UAV Navigation

UAV: Unmanned Aerial Vehicle

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

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

Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning
May 23, 2026Shiladitya Dutta, Aayush Gupta, Varun Saran +1RL for Robotics UAV Navigation
College of Engineering, Department of Electrical Engineering and Computer Science, University of California Berkeley
Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping
May 22, 2026Nitin Vegesna, Avideh ZakhorAerial Robotics UAV Navigation
Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA, USA
KIO-planner: Attention-Guided Single-Stage Motion Planning with Dual Mapping for UAV Navigation
May 19, 2026Dexing Yao, Haochen Li, Junhao Wei +11Constrained Motion Planning Kinodynamic Motion Planning
Faculty of Applied Sciences Macao Polytechnic University Macao, China · College of Animal Science and Technology Zhongkai University of Agriculture and Engineering Guangzhou, China · School of Economics and Management South China Normal University Macao, China +1
FlyMirage: A Fully Automated Generation Pipeline for Diverse and Scalable UAV Flight Data via Generative World Model
May 19, 2026Jinhan Li, Xijie Huang, Weiqi Gai +9UAV Navigation Synthetic Data Generation for Robotics
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China. · Differential Robotics, Hangzhou 311121, China.
Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments
May 19, 2026Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy +2RL Control Robot Trajectory Tracking
Robotics and AI group, in the Department of Computer Science, Electrical and Space Engineering at Luleåa University of Technology, Sweden
CosFly: Plan in the Matrix, Fly in the World
May 18, 2026Hanxuan Chen, Xiangyue Wang, Songsheng Cheng +8Trajectory Generation UAV Navigation
Autel Robotics · Nanjing University · Peking University +2
Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation
May 18, 2026Kenan Majewski, Marcin ŻugajKalman Filtering UAV Navigation
Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, Nowowiejska 24, Warsaw, 00-665, Poland
CaMeRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments
May 14, 2026Hong Hong, Feiyu Liao, Yongheng Liang +3RL for Robotics Reinforcement Learning
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China, and the Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
Loiter UAV Reinsertion Guidance for Fixed-wing UAV Corridors
May 13, 2026Pradeep J, Kedarisetty Siddhardha, Ashwini RatnooCollision Avoidance UAV Navigation
B.Tech (Honours) Student, Department of Mechatronics Engineering · Assistant Professor, Department of Mechatronics Engineering · Professor, Department of Aerospace Engineering
LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration
May 13, 2026Jonathan A. Diller, Fernando Cladera, Camillo J. Taylor +1Trajectory Generation Large Language Model-Based Robot Planning
GRASP Laboratory, University of Pennsylvania
Uncertainty-Aware 3D Position Refinement for Multi-UAV Systems
May 13, 2026Hosam Alamleh, Damir PulatovMulti-Robot Systems UAV Navigation
Computer Science University of North Carolina Wilmington Wilmington, North Carolina, USA
Galilean State Estimation for Inertial Navigation Systems with Unknown Time Delay
May 13, 2026Giulio Delama, Martin Scheiber, Yixiao Ge +3UAV Navigation Equivariant Filtering
Control of Networked Systems Group University of Klagenfurt 9020 Klagenfurt, Austria · Systems Theory and Robotics Group Australian National University ACT, 2601, Australia · I3S, CNRS, Universit´e Cˆote d’Azur and Institut Universitaire de France 06900 Sophia Antipolis, France
Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity
May 12, 2026Zijiang Yan, Hao Zhou, Wael Jaafar +4Multi-Objective Reinforcement Learning Language Model-Based Control
Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada · Samsung Research America, Toronto, ON, Canada · Department of Software and IT Engineering, École de technologie supérieure (ÉTS), University of Quebec, Montreal, QC, Canada +1
ASIP-Planner: Adaptive Planning for UAV Surface Inspection in Partially Known Indoor Environments
May 11, 2026Hanyu Jin, Zhefan Xu, Haoyu Shen +3Next-Best-View Planning UAV Navigation
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA 95616, USA
Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
May 11, 2026Haechan Mark Bong, Giovanni BeltrameUAV Navigation Convolutional Autoencoder
Department of Computer Engineering and Software Engineering, Polytechnique Montréal, QC., Canada. · MILA, QC., Canada.
A Cross-Layered Multi-Drone Coordination for Medical Supply Delivery during Disaster Response Management
May 10, 2026Aneesh Calyam, Subrahmanya Chandra Bhamidipati, Zack Murry +1Aerial Robotics UAV Navigation
University of Missouri–Columbia, Columbia, MO 65211, USA
AERO-VIS: Asynchronous Event-based Real-time Onboard Visual-Inertial SLAM
May 8, 2026Yannick Burkhardt, Sebastián Barbas Laina, Simon Boche +2Event-Based Vision UAV Navigation
Mobile Robotics Lab, ETH Zürich · Technical University of Munich · Munich Center for Machine Lerning (MCML) +1
Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance
May 8, 2026Zirui Wang, Xinjia Luo, Haotian Sun +3Robot Navigation UAV Navigation
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
Robust Visual SLAM for UAV Navigation in GPS-Denied and Degraded Environments: A Multi-Paradigm Evaluation and Deployment Study
May 5, 2026Prasoon Kumar, Akshay Deepak, Sandeep KumarUAV Navigation GNSS-Denied Localization
National Institute of Technology Patna, Bihar, India · Central Research Laboratory, Bharat Electronics Ltd., Ghaziabad, Uttar Pradesh, India
SAGA: A Robust Self-Attention and Goal-Aware Anchor-based Planner for Safe UAV Autonomous Navigation
May 4, 2026Junhao Wei, Yanxiao Li, Dexing Yao +9Self-Attention Roadmap-Based Motion Planning
Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China · Pazhou Lab (Huangpu), Guangzhou 510555, China · College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China +3
Zero-Shot, Safe and Time-Efficient UAV Navigation via Potential-Based Reward Shaping, Control Lyapunov and Barrier Functions
May 3, 2026Ashik Abrar Naeem, Mohammad Ariful HaqueRL Control Control Lyapunov Functions
Flying by Inference: Active Inference World Models for Adaptive UAV Swarms
Apr 30, 2026Kaleem Arshid, Ali Krayani, Lucio Marcenaro +2Multi-Robot Motion Planning World Models for Robotics
Department of Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy · Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Carlos III University of Madrid, 28911 Legan´es, Spain
Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction
Apr 30, 2026Wentao Chen, Jingtang Chen, Mingjian Fu +4Reinforcement Learning UAV Navigation
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
Apr 29, 2026Mahya Ramezani, Holger VoosReinforcement Learning UAV Navigation
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.
Lights Out: A Nighttime UAV Localization Framework Using Thermal Imagery and Semantic 3D Maps
Apr 29, 2026Ryan Allen, Melissa GreeffUAV Navigation GNSS-Denied Localization
Robora Lab (www.roboralab.com), Queen’s University · affiliated with Ingenuity Labs Research Institute
Slot-hopping Enabled Loiter Guidance and Automation for Fixed-wing UAV Corridors
Apr 28, 2026Pradeep J, Siddhardha Kedarisetty, Ashwini RatnooUAV Navigation Multi-Robot Coordination
Indian Institute of Technology Bhilai, Bhilai, 491002, India · Indian Institute of Science, Bengaluru, 560012, India
Optimal UGV-UAV Cooperative Partitioning and Inspection of Shortest Paths
Apr 28, 2026Ninh Nguyen, Srinivas AkellaCompetitive Analysis UAV Navigation
Self-Predictive Representation for Autonomous UAV Object-Goal Navigation
Apr 22, 2026Angel Ayala, Donling Sui, Francisco Cruz +3UAV Navigation Predictive Representation Learning
Escola Polit´ecnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil · School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia · Escuela de Ingenier´ıa, Universidad Central de Chile, Santiago, Chile
Onboard Wind Estimation for Small UAVs Equipped with Low-Cost Sensors: An Aerodynamic Model-Integrated Filtering Approach
Apr 22, 2026Bingchen Cheng, Tielin Ma, Jingcheng Fu +2UAV Navigation Extended Kalman Filtering
Shenyuan Honors College, Beihang University, Beijing, 100191, China · Institute of Unmanned System, Beihang University, Beijing 100191 · School of Aeronautical Science and Engineering, Beihang University, Beijing 100191, China
Autonomous UAV Pipeline Near-proximity Inspection via Disturbance-Aware Predictive Visual Servoing
Apr 21, 2026Wen Li, Hui Wang, Jinya Su +3UAV Navigation Industrial Inspection
School of Automation, Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing 210096, China · Department of Aeronautical and Automotive Engineering, Loughborough University, LE11 3TU, United Kingdom · Research Centre for Low Altitude Economy and the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China

May 23, 2026J/K move · Enter open · S save

Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning

Shiladitya Dutta, Aayush Gupta, Varun Saran, Avideh Zakhor

College of Engineering, Department of Electrical Engineering and Computer Science, University of California Berkeley

RL for Robotics UAV Navigation Visuomotor Policy Learning Dynamic Obstacle Avoidance Robust RL

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Although quadcopters boast impressive traversal capabilities enabled by their omnidirectional maneuverability, the need for continuous pilot control in complex environments impedes their application in GNSS and telemetry-denied scenarios. To this end, we propose a novel sensorimotor policy that uses stereo-vision depth and visual-inertial odometry (VIO) to autonomously navigate through obstacles in an unknown environment to reach a goal point. The policy is comprised of a pre-trained autoencoder as the perception head followed by a planning and control LSTM network which outputs velocity commands that can be followed by an off-the-shelf commercial drone. We leverage reinforcement and privileged learning paradigms to train the policy in simulation through a two-stage process: 1) initial training with optimal trajectories generated by a global motion planner acting as a supervisory backbone, 2) further fine-tuning in a curriculum environment. To bridge the sim-to-real gap, we employ domain randomization and reward shaping to create a policy that is both robust to noise and domain shift. In outdoor experiments, our approach achieves successful zero-shot transfer to both obstacle environments and a drone platform that were never encountered during training.