“探索者”号无缆自治水下机器人的信息系统

介绍了“探索者”号无缆自治水下机器人信息系统及构成，它分为水上、水下两大部分。最后，详细介绍了水上信息系统的特点。

“探索者”号无缆水下机器人水下回收系统的设计与应用

本文详细介绍了"探索者"号无缆水下机器人水下回收系统的设计思想及电控系统的实现方法.本系统分为水上水下两部分.ROV式潜水平台型回收器,可在水下灵活运动,并可定向、定深.依靠电视跟踪技术和声纳定位技术实现回收器与水下机器人的对接.

“探索者”号自治式无缆水下机器人控制软件体系结构

本文详细介绍了我们为“探索者”号自治式无缆水下机器人（以下简称ＡＵＶ）设计的控制软件体系结构。该体系结构可分为使命控制，运动监控与设备操作，实时调度和实时处理四个不同的控制层次及一个全局数据区五部分。

关于海洋机器人去耦

本文给出一开启式海洋机器人流体动力学模型、去耦方案选择、系统结构设计和仿真,供同行交流。

滑动控制理论在海洋机器人动态定位系统中的应用

本文论述了一种新的鲁棒性控制方法——滑动控制理论.并将该方法应用于海洋机器人的动态定位系统设计.仿真结果表明该方法是相当成功和有效的,对系统的参数变化有强鲁棒性.

INVESTIGATION OF OCEAN ACOUSTICS USING AUTONOMOUS INSTRUMENTATION TO QUANTIFY THE WATER-SEDIMENT BOUNDARY PROPERTIES

Sound propagation in shallow water is characterized by interaction with the oceans surface, volume, and bottom. In many coastal margin regions, including the Eastern U.S. continental shelf and the coastal seas of China, the bottom is composed of a depositional sandy-silty top layer. Previous measurements of narrow and broadband sound transmission at frequencies from 100 Hz to 1 kHz in these regions are consistent with waveguide calculations based on depth and frequency dependent sound speed, attenuation and density profiles. Theoretical predictions for the frequency dependence of attenuation vary from quadratic for the porous media model of M.A. Biot to linear for various competing models. Results from experiments performed under known conditions with sandy bottoms, however, have agreed with attenuation proportional to f1.84, which is slightly less than the theoretical value of f2 [Zhou and Zhang, J. Acoust. Soc. Am. 117, 2494]. This dissertation presents a reexamination of the fundamental considerations in the Biot derivation and leads to a simplification of the theory that can be coupled with site-specific, depth dependent attenuation and sound speed profiles to explain the observed frequency dependence. Long-range sound transmission measurements in a known waveguide can be used to estimate the site-specific sediment attenuation properties, but the costs and time associated with such at-sea experiments using traditional measurement techniques can be prohibitive. Here a new measurement tool consisting of an autonomous underwater vehicle and a small, low noise, towed hydrophone array was developed and used to obtain accurate long-range sound transmission measurements efficiently and cost effectively. To demonstrate this capability and to determine the modal and intrinsic attenuation characteristics, experiments were conducted in a carefully surveyed area in Nantucket Sound. A best-fit comparison between measured results and calculated results, while varying attenuation parameters, revealed the estimated power law exponent to be 1.87 between 220.5 and 1228 Hz. These results demonstrate the utility of this new cost effective and accurate measurement system. The sound transmission results, when compared with calculations based on the modified Biot theory, are shown to explain the observed frequency dependence.

Estimating Oceanic Primary Production Using Vertical Irradiance and Chlorophyll Profiles from Ocean Gliders in the North Atlantic

An autonomous underwater vehicle (Seaglider) has been used to estimate marine primary production (PP) using a combination of irradiance and fluorescence vertical profiles. This method provides estimates for depth-resolved and temporally evolving PP on fine spatial scales in the absence of ship-based calibrations. We describe techniques to correct for known issues associated with long autonomous deployments such as sensor calibration drift and fluorescence quenching. Comparisons were made between the Seaglider, stable isotope (13C), and satellite estimates of PP. The Seaglider-based PP estimates were comparable to both satellite estimates and stable isotope measurements.

A New ROV Design: Issues on Low Drag and Mechanical Symmetry

This paper reports the design of a new remotely operated underwater vehicle (ROV), which has been developed at the Underwater Systems and Technology Laboratory (USTL) - University of Porto. This design is contextualized on the KOS project (Kits for underwater operations). The main issues addressed here concern directional drag minimization, symmetry, optimized thruster positioning, stability and layout of ROV components. This design is aimed at optimizing ROV performance for a set of different operational scenarios. This is achieved through modular configurations which are optimized for each different scenario.

Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Correction of shading effects in vision-based UUV localization

This paper describes the improvements achieved in our mosaicking system to assist unmanned underwater vehicle navigation. A major advance has been attained in the processing of images of the ocean floor when light absorption effects are evident. Due to the absorption of natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination for processing underwater images. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion. In this paper a technique to correct non-uniform lighting is proposed. The acquired frames are compensated through a point-by-point division of the image by an estimation of the illumination field. Then, the gray-levels of the obtained image remapped to enhance image contrast. Experiments with real images are presented

Towards a real-time vision-based navigation system for a small-class UUV

This paper deals with the problem of navigation for an unmanned underwater vehicle (UUV) through image mosaicking. It represents a first step towards a real-time vision-based navigation system for a small-class low-cost UUV. We propose a navigation system composed by: (i) an image mosaicking module which provides velocity estimates; and (ii) an extended Kalman filter based on the hydrodynamic equation of motion, previously identified for this particular UUV. The obtained system is able to estimate the position and velocity of the robot. Moreover, it is able to deal with visual occlusions that usually appear when the sea bottom does not have enough visual features to solve the correspondence problem in a certain area of the trajectory

Augmented state Kalman filtering for AUV navigation

Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach