Brian S. Bourgeois

UUV teams, control from a biological perspective

Remote Operated Vehicles (ROVs) are used extensively for salvage operations, ocean floor surveying and numerous inspection activities that support a wide range of underwater commercial activities. In deep water (greater than 1000 ft) an ROV is the platform of choice because of the depth and endurance limitations for human divers. The key disadvantage to an ROV is the requirement for the long tether. The tether greatly inhibits the speed of the ROV, requires a ship with deck gear capable of handling this cable, and significantly restricts ship movement while deployed. Un-tethered Unmanned Underwater Vehicles (UUVs) have entered the commercial market and have demonstrated the ability to perform deep-water surveys faster and cheaper than towed vessels. With further technological advances, UUVs have the potential for supplementing and even replacing ROVs for many deep-water operations because of the cost and problems associated with the tether. One promising scenario for the near future is to use an ROV or surface ship to control multiple UUVs in a local work area. Typically in this scenario the UUVs are used to extend the sensor footprint of the ROV or surface ship. Another area of interest is the UUV team concept. A stereotypical UUV team would be a heterogeneous mix of several low-cost specific purpose vehicles, guided and supported by one or two higher cost control vessels. Because of the severe restrictions that the sub-sea environment places on communication and positioning, precision underwater navigation is difficult. Currently most precision underwater navigation relies on some sort of infrastructure such as surface ships or underwater beacons placed in known positions. Using these assets as reference-points sub-sea navigation is carried out. Some situations require that the environmental and/or commercial attributes of an area be assessed before an infrastructure exists. In order to do this the UUV team must be able to navigate to an area, carry out its task and return without any pre-existing infrastructure or step by step guidance. Given basic assumptions about the type and frequency of sensor input we present a biologically inspired, decentralized methodology for safely and efficiently moving a loose formation of UUVs to and from the task area with the goal of minimizing outside guidance.

Autonomous bathymetry survey system

This paper describes the Autonomous Bathymetry Survey System (AutoSurvey), a system that provides automation of swath sonar bathymetric surveys. This system enables faster surveying of an area through environmentally adaptive techniques while ensuring adequate coverage and data quality. AutoSurvey assesses data quality and coverage in real time and generates next-trackline waypoints based on actual system performance. The need for real-time performance assessment is discussed. A primary factor considered is the effect of the environment on swath bathymetry system performance, which is difficult to predict a priori. The systems features, design, and implementation are discussed in this paper. Simulation and sea trial results are presented, as well as an analysis of the systems ability to reduce survey time.

Seafloor characterization using texture

Texture analysis is performed on multibeam sonar imagery. A set of 14 texture features is computed using cooccurrence matrices to form the feature space. The dimensionality of the feature space is reduced by extracting the principal components from the original feature space. Classification of the image is performed on the principal components using the K-means algorithm. Results indicate that seafloor bottom types can be characterized by analyzing the texture of bathymetric sonar images.<<ETX>>

Sidescan Sonar Image Interpretation With Neural Networks

This paper investigates the use of neural networks for the direct estimation of image texture. Unlike previous approaches where networks are used to make decisions on feature vectors derived from traditional techniques, or where a network is trained to perform the function of a traditional technique, the proposed approach will use a network to directly model texture. The envisioned approaches to this method are described and the results of the preliminary l-dimensional tests are presented.

Fuzzy Multiple Criteria Base Realignment and Closure (BRAC) Benchmarking System at the Department of Defense

Purpose – The US Government adopted the base realignment and closure (BRAC) to resolve the military, economic and political issue of excess base capacity. There have been five rounds of BRAC since 1988, and more are expected to come in the years ahead. The complexity of the closure and realignment decisions and the plethora of factors that are often involved necessitate the need for a sound theoretical framework to structure and model the decision‐making process. This paper aims to address the issues.Design/methodology/approach – The paper presents a multiple criteria benchmarking system that integrates the employment, environmental, financial, strategic, and tactical impacts of the closure and realignment decisions into a weighted‐sum measure called the “survivability index.” The proposed index is used to determine whether the returns generated by each military base on the Department of Defense (DoD) hit list meet a sufficient target benchmark.Findings – There is a significant amount of evidence that int...

AUV positioning using bathymetry matching

A research concern in AUV positioning is the constraint of INS error growth; approaches to this include surfacing for GPS fixes, terrain matching methods and acoustic transponder systems. The paper presents a positioning technique for AUVs that exploits existing bathymetric data in an operation area. Unlike many terrain matching approaches, which do positioning using distinct ocean bottom features, this method generates a position estimate by comparing the in-situ measured depth at the position of the AUV with available bathymetry data in the immediate area. This builds on contemporary AUV INS/VL navigation systems by incorporating a maximum likelihood estimate of position. Particular emphasis is placed on the design of the maximum likelihood estimator module which produces point-wise position estimates and typically contains a large error component with many outliers. This estimate is merged with the output of the AUVs INS/VL system which constrains the INS drift. Further position accuracy and faster convergence to the correct position can be achieved by incorporating a single slant range measurement from the AUV to a fixed location. The slant range is used as external constraint on both the INS and the MLE. The paper describes the implementation of this approach and the results of simulation studies.

Formation maneuvering using passive acoustic communications

Interest in the use of unmanned underwater vehicles (UUVs) for both commercial and military uses is growing. Control of UUVs poses a difficult problem because traditional methods of communication and navigation, i.e. radio and GPS, are not effective due to the properties of seawater. Control and communication algorithms were developed to carry out multiple UUV formation maneuvering using acoustic communications and first tested in computer simulation and then on mobile robots. Three control schemes, classic logic, behavior, and neural network were tested in line formations in both simulator and lab environments. Results and issues are discussed along with future directions.

Relative positioning for team robot navigation

The research presented in this paper approaches the issue of robot team navigation using relative positioning. With this approach each robot is equipped with sensors that allow it to independently estimate the relative direction of an assigned leader. Acoustic sensor systems are used and were seen to work very effectively in environments where datum relative positioning systems (such as GPS or acoustic transponders) are typically ineffective. While acoustic sensors provide distinct advantages, the variability of the acoustic environment presents significant control challenges. To address this challenge, directional control of the robot was accomplished with a feed forward neural network trained using a genetic algorithm, and a new approach to training using recent memories was successfully implemented. The design of this controller is presented and its performance is compared with more traditional classic logic and behavior controllers.

ORCA's oceanographic sensor suite

The Mapping, Charting and Geodesy Branch of the Naval Research Laboratory (NRL) at Stennis Space Center, MS, is conducting a multi-year program for the development of unmanned, untethered sensor systems for the collection of tactical oceanographic data in littoral regions. This paper reviews the sensor systems, program progress to date and the future plans for a comprehensive oceanographic survey system. The prototype platform currently in use for this project is the ORCA semi-submersible. The ORCA is an air-breathing vessel which travels just below the water surface. The vessel utilizes a direct radio link for real-time data and control communications, as well as a DGPS system for precise platform positioning. The primary sensor installed on ORCA is the Simrad EM950 system which collects bathymetry and collocated acoustic imagery in water up to 300 meters in depth. With realtime data telemetry to the ORCA host vessel and the NRL developed HMPS bathymetry post-processing software, the system is capable of same-day chart production. In contrast to a full size survey vessel, ORCA is able to collect bathymetric data of the same quantity and quality, but will have one fortieth the life-cycle costs. Other sensors being integrated into ORCA include an acoustic sediment classification system, an acoustic Doppler current profiler, and obstacle avoidance systems.

An efficient multi-vehicle multi-criteria mission planning and control system for autonomous underwater vehicles

In their study, Tavana and Bourgeois (2010) proposed a multi-criteria decision analysis model that considered dynamic and episodic phenomenon on the surface of the ocean and provided specific navigation plans. They showed that the transect for an autonomous underwater vehicle (AUV) includes not only the desired horizontal path, but also the depth range that the vehicle will operate in. They argued that the current models should be extended to include considerations that change vertically, that is, with different ocean depths. They also suggested expansion of the current models to cooperative teams of AUVs and showed that working together will allow underwater vehicles to complete tasks that could not be completed by a single vehicle. This study extends their model by: reducing the number of judgements required to generate the navigation scores within the decision region; considering ocean depth and finding the navigation scores for the ocean surface and interior; and developing a model which derives an optimal allocation of multiple vehicles to various locations within the decision region. The proposed framework is an efficient multi-vehicle multi-criteria mission planning and control system that considers ocean phenomenon on the surface and in the interior and provides an optimal allocation of vehicles with respect to the stated objective and subjective mission goals.