USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Zhejiang University（浙江大学）；College of Information Science & Electronic Engineering（信息科学与电子工程学院）；New Jersey Institute of Technology（新泽西理工学院）
Autonomous underwater vehicles (AUVs) are valuable for ocean exploration due to their flexibility and ability to carry communication and detection units. Nevertheless, AUVs alone often face challenges in harsh and extreme sea conditions. This study introduces a unmanned surface vehicle (USV)-AUV collaboration framework, which includes high-precision multi-AUV positioning using USV path planning via Fisher information matrix optimization and reinforcement learning for multi-AUV cooperative tasks. Applied to a multi-AUV underwater data collection task scenario, extensive simulations validate the framework's feasibility and superior performance, highlighting exceptional coordination and robustness under extreme sea conditions. To accelerate relevant research in this field, we have made the simulation code (demo version) available as open-source.
Never too Cocky to Cooperate: An FIM and RL-based USV-AUV Collaborative System for Underwater Tasks in Extreme Sea Conditions
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Department of Data and Systems Engineering, The University of Hong Kong（数据与系统工程系，香港大学）；School of Engineering Science, University of Chinese Academy of Sciences（工程科学学院，中国科学院大学）；Department of Data Science, New Jersey Institute of Technology（数据科学系，新泽西理工学院）
Zhejiang University（浙江大学）；Shenzhen Sensetime Technology CO.,LTD（深圳商汤科技有限公司）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Autonomous Underwater Vehicle-Manipulator systems (AUVMS) is a new tool for ocean exploration, the AUVMS path planning problem is addressed in this paper. AUVMS is a high dimension system with a large difference in inertia distribution, also it works in a complex environment with obstacles. By integrating the rapidly-exploring random tree(RRT) algorithm with the AUVMS kinematics model, the proposed RRTAUVMS algorithm could randomly sample in the configuration space(C-Space), and also grow the tree directly towards the workspace goal in the task space. The RRTAUVMS can also deal with the redund…
Flinders University（弗林德斯大学）；University of Washington（华盛顿大学）；Seattle Children's Research Institute（西雅图儿童研究所）
The AUV three-dimension path planning in complex turbulent underwater environment is investigated in this research, in which static current map data and uncertain static-moving time variant obstacles are taken into account. Robustness of AUVs path planning to this strong variability is known as a complex NP-hard problem and is considered a critical issue to ensure vehicles safe deployment. Efficient evolutionary techniques have substantial potential of handling NP hard complexity of path planning problem as more powerful and fast algorithms among other approaches for mentioned problem. For the…
Queensland University of Technology（昆士兰科技大学）；The Australian Institute of Marine Science（澳大利亚海洋科学研究所）
This paper presents a novel approach to underwater terrain mapping for Autonomous Underwater Vehicles (AUVs) operating in close proximity to complex 3D environments. The proposed methodology creates a probabilistic elevation map of the terrain using a monocular image learning-based scene range estimator as a sensor. This scene range estimator can filter transient objects such as fish and lighting variations. The mapping approach considers uncertainty in both the estimated scene range and robot pose as the AUV moves through the environment. The resulting elevation map can be used for reactive p…
Institute for Ocean Technology, National Research Council Canada（加拿大国家研究委员会海洋技术研究所）
This paper presents a mission system and the therein implemented algorithms for path planning in a time-varying environment based on graph methods. The basic task of the introduced path planning algorithms is to find a time-optimal path from a defined start position to a goal position with consideration of the time-varying ocean current for an autonomous underwater vehicle (AUV). Building on this, additional practice-oriented considerations in planning are discussed in this paper. Such points are the discussion of possible methods to accelerate the algorithms and the determination of the optim…
Five-Tiered Route Planner for Multi-AUV Accessing Fixed Nodes in Uncertain Ocean Environments
College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；National Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）
NATO STO Centre for Maritime Research and Experimentation (CMRE)（北约科技组织海事研究与实验中心）
In this paper we address the mine countermeasures (MCM) search problem for an autonomous underwater vehicle (AUV) surveying the seabed using a side-looking sonar. We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected. To assess the algorithm, we collected data from three at-sea experiments. The adaptive survey allowed the AUV to recover from a situation where the sensor range was overestimated and resulte…
Flinders University of South Australia（南澳大利亚弗林德斯大学）
An Autonomous Underwater Vehicle (AUV) should carry out complex tasks in a limited time interval. Since existing AUVs have limited battery capacity and restricted endurance, they should autonomously manage mission time and the resources to perform effective persistent deployment in longer missions. Task assignment requires making decisions subject to resource constraints, while tasks are assigned with costs and/or values that are budgeted in advance. Tasks are distributed in a particular operation zone and mapped by a waypoint covered network. Thus, design an efficient routing-task priority as…
Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle's mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the biogeography-based optimization (BBO) algorithm toward satisfying…
This paper presents a concept for a control System for an autonomous underwater vehicle under ice using a "SLOCUM" underwater glider. The project concept, the separate working tasks for the next one-and-a-half years and the first results will be presented. In this context the structure of the obstacle avoidance system and a simulator structure with a sensor and environment simulation as well as the interfaces to the glider hardware will be discussed. As a first result of the main research, a graph-based algorithm for the path planning in a time-varying environment (variable ocean field, moving…
This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum sol…
Expansion of today's underwater scenarios and missions necessitates the requestion for robust decision making of the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively…
In this paper, a real-time quasi-optimal trajectory planning scheme is employed to guide an autonomous underwater vehicle (AUV) safely into a funnel-shape stationary docking station. By taking advantage of the direct method of calculus of variation and inverse dynamics optimization, the proposed trajectory planner provides a computationally efficient framework for autonomous underwater docking in a 3D cluttered undersea environment. Vehicular constraints, such as constraints on AUV states and actuators; boundary conditions, including initial and final vehicle poses; and environmental constrain…
The Technology Innovation Institute（技术创新研究院）；SpinItalia（SpinItalia公司）
The Technology Innovation Institute in Abu Dhabi, United Arab Emirates, has recently finished the production and testing of a new unmanned surface vehicle, called Nukhada, specifically designed for autonomous survey, inspection, and support to underwater operations. This manuscript describes the main characteristics of the Nukhada USV, as well as some of the trials conducted during the development.
Modern autonomous underwater vehicles (AUVs) have advanced sensing capabilities including sonar, cameras, acoustic communication, and diverse bio-sensors. Instead of just sensing its environment and storing the data for post-Mission inspection, an AUV could use the collected information to gain an understanding of its environment, and based on this understanding autonomously adapt its behavior to enhance the overall effectiveness of its mission. Many such tasks are highly computation intensive. This paper presents the results of a case study that illustrates the effectiveness of an energy-awar…
Motion control of underwater robotic vehicles is a demanding task with great challenges imposed by external disturbances, model uncertainties and constraints of the operating workspace. Thus, robust motion control is still an open issue for the underwater robotics community. In that sense, this paper addresses the tracking control problem or 3D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles. In particular, a robust Nonlinear Model Predictive Control (NMPC) scheme is presented for the case of underactuated Autonomous Underwate…
OceanPlan: Hierarchical Planning and Replanning for Natural Language AUV Piloting in Large-scale Unexplored Ocean Environments
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；The Hong Kong University of Science and Technology（香港科技大学）；School of Electrical Engineering, University of Notre Dame（圣母大学电气工程学院）
Virginia Tech（弗吉尼亚理工大学）；US Naval Research Laboratory（美国海军研究实验室）
We present the results of experiments performed using a small autonomous underwater vehicle to determine the location of an isobath within a bounded area. The primary contribution of this work is to implement and integrate several recent developments real-time planning for environmental mapping, and to demonstrate their utility in a challenging practical example. We model the bathymetry within the operational area using a Gaussian process and propose a reward function that represents the task of mapping a desired isobath. As is common in applications where plans must be continually updated bas…
Real-Time Buoyancy Estimation for AUV Simulations Using Convex Hull-Based Submerged Volume Calculation
1 Department of Electrical \& Electronic Engineering, Shahjalal University of Science \& Technology；2 Department of Electrical \& Computer Engineering, Johns Hopkins University
针对NVIDIA Isaac Sim无原生浮力系统的问题，提出基于凸包的AUV浸没体积实时计算方法，结合横截面扩展降本，在SAUVC 2025定制AUV上实现实时高精度浮力仿真，无需预计算水动力模型。
Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际 Graduate School）；School of Engineering, WestLake University（西湖大学工程学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Ocean College, Zhejiang University（浙江大学海洋学院）；Department of Data Science, New Jersey Institute of Technology（新泽西理工学院数据科学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
ReefGlider: A highly maneuverable vectored buoyancy engine based underwater robot
Woods Hole Oceanographic Institution (WHOI)（伍兹霍尔海洋研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Massachusetts Institute of Technology（麻省理工学院）；Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program（麻省理工学院与伍兹霍尔海洋研究所联合项目）
Recently, reinforcement learning (RL) has been extensively studied and achieved promising results in a wide range of control tasks. Meanwhile, autonomous underwater vehicle (AUV) is an important tool for executing complex and challenging underwater tasks. The advances in RL offers ample opportunities for developing intelligent AUVs. This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking. To this end, we first present a concise introduction to the RL based control framework. Then, …
University of Coimbra（科英布拉大学）；Ingeniarius, Ltd.（Ingeniarius有限公司）
Autonomous Underwater Vehicles (AUVs) have the ability to operate in harsh underwater environments without endangering human lives in the process. Nevertheless, just like their ground and aerial counterparts, AUVs need to be able to estimate their own position. Yet, unlike ground and aerial robots, estimating the pose of AUVs is very challenging, with only a few high-cost technological solutions available in the market. In this paper, we present the development of a realistic underwater acoustic model, implemented within the Robot Operating System (ROS) and the Gazebo simulator framework, for …
University of Minnesota（明尼苏达大学）；Clarkson University（克拉克森大学）；University of Minnesota - Duluth（明尼苏达大学德卢斯分校）
In this paper we present LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 meters. We discuss the open and expandable design of this underwater robot, as well as the design of a simulator in Gazebo. Additionally, we explore the platform's preliminary local motion control and state estimation abilities, which enable it to perform maneuvers autonomously. In order to demonstrate its usefulness for a variety of tasks, we implement a variety of our previously presented human-robot interaction cap…
Technical University of Delft（代尔夫特理工大学）；University of Oslo（奥斯陆大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Once deployed in the real world, autonomous underwater vehicles (AUVs) are out of reach for human supervision yet need to take decisions to adapt to unstable and unpredictable environments. To facilitate research on self-adaptive AUVs, this paper presents SUAVE, an exemplar for two-layered system-level adaptation of AUVs, which clearly separates the application and self-adaptation concerns. The exemplar focuses on a mission for underwater pipeline inspection by a single AUV, implemented as a ROS2-based system. This mission must be completed while simultaneously accounting for uncertainties suc…
K J Somaiya College Of Engineering（K J Somaiya工程学院）
The paper deals with the software and the electronics unit for an autonomous underwater vehicle. The implementation in the electronics unit is the connection and communication between SBC, pixhawk controller and other sensory hardware and actuators. The major implementation of the software unit is the algorithm for object detection based on Convolutional Neural Network (CNN) and its models. The Hyperparameters were tuned according to Odroid Xu4 for various models. The maneuvering algorithm uses the MAVLink protocol of the ArduSub project for movement and its simulation.
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are used for a wide variety of missions related to exploration and scientific research. Successful navigation by these systems requires a good localization system. Kalman filter based localization techniques have been prevalent since the early 1960s and extensive research has been carried out using them, both in development and in design. It has been found that the use of a dynamic model (instead of a kinematic model) in the Kalman filter can lead to more accurate predictions, as the dynamic model takes the forces acti…
CYBRES GmbH（CYBRES有限公司）；Research Center of Advanced Robotics and Environmental Science（先进机器人与环境科学研究中心）
This work explores the application of nonlinear oscillators coupled by electric field in water for collective tasks in underwater robotics. Such coupled oscillators operate in clear and colloidal (mud, bottom silt) water and represent a collective electrochemical sensor that is sensitive to global environmental parameters, geometry of common electric field and spatial dynamics of autonomous underwater vehicles (AUVs). Implemented in hardware and software, this approach can be used to create global awareness in the group of robots, which possess limited sensing and communication capabilities. U…
The design of autonomous underwater vehicles (AUVs) and their docking stations has been a popular research topic for several decades. Although many AUV and dock designs have been proposed, materialized, and commercialized, most of these existing designs prioritize the functionality of the AUV over the dock, or vise versa; there has been limited formal research in analytical optimization for AUV docking systems. In this paper, a multidisciplinary optimization framework is presented with the aim to fill this theoretical gap. We propose a co-design optimization method that optimizes multiple desi…
In this paper, we present a motion-based robotic communication framework that enables non-verbal communication among autonomous underwater vehicles (AUVs) and human divers. We design a gestural language for AUV-to-AUV communication which can be easily understood by divers observing the conversation unlike typical radio frequency, light, or audio based AUV communication. To allow AUVs to visually understand a gesture from another AUV, we propose a deep network (RRCommNet) which exploits a self-attention mechanism to learn to recognize each message by extracting maximally discriminative spatio-t…
Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks. Reinforcement learning (RL) and deep reinforcement learning have been introduced into the AUV design and research to improve its autonomy. However, these methods are still difficult to apply directly to the actual AUV system because of the sparse rewards and low learning efficiency. In this paper, we proposed a deep interactive reinforcement learning method for path following of AUV by combi…
A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data
College of Shipbuilding Engineering, Harbin Engineering University（哈尔滨工程大学船舶工程学院）；China Academy of Aerospace Aerodynamics（中国航天空气动力技术研究院）；Institute of Artificial Intelligence, Shanghai University（上海大学人工智能研究院）；China Ship Scientific Research Center（中国船舶科学研究中心）
Minnesota Robotics Institute（明尼苏达机器人研究所）；University of Minnesota Twin Cities（明尼苏达大学双城分校）
In this paper, we present the Diver Interest via Pointing (DIP) algorithm, a highly modular method for conveying a diver's area of interest to an autonomous underwater vehicle (AUV) using pointing gestures for underwater human-robot collaborative tasks. DIP uses a single monocular camera and exploits human body pose, even with complete dive gear, to extract underwater human pointing gesture poses and their directions. By extracting 2D scene geometry based on the human body pose and density of salient feature points along the direction of pointing, using a low-level feature detector, the DIP al…
Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking
College of Computer Science and Software Engineering, Hohai University（河海大学计算机科学与软件工程学院）；College of Information Science and Engineering, Hohai University（河海大学信息科学与工程学院）；Software College, Northeastern University（东北大学软件学院）
Institute of Sensors, Signals and Systems, Heriot-Watt University（赫瑞瓦特大学传感器、信号与系统研究所）；Department of Mechanical Engineering, Louisiana State University（路易斯安那州立大学机械工程系）
Autonomous Underwater Vehicles (AUVs) are becoming increasingly important for different types of industrial applications. The generally high cost of (AUVs) restricts the access to them and therefore advances in research and technological development. However, recent advances have led to lower cost commercially available Remotely Operated Vehicles (ROVs), which present a platform that can be enhanced to enable a high degree of autonomy, similar to that of a high-end (AUV). In this article, we present how a low-cost commercial-off-the-shelf (ROV) can be used as a foundation for developing versat…
In this work, we present the SeaShark AUV: a modular, easily configurable, one-man portable micro-AUV. The SeaShark AUV is conceived as modular parts that fit around a central main tube, which holds battery and other vital parts. The head unit comprises easy exhangeable, stackable, and 360 degree rotatable payload sections to quickly obtain a suitable configuration for many objectives. We employ navigation no better than dead reckoning or relative navigation with respect to some well-known structure, and thus aim at underwater activities that do not require highly accurate geo-referenced data-…
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…
Minnesota Robotics Institute（明尼苏达机器人研究所）；University of Minnesota–Twin Cities（明尼苏达大学双城分校）
This paper presents an evaluation of a number of probabilistic algorithms for localization of autonomous underwater vehicles (AUVs) using bathymetry data. The algorithms, based on the principles of the Bayes filter, work by fusing bathymetry information with depth and altitude data from an AUV. Four different Bayes filter-based algorithms are used to design the localization algorithms: the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and Marginalized Particle Filter (MPF). We evaluate the performance of these four Bayesian bathymetry-based AUV localization…
Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用技术大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Docking control of an autonomous underwater vehicle (AUV) is a task that is integral to achieving persistent long term autonomy. This work explores the application of state-of-the-art model-free deep reinforcement learning (DRL) approaches to the task of AUV docking in the continuous domain. We provide a detailed formulation of the reward function, utilized to successfully dock the AUV onto a fixed docking platform. A major contribution that distinguishes our work from the previous approaches is the usage of a physics simulator to define and simulate the underwater environment as well as the D…
Characterizing Wildlife Response to Biomimetic and Conventional Underwater Vehicles
University of Colorado at Boulder（科罗拉多大学博尔德分校）；Case Western Reserve University（凯斯西储大学）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Massachusetts Institute of Technology（麻省理工学院）
