Docking at pool and sea by using active marker in turbid and day/night environment

Nowadays, autonomous underwater vehicle (AUV) is playing an important role in human society in different applications such as inspection of underwater structures (dams, bridges). It has been desired to develop AUVs that can work in a sea with a long period of time for the purpose of retrieving methane hydrate, or rare metal, and so on. To achieve such AUVs, the automatic recharging capability of AUVs under the sea is indispensable and it requires AUVs to dock itself to recharging station autonomously. Therefore, we have developed a stereo-vision-based docking methodology for underwater battery recharging to enable the AUV to continue operations without returning surface vehicle for recharging. Since underwater battery recharging units are supposed to be installed in a deep sea, the deep-sea docking experiments cannot avoid turbidity and low-light environment. In this study, the proposed system with a newly designed active—meaning self-lighting—3D marker has been developed to improve the visibility of the marker from an underwater vehicle, especially in turbid water. Experiments to verify the robustness of the proposed docking approach have been conducted in a simulated pool where the lighting conditions change from day to night. Furthermore, sea docking experiment has also been executed to verify the practicality of the active marker. The experimental results have confirmed the effectiveness of the proposed docking system against turbidity and illumination variation.     

基于多AUV间任务协作的水下多目标探测机制

利用自主式水下航行器（Autonomous Underwater Vehicle, AUV）对水下多目标进行协同探测是目前海洋技术领域的研究热点。本文主要研究在水下三维区间内的多AUV任务分配与协作探测机制,建立了以每个AUV能量耗费与能耗均衡为约束条件的水下三维空间中的多旅行商（Multiple Traveling Salesman Problem, MTSP）问题模型,利用遗传算法（Genetic Algorithm, GA）对该NP-Complete问题进行启发式求解,同时设计了考虑巡航总路径及访问目标数的适应度函数以提高多AUV间的能耗均衡性,实现多个AUV对多个水下目标的优化协同探测。最后本文利用Matlab R2014a软件对多AUV任务协作与多目标探测机制进行了仿真,仿真结果验证了该方法能够均衡多AUV多目标探测问题的能量消耗,进而提高巡航速度和生命周期。     

Underwater guidance of distributed autonomous underwater vehicles using one leader

Consider the case where autonomous underwater vehicles (AUVs) are deployed to monitor a 3D underwater environment. This paper tackles the problem of guiding all AUVs to the destination while not colliding with a priori unknown 3D obstacles. Suppose that among all AUVs, only the leader AUV has an ability of locating itself, while accessing a destination location. A follower, an AUV that is not a leader, has no sensors for locating itself. Every follower can only measure the relative position of its neighbor AUVs utilizing its sonar sensors. Our paper addresses distributed controls, so that multiple followers track the leader while preserving communication connectivity. We design controls, so that all AUVs reach the destination safely, while maintaining connectivity in cluttered 3D environments. To the best of our knowledge, our article is novel in developing 3D underwater guidance controls, so that all AUVs equipped with sonar sensors are guided to reach a destination in a priori unknown cluttered environments. MATLAB simulations are used to validate the proposed guidance scheme in underwater environments with many obstacles.     

基于Agent面向群体合作的AUV体系结构

分析了AUV体系结构的研究现状及尚未解决的主动性、社会性等问题，提出了一种基于Agent面向群体合作的AUV体系结构(ATA-AUV). ATA-AUV具有主动性、自治性、反应性、社会性等良好特性，适合于复杂海洋环境下AUV群体合作应用领域．提出了4条AUV体系结构功能评价准则，对ATA-AUV及已有的典型的AUV体系结构进行了对比评价分析．给出了ATA-AUV仿真实验结果，证明了ATA-AUV的可行性和有效性．     

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter’s measurement model driven by the AUV’s altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.     

An Acoustic Network Navigation System

This work describes a system for acoustic‐based navigation that relies on the addition of localization services to underwater networks. The localization capability has been added on top of an existing network, without imposing constraints on its structure/operation. The approach is based on the inclusion of timing information within acoustic messages through which it is possible to know the time of an acoustic transmission in relation to its reception. Exploiting such information at the network application level makes it possible to create an interrogation scheme similar to that of a long baseline. The advantage is that the nodes/autonomous underwater vehicles (AUVs) themselves become the transponders of a network baseline, and hence there is no need for dedicated instrumentation. The paper reports at sea results obtained from the COLLAB–NGAS14 experimental campaign. During the sea trial, the approach was implemented within an operational network in different configurations to support the navigation of the two Centre for Maritime Research and Experimentation Ocean Explorer (CMRE OEX) vehicles. The obtained results demonstrate that it is possible to support AUV navigation without constraining the network design and with a minimum communication overhead. Alternative solutions (e.g., synchronized clocks or two‐way‐travel‐time interrogations) might provide higher precision or accuracy, but they come at the cost of impacting on the network design and/or on the interrogation strategies. Results are discussed, and the performance achieved at sea demonstrates the viability to use the system in real, large‐scale operations involving multiple AUVs. These results represent a step toward location‐aware underwater networks that are able to provide node localization as a service.     

基于CADCON的群体AUV三维仿真

近年的AUV仿真工作更多地集中于对AUV水动力学特性仿真数学模型的建立上，不能满足AUV群体协作策略级仿真的需要。CADCON是AUSI基于Internet架构开发的群体协作AUV三维水下环境分布式仿真平台，提供了可定制的虚拟海洋环境和一系列可灵活配置的AUV模型以及AUV群体行为组件开发接口。该文介绍了CADCON的功能和体系结构，归纳了基于CADCON平台进行AUV群体协作仿真的基本流程和参数配置方法，给出了Visual C＋＋环境下的AUV群体行为组件开发的基本方法，该方法的可行性已得到实验验证。     

自治式水下机器人回收系统的研究与设计

本文介绍了我们承担研制的国家“８６３”计划１０００ｍ及６０００ｍ无缆水下机器人的回收系统，回收系统在４级海况下不用专用母船能够成功地回收水下机器人，依据母船、海况水下机器人及其他具体情况，介绍了两种不同的回收方案和回收器，经海上试验证明是有效和可行的。     

Design and Control of Autonomous Underwater Robots: A Survey

During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.     

Policy learning for autonomous feature tracking

We consider the problem of tracing the structure of oceanological features using autonomous underwater vehicles (AUVs). Solving this problem requires the construction of a control strategy that will determine the actions for the AUV based on the current state, as measured by on-board sensors and the historic trajectory (including sensed data) of the AUV. We approach this task by applying plan-based policy-learning, in which a large set of sampled problems are solved using planning and then, from the resulting plans a decision-tree is learned, using an established machine-learning algorithm, which forms the resulting policy. We evaluate our approach in simulation and report on sea trials of a prototype of a learned policy. We indicate some of the lessons learned from this deployed system and further evaluate an extended policy in simulation.     

A comparative study of control techniques for an underwater flight vehicle

Unmanned, underwater vehicles have been developed considerably in recent years. Remotely operated vehicles (ROVs) are increasingly used for routine inspection and maintenance tasks but have a range that is limited by the umbilical cable. For long range operations, such as oceanographic exploration and surveying, autonomous underwater vehicles (AUVs) are emerging which haveon-board power and are equipped with advanced control capabilities to carry out tasks with the minimum of human intervention. AUVs typically resemble torpedoes in that mosthave control surfaces and a single propulsion unit, and must move forwards to manoeuvre. Such vehicles are called flight vehicles. This paper describes techniques which are candidates for control of a flight AUV and identifies controllers used on some existing vehicles. Since underwater vehicle dynamics are nonlinear, fuzzy logic and sliding mode control were felt to have promise for autopilot application due to their potential robustness. Following development using a comprehensive simulation programme, the controllers were tested using the experimental vehicle, Subzero II, and their performance compared with that of a classical linear controller. The relative merits of the methods for practical implementation are discussed.     

Synchronization of multiple autonomous underwater vehicles without velocity measurements

In this paper,we investigate the synchronization control of multiple autonomous underwater vehicles (AUVs),considering both state feedback and output feedback cases.Treating multiple AUVs as a graph,we define the tracking error of each AUV with both its own tracking error and the relative position errors with respect to its neighbors taken into account.Lyapunov analysis is used to derive the control law for each AUV.For the output feedback case,a passive filter is used to compensate for the unknown relative velocity errors among AUVs,and an observer is employed to estimate the velocity of the AUV itself.Rigid mathematical proof is provided for the proposed algorithms for both state feedback and output feedback cases.Simulations are provided to demonstrate the effectiveness of the proposed approach.It is shown that,the synchronization error is smaller in the case of considering the relative errors between AUVs than in the case of considering the tracking error of the single AUV only.     

AUV水下着陆策略研究

水下自航行器（AUV）的续航能力主要取决于其所携带能源总量．为了有效地减少能耗,提出了 一种具有变浮力系统、能够着陆坐底的小型AUV,它可以利用有限的能源实现长时间的海洋环境监测．首先 介绍了该AUV 的总体结构,然后对AUV 的着陆策略进行了详细研究．在对三种着陆策略进行对比的基础上, 选择下潜航行到位控制注水着陆策略作为最佳的水下着陆方案．该策略能够使着陆时的冲击力保持在安全范 围内,并且着陆时间最短．最后对着陆轨迹进行规划．仿真和水域实验的结果都证明采用该着陆策略能够安 全、平稳地实现水下着陆．     

Minimum Speed Seeking Control for Nonhovering Autonomous Underwater Vehicles

Most autonomous underwater vehicles (AUVs) are propelled by a single thruster, use elevators and rudders as control surfaces, and are torpedo‐shaped. Furthermore, they are positively buoyant to facilitate recovery during an emergency. For this class of nonhovering AUVs, there is a minimum speed at which the AUV must travel for stable depth control. Otherwise, the extra buoyancy will bring the AUV up to the surface when the fin loses its effectiveness at low speeds. Hence, we develop a novel algorithm such that the AUV is automatically controlled to travel at its minimum speed while maintaining a constant depth. This capability is important in a number of practical scenarios, including underwater loitering with minimum energy consumption, underwater docking with minimum impact, and high‐resolution sensing at minimum speed. First, we construct a depth dynamic model to explain the mechanism of the minimum speed, and we show its relationship with the buoyancy, the righting moment, and the fin's effectiveness of the AUV. Next, we discuss the minimum speed seeking problem under the framework of extremum seeking. We extend the framework by introducing a new definition of steady‐state mapping that imposes new structure on the seeking algorithm. The proposed algorithm employs a fuzzy inference system, which is driven by the real‐time measurements of pitch error and elevator deflection. The effectiveness of the algorithm in seeking the minimum speed is validated in both simulations and field experiments.     

Moth-inspired chemical plume tracing on an autonomous underwater vehicle

This paper presents a behavior-based adaptive mission planner (AMP)to trace a chemical plume to its source and reliably declare the source location. The proposed AMP is implemented on a REMUS autonomous underwater vehicle (AUV)equipped with multiple types of sensors that measure chemical concentration,the flow velocity vector, and AUV position, depth, altitude, attitude, and speed. This paper describes the methods and results from experiments conducted in November 2002 on San Clemente Island, CA, using a plume of Rhodamine dye developed in a turbulent fluid flow (i.e., near-shore ocean conditions). These experiments demonstrated chemical plume tracing over 100 m and source declaration accuracy relative to the nominal source location on the order of tens of meters. The designed maneuvers are divided into four behavior types: finding a plume,tracing the plume, reacquiring the plume, and declaring the source location. The tracing and reacquiring behaviors are inspired by male moths flying up wind along a pheromone plume to locate a sexually receptive female. All behaviors are formulated by perception and action modules and translated into chemical plume-tracing algorithms suitable for implementation on a REMUS AUV. To coordinate the different behaviors, the subsumption architecture is adopted to define and arbitrate the behavior priorities. AUVs capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants.     

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise-time, overshoot, steady-state error, and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.     

一种水下机器人集成仿真系统的设计

在水下机器人的设计和开发过程中有一个严重的限制问题,就是由于水下机器人工作在较深的水中,因此很难观察到水下机器人的工作情况.针对实验室开发的新型水下机器人,该文的集成仿真系统提供了一个虚拟的海洋环境,和一些传感器的仿真,能够较为真实地模拟水下机器人的工作过程.它允许研究人员在实验室里方便地进行测试,并通过三维实时的图形场景对机器人的工作过程进行监控.最终通过仿真试验,完善了机器人控制系统的设计.     

Robust underwater obstacle detection and collision avoidance

A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.     

Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment

There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.