This paper presents a robotics vision-based heuristic reasoning system for underwater target tracking and navigation. This system is introduced to improve the level of automation of underwater Remote Operated Vehicles (ROVs) operations. A prototype which combines computer vision with an underwater robotics system is successfully designed and developed to perform target tracking and intelligent navigation. ...
Robotics Institute（机器人研究所）；University of Technology Sydney（悉尼科技大学）
Underwater visual-inertial navigation is challenging due to the poor visibility and presence of outliers in underwater environments. The navigation performance is closely related to outlier detection and elimination. Existing methods assume the inertial odometry is accurate enough for outlier detection, which is not valid for low-cost inertial applications. We propose a novel iterative smoothing and outlier detection method aiming for underwater navigation. Using the dataset collected from an underwater robot and fiducial markers, experimental results confirm that the method can successfully e…
University of Electronic Science and Technology of China（电子科技大学）；Dartmouth College（达特茅斯学院）
Underwater navigation presents several challenges, including unstructured unknown environments, lack of reliable localization systems (e.g., GPS), and poor visibility. Furthermore, good-quality obstacle detection sensors for underwater robots are scant and costly; and many sensors like RGB-D cameras and LiDAR only work in-air. To enable reliable mapless underwater navigation despite these challenges, we propose a low-cost end-to-end navigation system, based on a monocular camera and a fixed single-beam echo-sounder, that efficiently navigates an underwater robot to waypoints while avoiding nea…
UnderwaterVLA: Dual-brain Vision-Language-Action architecture for Autonomous Underwater Navigation
School of Engineering, Westlake University（西lake大学工程学院）；College of Information Science & Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；College of Environmental and Resource Sciences, Zhejiang University（浙江大学环境与资源科学学院）；Australian National University（澳大利亚国立大学）
Vassar College（瓦萨学院）；University of South Carolina（南卡罗来纳大学）
Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through …
Bio-inspired vehicles are currently leading the way in the quest to produce a vehicle capable of flight and underwater navigation. However, a fully functional vehicle has not yet been realized. We present the first fully functional vehicle platform operating in air and underwater with seamless transition between both mediums. These unique capabilities combined with the hovering, high maneuverability and reliability of multirotor vehicles, results in a disruptive technology for both civil and military application including air/water search and rescue, inspection, repairs and survey missions amo…
Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation.This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple object…
Universidade Federal de Santa Maria - UFSM（圣玛丽亚联邦大学）
Since the application of Deep Q-Learning to the continuous action domain in Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been qualitatively enhanced. Nowadays, modern Deep-RL can be successfully applied to solve a wide range of complex decision-making tasks for many types of vehicles. Based on this context, in this paper, we propose the use of Deep-RL to perform autonomous mapless navigation for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs), robots that can operate in both, air or water media. We developed two approaches, one deterministic an…
DUViN: Diffusion-Based Underwater Visual Navigation via Knowledge-Transferred Depth Features
Department of Electrical and Electronic Engineering, The University of Melbourne, Australia（墨尔本大学电子与电气工程系）；School of Mathematics and Statistics, The University of Melbourne, Australia（墨尔本大学数学与统计学学院）
University of Toronto（多伦多大学）；McGill University（麦吉尔大学）
We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose, safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by traini…
FGO-ILNS: Tightly Coupled Multi-Sensor Integrated Navigation System Based on Factor Graph Optimization for Autonomous Underwater Vehicle
Sun Yat-sen University（中山大学）；Shenzhen Key Laboratory of Navigation and Communication Integration（深圳导航与通信融合重点实验室）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））
Universidad Tecnológica del Uruguay (UTEC)（乌拉圭技术大学）；Centro de Ciências Computacionais (C3) of Universidade Federal do Rio Grande (FURG)（里奥格兰德联邦大学计算科学中心）；Universidade Federal de Santa Maria (UFSM)（圣玛丽亚联邦大学）
Previous works showed that Deep-RL can be applied to perform mapless navigation, including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs). This paper presents new approaches based on the state-of-the-art actor-critic algorithms to address the navigation and medium transition problems for a HUAUV. We show that a double critic Deep-RL with Recurrent Neural Networks improves the navigation performance of HUAUVs using solely range data and relative localization. Our Deep-RL approaches achieved better navigation and transitioning capabilities with a solid generalizatio…
This paper presents a deep learning approach to aid dead-reckoning (DR) navigation using a limited sensor suite. A Recurrent Neural Network (RNN) was developed to predict the relative horizontal velocities of an Autonomous Underwater Vehicle (AUV) using data from an IMU, pressure sensor, and control inputs. The RNN network is trained using experimental data, where a doppler velocity logger (DVL) provided ground truth velocities. The predictions of the relative velocities were implemented in a dead-reckoning algorithm to approximate north and east positions. The studies in this paper were twofo…
SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar
Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯A&M大学）；Aquaculture Robotics and Automation, SINTEF Ocean（水产养殖机器人与自动化，SINTEF海洋）
Precise Time Delay Measurement and Compensation for Tightly Coupled Underwater SINS/piUSBL Navigation
State Key Laboratory of Ocean Sensing, Ocean College of Zhejiang University（浙江大学海洋学院海洋传感国家重点实验室）；School of Oceanography, Shanghai Jiao Tong University（上海交通大学海洋学院）；Donghai Laboratory（东海实验室）
STERN: Simultaneous Trajectory Estimation and Relative Navigation for Autonomous Underwater Proximity Operations
Centre for Naval Architecture, KTH Royal Institute of Technology（KTH皇家理工学院海军建筑中心）；Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology（KTH皇家理工学院机器人、感知与学习系）
This article focuses on feature-based underwater localization and navigation for autonomous underwater vehicles (AUVs) using 2D imaging sonar measurements. The sparsity of underwater acoustic features and the loss of elevation angle in sonar images may introduce wrong feature matches or insufficient features for optimization-based underwater localization (i.e. under-constrained/degeneracy cases). This motivates us to propose a novel inertial-aided sliding window optimization framework to improve the estimation accuracy and the robustness to front-end outliers. Concretely, we first discriminate…
Digital Twins Below the Surface: Enhancing Underwater Teleoperation
School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）；School of Mathematics and Computer Sciences, Heriot-Watt University（赫瑞瓦特大学数学与计算机科学学院）
University of Technology Sydney（悉尼科技大学）；Blue Ocean Monitoring Ltd（蓝海监测有限公司）；Defence Science and Technology Group（国防科技集团）
Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process~(GP)-based expectation-maximisation~(EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean curre…
Autonomous underwater vehicles (AUV) perform various applications such as seafloor mapping and underwater structure health monitoring. Commonly, an inertial navigation system aided by a Doppler velocity log (DVL) is used to provide the vehicle's navigation solution. In such fusion, the DVL provides the velocity vector of the AUV, which determines the navigation solution's accuracy and helps estimate the navigation states. This paper proposes BeamsNet, an end-to-end deep learning framework to regress the estimated DVL velocity vector that improves the accuracy of the velocity vector estimate, a…
Compressed delayed-information projection for six-degree-of-freedom underwater vehicle navigation under delayed acoustic positioning
Department of Electrical Engineering and Electronics, School of Engineering, University of Liverpool（利物浦大学工程学院电气工程与电子系）；Jiangsu JITRI Tsingunited Intelligent Control Technology Co., Ltd.（江苏集萃清联智控科技有限公司）；School of Computer Science and Informatics, University of Liverpool（利物浦大学计算机科学与信息学院）；ARIES Research Centre, Polytechnic School, Universidad Antonio de Nebrija（安东尼奥·德·内布里哈大学理工学院ARIES研究中心）
Simultaneous localization and mapping (SLAM) frameworks for autonomous navigation rely on robust data association to identify loop closures for back-end trajectory optimization. In the case of autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES), data association is particularly challenging due to the scarcity of identifiable landmarks in the seabed, the large drift in dead-reckoning navigation estimates to which AUVs are prone and the low resolution characteristic of MBES data. Deep learning solutions to loop closure detection have shown excellent performance on d…
French Research Institute for Exploitation of the Sea (IFREMER)（法国海洋开发研究院）；Research Centre in Underwater Robotics (CIRS), University of Girona（赫罗纳大学水下机器人研究中心）
Hyperspectral imaging has been increasingly used for underwater survey applications over the past years. As many hyperspectral cameras work as push-broom scanners, their use is usually limited to the creation of photo-mosaics based on a flat surface approximation and by interpolating the camera pose from dead-reckoning navigation. Yet, because of drift in the navigation and the mostly wrong flat surface assumption, the quality of the obtained photo-mosaics is often too low to support adequate analysis.In this paper we present an initial method for creating hyperspectral 3D reconstructions of u…
Raspi$^2$USBL: An open-source Raspberry Pi-Based Passive Inverted Ultra-Short Baseline Positioning System for Underwater Robotics
State Key Laboratory of Ocean Sensing, Ocean College of Zhejiang University（浙江省海洋传感重点实验室，浙江大学海洋学院）；School of Oceanography, Shanghai Jiao Tong University（上海交通大学海洋学院）
USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（认知与决策智能复杂系统重点实验室，自动化研究所，中国科学院）；Baidu Inc.（百度公司）；The School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers
Shenzhen Key Laboratory of Robotics Perception and Intelligence（深圳机器人感知与智能重点实验室）；Department of Electronic and Electrical Engineering（电子与电气工程系）；Southern University of Science and Technology（南方科技大学）；Department of Electronic and Computer Engineering（电子与计算机工程系）；Hong Kong University of Science and Technology（香港科学与技术大学）；Jiaxing Research Institute（嘉兴研究院）；Department of Electronic Engineering（电子工程系）；The Chinese University of Hong Kong（香港中文大学）
On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine
Hamlyn Centre for Robotic Surgery, Imperial College London（机器人手术哈明中心，帝国理工学院伦敦分校）；Center for Engineering and Industrial Development (CIDESI)（工程与工业发展中心（CIDESI））
Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders
Oxford Robotics Institute（牛津机器人研究所）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Optiver（Optiver公司）；Stateful Robotics（Stateful Robotics公司）；National Oceanography Centre（国家海洋研究中心）；School of Environmental Sciences, University of East Anglia（东安格利亚大学环境科学学院）；Scottish Association for Marine Science（苏格兰海洋科学协会）
