AUVAC

TRITON — Multisensory Based Underwater Intervention through Cooperative Marine Robots

The TRITON research project pursues the use of autonomous vehicles in the accomplishment of complex underwater intervention tasks. The project will emphasize the operation of multiple vehicles (an AUV and an I-AUV) cooperating in a coordinate manner during the execution of a mission, as well as in increasing the dexterity of a robotic arm (currently under development in the context of the RAUVI project), that will be installed in the I-AUV.

National project, 2012–2014, in progress

Global Objectives

The TRITON project proposes two scenarios as a proof of concept to demonstrate the developed capabilities: (1) the search and recovery of an object of interest (e.g. a black box) and (2) the intervention of an underwater panel in a permanent observatory.

The first mission scenario will be divided in several sub-tasks. First, the mission will begin with the deployment of the AUV and the intervention I-AUV, which should then adopt and maintain a safe formation that enables acoustic communication and absolute positioning of both vehicles. Then, a sonar survey will be carried out by the AUV to detect the signal emitted by a pinger in the black box. The detection of such signal will be followed by a second survey, whose objective is to create a photomosaic of the area, making possible to visually identify the object. Finally, the AUV will be sent back to perform the intervention task to recover the object.

The second mission scenario will also begin with the deployment and formation of both marine robots in order to establish communications and absolute positioning. Then, the AUV will use acoustics to interrogate a transponder mounted in the observatory with the objective to guide the vehicle transit to the panel. When visual contact with the objective is established, the AUV will approach the panel using visual servoing. The final part of the docking operation will involve a mechanism to rigidly attach the vehicle to the panel. After this, the manipulation will take place. Two demonstrative applications are foreseen: Opening/closing a valve, and connecting/disconnecting a cable.

Constructing a Distributed AUV Network for Underwater Plume-Tracking Operations

Abstract—In recent years, there has been significant concern about the impacts of offshore oil spill plumes and harmful algal blooms on the coastal ocean environment and biology, as well as on the human populations adjacent to these coastal regions. Thus, it has become increasingly important to determine the 3-D extent of these ocean features (‘plumes’) and how they evolve over time. The ocean environment is largely inaccessible to sensing directly by humans, motivating the need for robots to intelligently sense the ocean for us. In this paper, we propose the use of an autonomous underwater vehicle (AUV) network to track and predict plume shape and motion, discussing solutions
to the challenges of spatiotemporal data aliasing (coverage vs. resolution), underwater communication, AUV autonomy, data fusion, and coordination of multiple AUVs. A plume simulation is also developed here as the first step toward implementing behaviors for autonomous, adaptive plume tracking with AUVs, modeling a plume as a sum of Fourier orders and examining the resulting errors. This is then extended to include plume forecasting based on time variations, and future improvements and implementation are discussed.

Exploring Trade-offs in AUV Controller Design for Shark Tracking

This thesis explores the use of an Autonomous Underwater Vehicle (AUV) to track and pursue a tagged shark through the water. A controller was designed to take bearing and range to the shark tag and then control the AUV to pursue it.

First, the ability of a particle lter to provide an accurate estimation of the location of the shark relative to the AUV is explored. Second, the ability of the AUV to follow the shark0s path through the water is shown. This ability allows for localized environmental sampling of the shark0s preferred path. Third, various path weightings are used to optimize the efficiency of pursuing the shark. This demonstrates that the proposed controller is efficient and effective. Fourth, the benets of the addition of a second AUV are explored and quantied. The secondary AUV is shown to maintain formation without direct communication from the primary AUV. However, the communication of the AUVs increases the accuracy of all measurements and allows for future expansion in the complexity of the controller. Fifth, the eects of predicting the shark’s future movement is explored. Sixth, the eect of noise in the signal from the shark tag is tested and the level of noise at which the AUV can no longer pursue the shark is shown. This investigates the real world ability of the controller to accept noisy inputs and still generate the appropriate response. Finally, the positive results of the previous sections are combined and tested for various noise levels to show the improved controller response even under increased noise levels.

To validate the proposed estimator and controller, seven tests were conducted. All tests were conducted on existing shark path data recorded by a stationary acoustic receiver and a boat mounted acoustic receiver. Tests were conducted on data sets from two dierent species of sharks, (Shovelnose and White) with two very dierent swimming behaviors. This shows the solution”s fexibility in the species of shark tracked.

An Overview of Autonomous Underwater Vehicle Research and Testbed at PeRL

A B S T R A C T
This article provides a general overview of the autonomous underwater vehicle (AUV) research thrusts being pursued within the Perceptual Robotics Laboratory (PeRL) at the University of Michigan. Founded in 2007, PeRL’s research centers on improving AUV autonomy via algorithmic advancements in environmentally based perceptual feedback for real-time mapping, navigation, and control.

Our three major research areas are (1) real-time visual simultaneous localization and mapping (SLAM), (2) cooperative multi-vehicle navigation, and (3) perceptiondriven control. Pursuant to these research objectives, PeRL has developed a new multi-AUV SLAM testbed based upon a modified Ocean-Server Iver2 AUV platform.

PeRL upgraded the vehicles with additional navigation and perceptual sensors for underwater SLAM research. In this article, we detail our testbed development, provide an overview of our major research thrusts, and put into context how our modified AUV testbed enables experimental real-world validation of these algorithms.

Keywords: AUVs, SLAM, navigation, mapping, testbed

Cooperative Mapping and Navigation for Multiple Unmanned Underwater Vehicles

Abstract

This Paper considers the problem of cooperative mapping and navigation (CMAN) by multiple unmanned underwater vehicles (UUVs). The goal is for several UUVs to concurrently build maps of an unknown environment, and to use these maps for navigation. This work builds on our previous research in development of concurrent mapping and localization (CML) techniques for a single vehicle. In this paper, cooperative stochastic mapping is proposed as a new framework for featurebased CML by multiple vehicles. Previous research related to cooperative mapping and navigation is reviewed. New research issues encountered, such as information transfer management, decentralized data fusion, and cooperative adaptive sampling are discussed.