This paper introduces an online inspection algorithm that enables an autonomous UAV to fly around a transmission tower and obtain detailed inspection images without a prior map of the tower. Our algorithm relies on camera-LiDAR sensor fusion for online detection and localization of insulators. In particular, the algorithm is based on insulator detection using a convolutional neural network, projection of LiDAR points onto the image, and filtering them using the bounding boxes. The detection pipeline is coupled with several proposed insulator localization methods based on DBSCAN, RANSAC, and PCA algorithms. The performance of the proposed online inspection algorithm and camera-LiDAR sensor fusion pipeline is demonstrated through simulation and real-world flights. In simulation, we showed that our single-flight inspection strategy can save up to 24 % of total inspection time, compared to the two-flight strategy of scanning the tower and afterwards visiting the inspection waypoints in the optimal way. In a real-world experiment, the best performing proposed method achieves a mean horizontal and vertical localization error for the insulator of 0.16 +- 0.08 m and 0.16 +- 0.11 m, respectively. Compared to the most relevant approach, the proposed method achieves more than an order of magnitude lower variance in horizontal insulator localization error.

Václav Riss, Vít Krátký, Robert Pěnička +1

Robotic InspectionIndustrial InspectionUAV NavigationCamera-LiDAR Fusion
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Feb 27, 2026

Acceleration-Based Control of Fixed-Wing UAVs for Guidance Applications

Acceleration-commanded guidance laws (e.g., proportional navigation) are attractive for high-level decision making, but their direct deployment on fixed-wing UAVs is challenging because accelerations are not directly actuated and must be realized through attitude and thrust under flight-envelope constraints. This paper presents an acceleration-level outer-loop control framework that converts commanded tangential and normal accelerations into executable body-rate and normalized thrust commands compatible with mainstream autopilots (e.g., PX4/APM). For the normal channel, we derive an engineering mapping from the desired normal acceleration to roll- and pitch-rate commands that regulate the direction and magnitude of the lift vector under small-angle assumptions. For the tangential channel, we introduce an energy-based formulation inspired by total energy control and identify an empirical thrust-energy acceleration relationship directly from flight data, avoiding explicit propulsion modeling or thrust bench calibration. We further discuss priority handling between normal and tangential accelerations under saturation and non-level maneuvers. Extensive real-flight experiments on a VTOL fixed-wing platform demonstrate accurate acceleration tracking and enable practical implementation of proportional navigation using only body-rate and normalized thrust interfaces.

Jixiang Wang, Siyuan Yang, Ziyi Wu +5

School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China. · Autonomous Robotics Research Centre, Technology Innovation Institute, P.O.Box: 9639, Masdar City, Abu Dhabi, United Arab Emirates.

Aerial RoboticsUAV Navigation
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Feb 26, 2026

SCOPE: Skeleton Graph-Based Computation-Efficient Framework for Autonomous UAV Exploration

Autonomous exploration in unknown environments is key for mobile robots, helping them perceive, map, and make decisions in complex areas. However, current methods often rely on frequent global optimization, suffering from high computational latency and trajectory oscillation, especially on resource-constrained edge devices. To address these limitations, we propose SCOPE, a novel framework that incrementally constructs a real-time skeletal graph and introduces Implicit Unknown Region Analysis for efficient spatial reasoning. The planning layer adopts a hierarchical on-demand strategy: the Proximal Planner generates smooth, high-frequency local trajectories, while the Region-Sequence Planner is activated only when necessary to optimize global visitation order. Comparative evaluations in simulation demonstrate that SCOPE achieves competitive exploration performance comparable to state-of-the-art global planners, while reducing computational cost by an average of 86.9%. Real-world experiments further validate the system's robustness and low latency in practical scenarios.

Kai Li, Shengtao Zheng, Linkun Xiu +4

Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639956

Autonomous Robot ExplorationRobot NavigationUAV NavigationMotion PlanningGraph-Based Planning
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Feb 23, 2026

Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation

Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, and strict payload and compute constraints. While prior unmanned aerial vehicle (UAV)-based olfaction systems have demonstrated gas distribution mapping or reactive plume tracing, they rely on predefined coverage patterns, external infrastructure, or extensive sensing and coordination. In this work, we present a complete, open-source UAV system for online odor source localization using a minimal sensor suite. The system integrates custom olfaction hardware, onboard sensing, and a learning-based navigation policy trained in simulation and deployed on a real quadrotor. Through our minimal framework, the UAV is able to navigate directly toward an odor source without constructing an explicit gas distribution map or relying on external positioning systems. Vision is incorporated as an optional complementary modality to accelerate navigation under certain conditions. We validate the proposed system through real-world flight experiments in a large indoor environment using an ethanol source, demonstrating consistent source-finding behavior under realistic airflow conditions. The primary contribution of this work is a reproducible system and methodological framework for UAV-based olfactory navigation and source finding under minimal sensing assumptions. We elaborate on our hardware design and open source our UAV firmware, simulation code, olfaction-vision dataset, and circuit board to the community. Code, data, and designs will be made available at https://github.com/KordelFranceTech/ChasingGhosts.

Kordel K. France, Ovidiu Daescu, Latifur Khan +1

Dept. of Computer Science University of Texas at Dallas Richardson, TX, USA

Robot NavigationUAV Navigation
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Feb 23, 2026

Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility. Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence beyond conventional optimization-based and learning-based approaches. By integrating LLMs into UAV systems, advanced environmental understanding, swarm coordination, mobility optimization, and high-level task reasoning can be achieved, thereby allowing more adaptive and context-aware aerial operations. This survey systematically explores the intersection of LLMs and UAV technologies and proposes a unified framework that consolidates existing architectures, methodologies, and applications for UAVs. We first present a structured taxonomy of LLM adaptation techniques for UAVs, including pretraining, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering, along with key reasoning capabilities such as Chain-of-Thought (CoT) and In-Context Learning (ICL). We then examine LLM-assisted UAV communications and operations, covering navigation, mission planning, swarm control, safety, autonomy, and network management. After that, the survey further discusses Multimodal LLMs (MLLMs) for human-swarm interaction, perception-driven navigation, and collaborative control. Finally, we address ethical considerations, including bias, transparency, accountability, and Human-in-the-Loop (HITL) strategies, and outline future research directions. Overall, this work positions LLM-assisted UAVs as a foundation for intelligent and adaptive aerial systems.

Yousef Emami, Hao Zhou, Radha Reddy +5

Language Model-Based ControlUAV NavigationLarge Language Model-Based Robot Planning
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Feb 19, 2026

Bluetooth Phased-array Aided Inertial Navigation Using Factor Graphs: Experimental Verification

Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such as warehouse logistics, drone landings, and autonomous docking. Basing a navigation system off of commercial-off-the-shelf components may reduce the barrier of entry for phased-array radio navigation systems, albeit at the cost of significantly noisier measurements and relatively short feasible range. In this paper, we compare robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight. We evaluate performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.

Glen Hjelmerud Mørkbak Sørensen, Torleiv H. Bryne, Kristoffer Gryte +1

Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU) NO-7491 Trondheim, Norway

GNSS-Denied LocalizationInertial NavigationUAV NavigationRobust EstimationFactor Graph Optimization
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Feb 17, 2026

Hybrid F' and ROS2 Architecture for Vision-Based Autonomous Flight: Design and Experimental Validation

Autonomous aerospace systems require architectures that balance deterministic real-time control with advanced perception capabilities. This paper presents an integrated system combining NASA's F' flight software framework with ROS2 middleware via Protocol Buffers bridging. We evaluate the architecture through a 32.25-minute indoor quadrotor flight test using vision-based navigation. The vision system achieved 87.19 Hz position estimation with 99.90% data continuity and 11.47 ms mean latency, validating real-time performance requirements. All 15 ground commands executed successfully with 100 % success rate, demonstrating robust F'--PX4 integration. System resource utilization remained low (15.19 % CPU, 1,244 MB RAM) with zero stale telemetry messages, confirming efficient operation on embedded platforms. Results validate the feasibility of hybrid flight-software architectures combining certification-grade determinism with flexible autonomy for autonomous aerial vehicles.

Abdelrahman Metwally, Monijesu James, Aleksey Fedoseev +3

Skolkovo Institute of Science and Technology Moscow, Russia

RoboticsUAV Navigation
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Feb 14, 2026

FC-Vision: Real-Time Visibility-Aware Replanning for Occlusion-Free Aerial Target Structure Scanning in Unknown Environments

Autonomous aerial scanning of target structures is crucial for practical applications, requiring online adaptation to unknown obstacles during flight. Existing methods largely emphasize collision avoidance and efficiency, but overlook occlusion-induced visibility degradation, severely compromising scanning quality. This study proposes FC-Vision, an on-the-fly visibility-aware replanning framework that proactively and safely prevents target occlusions while preserving full target coverage and efficiency of the original plan. Our approach explicitly enforces dense surface-visibility constraints to regularize replanning behavior in real-time via an efficient two-level decomposition: occlusion-free viewpoint repair that maintains coverage with minimal deviation from the nominal scan, followed by segment-wise clean-sensing connection in 5-DoF space. A plug-in integration strategy is also presented to seamlessly interface FC-Vision with existing UAV scanning systems without architectural changes. Comprehensive simulation and real-world evaluations show that FC-Vision consistently improves scanning quality under unexpected occluders, delivering a maximum coverage gain of 55.32% and a 73.17% reduction in the occlusion ratio, while achieving real-time performance with a moderate increase in flight time. The code has been released at https://github.com/FC-Family/FC-Vision.

Chen Feng, Yang Xu, Shaojie Shen

Dept. of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.

Next-Best-View PlanningUAV NavigationMotion Planning
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Feb 9, 2026

High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning

Quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in complex missions that demand reliable autonomous navigation and robust obstacle avoidance. However, traditional modular pipelines often incur cumulative latency, whereas purely reinforcement learning (RL) approaches typically provide limited formal safety guarantees. To bridge this gap, we propose an end-to-end RL framework augmented with model-based safety mechanisms. We incorporate physical priors in both training and deployment. During training, we design a physics-informed reward structure that provides global navigational guidance. During deployment, we integrate a real-time safety filter that projects the policy outputs onto a provably safe set to enforce strict collision-avoidance constraints. This hybrid architecture reconciles high-speed flight with robust safety assurances. Benchmark evaluations demonstrate that our method outperforms both traditional planners and recent end-to-end obstacle avoidance approaches based on differentiable physics. Extensive experiments demonstrate strong generalization, enabling reliable high-speed navigation in dense clutter and challenging outdoor forest environments at velocities up to 7.5 m/s}.

Jiarui Zhang, Chengyong Lei, Chengjiang Dai +4

Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · Differential Robotics, Hangzhou 311121, China.

RL for RoboticsUAV NavigationSafe RLVision-Based Robot ControlRobot Collision Avoidance
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Feb 1, 2026

SPOT: Spatio-Temporal Obstacle-free Trajectory Planning for UAVs in Unknown Dynamic Environments

We address the problem of reactive motion planning for quadrotors operating in unknown environments with dynamic obstacles. Our approach leverages a 4-dimensional spatio-temporal planner, integrated with vision-based Safe Flight Corridor (SFC) generation and trajectory optimization. Unlike prior methods that rely on map fusion, our framework is mapless, enabling collision avoidance directly from perception while reducing computational overhead. Dynamic obstacles are detected and tracked using a vision-based object segmentation and tracking pipeline, allowing robust classification of static versus dynamic elements in the scene. To further enhance robustness, we introduce a backup planning module that reactively avoids dynamic obstacles when no direct path to the goal is available, mitigating the risk of collisions during deadlock situations. We validate our method extensively in both simulation and real-world hardware experiments, and benchmark it against state-of-the-art approaches, showing significant advantages for reactive UAV navigation in dynamic, unknown environments.

Astik Srivastava, Thomas J Chackenkulam, Bitla Bhanu Teja +2

Robotics Research Center, IIIT Hyderabad, India · IIT-BHU, India

UAV NavigationSafe Motion PlanningMotion PlanningDynamic Obstacle Avoidance
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Feb 1, 2026

A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection

Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, temporary structures) invisible to traditional geometric sensors. In this paper, we propose a novel framework leveraging Remote Sensing (RS) imagery and Multimodal Large Language Models (MLLMs) for global context-aware landing site assessment. Unlike local geometric methods, our approach employs a coarse-to-fine pipeline: first, a lightweight semantic segmentation module efficiently pre-screens candidate areas; second, a vision-language reasoning agent fuses visual features with Point-of-Interest (POI) data to detect subtle hazards. To validate this approach, we construct and release the Emergency Landing Site Selection (ELSS) benchmark at https://github.com/chunlianghua/ELSS-dataset. ELSS includes a remote-sensing subset for simulation validation and a UAV aerial-video subset for real-world testing. Experiments demonstrate that our framework significantly outperforms geometric baselines in risk identification accuracy. Furthermore, qualitative results confirm its ability to generate human-like, interpretable justifications, enhancing trust in automated decision-making.

Chunliang Hua, Lei Zhang, Jiayang Sun +2

School of Information Science and Engineering, Southeast University, Nanjing 211189, China · LASER, International Digital Economy Academy, Shenzhen 510085, China · Department of Electronic Engineering, East China Normal University, Shanghai 200241, China

UAV NavigationMultimodal Large Language Models
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Dec 9, 2025

RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight

This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velocity Constraints (RVCs). Unlike most existing methods, the proposed approach relies solely on observable information about other robots, eliminating the need for excessive communication. The computationally efficient algorithm for computing RVCs, together with the direct integration of these constraints into the NMPC problem formulation at the controller level, allows the whole pipeline to run at 100 Hz. This high processing rate, combined with modeled nonlinear dynamics of the controlled Uncrewed Aerial Vehicles (UAVs), is a key feature that facilitates the use of the proposed approach for agile UAV flight. The proposed approach was evaluated through extensive simulations emulating real-world conditions in scenarios involving up to 10 UAVs and velocities of up to 25 m/s, and in real-world experiments with accelerations up to 30 m/s2^2. Comparison with the state of the art shows 31% improvement in terms of flight time reduction in challenging scenarios, while maintaining a collision-free navigation in all trials.

Vit Kratky, Robert Penicka, Parakh M. Gupta +2

Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic

Multi-Robot Motion PlanningNonlinear MPCUAV NavigationModel Predictive ControlSafe Motion PlanningRobot Collision Avoidance
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Nov 21, 2025

Reflection-Based Relative Localization for Cooperative UAV Teams Using Active Markers

Reflections of active markers in the environment are a common source of ambiguity in onboard visual relative localization. This work presents a novel approach that exploits these typically unwanted reflections for onboard relative localization in heterogeneous multi-UAV teams operating over specular, near-planar surfaces such as calm water and flat indoor floors. Using only a camera, active markers, and a single-beam rangefinder, the method requires no prior knowledge of robot size, marker configuration, or surface reflectance properties, and propagates the uncertainty introduced by surface irregularities, including those of dynamic water surfaces. We further characterize, analytically and experimentally, how surface tilt biases the estimate, and identify the slope range over which the level-surface assumption holds. Indoor multi-UAV and outdoor experiments validate the localization accuracy, demonstrating reliable operation across varying lighting conditions and greater effective range than state-of-the-art vision-based active-marker methods, with higher accuracy beyond 30 m. The project website with the video is available under the following link: https://timlakemann.github.io/reflection-based-relative-localization/.

Tim Lakemann, Daniel Bonilla Licea, Viktor Walter +1

Multi-Robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, Prague, Czech Republic · Mohammed VI Polytechnic University, Morocco

Relative Pose EstimationCooperative LocalizationUAV Navigation
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Oct 20, 2025

HumanHalo: Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC

Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on simplified 2D crowd navigation and fail to account for the full complexity of human body dynamics beyond root motion. We present HumanHalo, an MPC framework for 3D MAV navigation among humans that combines theoretical safety guarantees with data-driven models for realistic human motion forecasting. Our approach introduces a novel reachability-based safety formulation that constrains only the initial control input for safety while modeling its effects over the entire planning horizon, enabling safe yet efficient navigation. We validate HumanHalo in both simulated experiments using real human trajectories and in the real world, demonstrating its effectiveness across tasks ranging from goal-directed navigation to visual servoing for human tracking. While we apply our method to MAV in this work, it is generic and can be adapted to other platforms. Our results show that the method preserves safety without excessive conservatism, even under imperfect human prediction and in constrained workspaces, while remaining efficient enough for real-time onboard execution.

Simon Schaefer, Helen Oleynikova, Sandra Hirche +1

Technical University of Munich · ETH Zurich

Constrained Motion PlanningUAV NavigationSafe Robot NavigationSocial Robot NavigationRobot SafetyModel Predictive Control
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Oct 16, 2025

STITCHER: Constrained Trajectory Planning in Complex Environments with Real-Time Motion Primitive Search

Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy state or actuator constraints. Modern trajectory planning techniques primarily use numerical optimization, as they enable the systematic computation of high-quality, expressive trajectories that satisfy various constraints. However, stringent requirements on computation time and the risk of numerical instability can limit the use of optimization-based planners in safety-critical scenarios. This work presents an optimization-free planning framework called STITCHER that stitches short trajectory segments together with graph search to compute long-range, expressive, and near-optimal trajectories in real-time. STITCHER outperforms modern optimization-based planners through our innovative planning architecture and several algorithmic developments that make real-time planning possible. Extensive simulation testing is performed to analyze the algorithmic components that make up STITCHER, along with a thorough comparison with three state-of-the-art optimization planners. Simulation tests show that safe trajectories can be created within a few milliseconds for paths that span the entirety of two 50 m x 50 m environments. Hardware tests with a custom quadrotor verify that STITCHER can produce trackable paths in real-time while respecting nonconvex constraints, such as limits on tilt angle and motor forces, with flight speeds up to 63 km/h.

Helene J. Levy, Brett T. Lopez

VECTR Laboratory, University of California, Los Angeles, Los Angeles, CA, USA

Constrained Motion PlanningTrajectory GenerationUAV NavigationGraph-Based PlanningSafe Motion PlanningMotion Planning+1
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Sep 19, 2025

Real-Time Maneuver Planning for Fixed-Wing UAVs in Unsteady Flows Using a GPU-Accelerated Vortex Particle Model

Unsteady aerodynamic effects can have a profound impact on aerial vehicle flight performance, especially during agile maneuvers and in complex aerodynamic environments. In this paper, we present a real-time planning and control approach capable of reasoning about unsteady aerodynamics. Our approach relies on a lightweight GPU-accelerated vortex particle model (VPM) and a sampling-based policy optimization strategy capable of leveraging the VPM for predictive reasoning. Through hardware experiments, we show that by replanning with our unsteady aerodynamics model, we can improve the performance of a post-stall fixed-wing perching maneuver in the presence of unsteady environmental flow disturbances.

Ashwin Gupta, Kevin Wolfe, Gino Perrotta +1

Johns Hopkins University Whiting School of Engineering · Johns Hopkins University Applied Physics Lab

UAV NavigationMotion Planning
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Jun 26, 2025

Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments

Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation, yet global navigation satellite system (GNSS) spoofing attacks can induce out-of-distribution observation shifts that corrupt value estimation and degrade mission performance. Existing robust RL methods often lack generalization to unseen attacks. To address this, we propose a curriculum-guided adaptation framework that progressively exposes a robust policy to gradient-based adversarial observation perturbations of increasing intensity while aligning temporal-difference (TD) error distributions across curriculum stages. By preserving TD-error consistency rather than targeting specific attack models, the framework promotes resilience across attack conditions. We further derive a TD-space generalization certificate that bounds performance degradation when the TD-error distribution under a test-time attack remains sufficiently close to that of the final curriculum stage. We evaluate the framework in a UAV deconfliction environment with dynamic 3D obstacles under unseen fixed and dynamic GNSS spoofing attacks. Under fixed spoofing, the curriculum-adapted (CurAd) policy achieves approximately 99-100% mission success across evaluated traffic densities, compared with 20-24% for the best-performing robust baseline, while also outperforming the non-robust vanilla baseline. Under dynamic obstacle-luring spoofing, CurAd achieves the highest episodic rewards and reduces median mission completion steps by up to 22% relative to the best-performing robust RL baseline. These findings demonstrate that adversarial curriculum adaptation transfers resilience from synthetic perturbations to unseen GNSS spoofing attacks without attack-specific retraining, enhancing robust autonomous UAV navigation.

Deepak Kumar Panda, Adolfo Perrusquia, Weisi Guo

the Faculty of Engineering and Applied Sciences, Cranfield University, MK43 0AL Cranfield, U.K

Robust RLUAV NavigationCurriculum RL
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