Vision-and-language navigation (VLN) is a long-standing challenge in autonomous robotics, aiming to empower agents with the ability to follow human instructions while navigating complex environments. Two key bottlenecks remain in this field: generalization to out-of-distribution environments and reliance on fixed discrete action spaces. To address these challenges, we propose Vision-Language Fly (VLFly), a framework tailored for Unmanned Aerial Vehicles (UAVs) to execute language-guided flight. Without the requirement for localization or active ranging sensors, VLFly outputs continuous velocity commands purely from egocentric observations captured by an onboard monocular camera. The VLFly integrates three modules: an instruction encoder based on a large language model (LLM) that reformulates high-level language into structured prompts, a goal retriever powered by a vision-language model (VLM) that matches these prompts to goal images via vision-language similarity, and a waypoint planner that generates executable trajectories for real-time UAV control. VLFly is evaluated across diverse simulation environments without additional fine-tuning and consistently outperforms all baselines. Moreover, real-world VLN tasks in indoor and outdoor environments under direct and indirect instructions demonstrate that VLFly achieves robust open-vocabulary goal understanding and generalized navigation capabilities, even in the presence of abstract language input.
OpenObject-NAV: Open-Vocabulary Object-Oriented Navigation Based on Dynamic Carrier-Relationship Scene Graph
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；School of Mechanical Engineering, University of Nottingham Ningbo China（宁波诺丁汉大学机械工程学院）
Centre for Automation and Robotics, Polytechnic University of Madrid（马德里理工大学自动化与机器人中心）
This article proposes a method to diminish the pose (position plus attitude) drift experienced by an SVO (Semi-Direct Visual Odometry) based visual navigation system installed onboard a UAV (Unmanned Air Vehicle) by supplementing its pose estimation non linear optimizations with priors based on the outputs of a GNSS (Global Navigation Satellite System) Denied inertial navigation system. The method is inspired in a PI (Proportional Integral) control system, in which the attitude, altitude, and rate of climb inertial outputs act as targets to ensure that the visual estimations do not deviate far…
In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target re-project…
ETH Zürich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful external computation servers. In this work, we present what is, to the best of our knowledge, the first 27g nano-UAV system able to run aboard an end-to-end, closed-loop visual pipeline for autonomous navigation based on a state-of-the-art deep-learning algorithm, built upon the op…
Survey Copter / Airbus Defense and Space（空客防务与航天公司Survey Copter部门）；Hubert Curien Laboratory（于贝尔·居里安实验室）；Center for Research in Computer Vision, UCF（中佛罗里达大学计算机视觉研究中心）
In recent years, consumer Unmanned Aerial Vehicles have become very popular, everyone can buy and fly a drone without previous experience, which raises concern in regards to regulations and public safety. In this paper, we present a novel approach towards enabling safe operation of such vehicles in urban areas. Our method uses geodetically accurate dataset images with Geographical Information System (GIS) data of road networks and buildings provided by Google Maps, to compute a weighted A* shortest path from start to end locations of a mission. Weights represent the potential risk of injuries …
LogisticsVLN: Vision-Language Navigation For Low-Altitude Terminal Delivery Based on Agentic UAVs
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology（澳门科技大学创新工程学院工程科学系）；China Ship Research and Development Academy（中国船舶科研 Academy）；Obuda University（奥布达大学）；State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences（中国科学院复杂系统管理与控制国家重点实验室）
Object-Oriented Semantic Mapping for Reliable UAVs Navigation
University of Engineering and Technology, Vietnam National University Hanoi（越南国立大学河内工程技术大学）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）
Uncertainty-Informed Active Perception for Open Vocabulary Object Goal Navigation
Center for Robotics, University of Bonn（波恩大学机器人中心）；MAVLab, TU Delft（代尔夫特理工大学MAVLab）；Lamarr Institute for Machine Learning and Artificial Intelligence（机器学习与人工智能拉马尔研究所）
R2F: Repurposing Ray Frontiers for LLM-free Object Navigation
Department of Computer, Automation and Management Engineering, Sapienza University of Rome（罗马萨皮恩扎大学计算机、自动化与管理工程系）；Department of Electronics and Automation, Mohamed Khider University of Biskra（比斯克拉Mohamed Khider大学电子与自动化系）；International University of Rome UNINT, Rome（罗马国际大学UNINT）
Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；Swiss Federal Institute of Technology（瑞士联邦理工学院）；ETH Zürich（苏黎世联邦理工学院）；FieldAI Inc.（FieldAI公司）；Stanford University（斯坦福大学）；University of California Berkeley（加州大学伯克利分校）
FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；College of Information Science and Technology, Eastern Institute of Technology（东方理工学院信息科学与技术学院）
OpenMap: Instruction Grounding via Open-Vocabulary Visual-Language Mapping
School of Software, Tsinghua University（清华大学软件学院）；School of computer science and engineering, Central South University（中南大学计算机科学与工程学院）；Inspur Yunzhou Industrial Internet Co., Ltd（Inspur Yunzhou工业互联网有限公司）；Tsinghua University（清华大学）
Polytechnic University of Madrid（马德里理工大学）；Boeing Research & Technology Europe（波音欧洲研究与技术公司）；National University of Distance Education（国立远程教育大学）；Center for Automation and Robotics (UPM-CSIC)（自动化与机器人中心（UPM-CSIC））
This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs (Unmanned Air Vehicles). In addition to accelerometers and gyroscopes, the filter takes advantage of sensors usually present onboard these platforms, such as magnetometers, Pitot tube, and air vanes, and aims to minimize the attitude error and reduce the position drift (both horizontal and vertical) with the dual objective of improving the aircraft…
Institute for Systems Analysis of Russian Academy of Sciences（俄罗斯科学院系统分析研究所）；Soloviev Rybinsk State Aviation Technical University（索洛维约夫雷宾斯克国立航空技术大学）；NPP SATEK plus（SATEK plus公司）
The problem of developing distributed control and navigation system for quadrotor UAVs operating in GPS-denied environments is addressed in the paper. Cooperative navigation, marker detection and mapping task solved by a team of multiple unmanned aerial vehicles is chosen as demo example. Developed intelligent control system complies with on 4D\RCS reference model and its implementation is based on ROS framework. Custom implementation of EKF-based map building algorithm is used to solve marker detection and map building task.
VIO has been widely used and researched to control and aid the automation of navigation of robots especially in the absence of absolute position measurements, such as GPS. However, when observable landmarks in the scene lie far away from the robot's sensor suite, as it is the case at high altitude flights, the fidelity of estimates and the observability of the metric scale degrades greatly for these methods. Aiming to tackle this issue, in this article, we employ two UAVs equipped with one monocular camera and one IMU each, to exploit their view overlap and relative distance measurements betwe…
Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a technique to distinguish dynamic obstacles from static ones with only point cloud input. Then, a computationally efficient obstacle avoidance motion planning approach is proposed and it is in line with an improved relative velocity method. The approach is able to avoid both static obstacles and dynamic ones in the same framework. For static and dynamic obstacles, the collision check and motion constraints are different, and …
Unmanned aerial vehicles (UAVs) are becoming largely ubiquitous with an increasing demand for aerial data. Accurate navigation and localization, required for precise data collection in many industrial applications, often relies on RTK GNSS. These systems, able of centimeter-level accuracy, require a setup and calibration process and are relatively expensive. This paper addresses the problem of accurate positioning and navigation of UAVs through cooperative localization. Inexpensive ultra-wideband (UWB) transceivers installed on both the UAV and a support ground robot enable centimeter-level re…
We study the problem of infrastructure inspection using an Unmanned Aerial Vehicle (UAV) in box girder bridge environments. We consider a scenario where the UAV needs to fully inspect box girder bridges and localize along the bridge surface when standard methods like GPS and optical flow are denied. Our method for overcoming the difficulties of box girder bridges consist of creating local navigation routines, a supervisor, and a planner. The local navigation routines use two 2D Lidars for girder and column flight. For switching between local navigation routines we implement a supervisor which …
Johns Hopkins University Applied Physics Lab（约翰斯·霍普金斯大学应用物理实验室）
Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) to maneuver in constrained spaces through the use of direct nonlinear model predictive control (NMPC). However, this approach has been limited to a priori known maps and ground truth state measurements. In this paper, we present a direct NMPC approach that leverages NanoMap, a light-weight point-cloud mapping framework to generate collision-free trajectories using onboard stereo vision. We first explore our approach in simulation and demonstrate that our algorithm is sufficient to enable vision-based navigation in urban env…
Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. The work presented in this report contributes towards the state-of-the-art in UAV contro…
This study presents a new methodology for learning-based motion planning for autonomous exploration using aerial robots. Through the reinforcement learning method of learning through trial and error, the action policy is derived that can guide autonomous exploration of underground and tunnel environments. A new Markov decision process state is designed to learn the robot's action policy by using simulation only, and the results are applied to the real-world environment without further learning. Reduce the need for the precision map in grid-based path planner and achieve map-less navigation. Th…
DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Irvine（加利福尼亚大学欧文分校）；George Washington University（乔治·华盛顿大学）；Beijing Sineva Robot Technology Co., Ltd（北京思知机器人科技有限公司）；University of Southern California（南加利福尼亚大学）；Santa Clara University（圣克拉拉大学）；Mangosuthu University of Technology（曼戈苏图科技大学）
Sapienza University of Rome（罗马大学）；IT+Robotics S.r.l.（IT+Robotics有限公司）
This work addresses the problem of coupling vision-based navigation systems for Unmanned Aerial Vehicles (UAVs) with robust obstacle avoidance capabilities. The former problem is solved by maximizing the visibility of the points of interest, while the latter is modeled by means of ellipsoidal repulsive areas. The whole problem is transcribed into an Optimal Control Problem (OCP), and solved in a few milliseconds by leveraging state-of-the-art numerical optimization. The resulting trajectories are well suited for reaching the specified goal location while avoiding obstacles with a safety margin…
Åbo Akademi University（奥布学术大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
With the growing popularity of Unmanned Aerial Vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation can not be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the…
Faculty of Electrical Engineering, Czech Technical University in Prague（捷克技术大学电气工程学院）
Autonomous operation of UAVs in a closed environment requires precise and reliable pose estimate that can stabilize the UAV without using external localization systems such as GNSS. In this work, we are concerned with estimating the pose from laser scans generated by an inexpensive and lightweight LIDAR. We propose a localization system for lightweight (under 200g) LIDAR sensors with high reliability in arbitrary environments, where other methods fail. The general nature of the proposed method allows deployment in wide array of applications. Moreover, seamless transitioning between different k…
