Abstract—During the operation of and AUV in the ocean, it is necessary to monitor the vehicles position and the status in real time to prevent accidents that may occur abrupt changes in oceanic condition. Therefore a support ship should follow and monitor the AUV during survey. The ship operation costs are high and it is inefficient use if only one AUV dives during a survey. We developed a new survey method for multiple AUVs to make effective use of ship time. In our survey method, two cruising AUVs (AE2000a and AE2000f) pass through approximately the same route and observe the seafloor at a high altitude. One hovering type AUV (Tuna-Sand) navigates and takes pictures of seafloor at a low altitude. The ship follows the two cruising AUVs and sends the command by acoustic communication as necessary. Then an SSBL device on a moored buoy localizes Tuna-Sand and sends vehicle’s position and status
to the ship by satellite communications. To avoid sound wave interference, sufficient time gap is added between signal
transmissions and the GPS time is used for synchronization. The Tuna-Sand AUV observed on Smith caldera in Izu-Ogasawara ocean area in two dives using our survey method. In first dive, the vehicle surveyed for about 2 hours and took 170 pictures of the seafloor. In second dive, we succeeded that AE2000a, AE2000f and Tuna-Sand dived and surveyed on the caldera at same time. The results obtained during the survey are described in detail in this paper.
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
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.
Abstract: Deploying a multi-hop underwater acoustic sensor network (UASN) in a large area brings about new challenges in reliable data transmissions and survivability of network due to the limited underwater communication range/bandwidth and the limited energy of underwater sensor nodes. In order to address those challenges and achieve the objectives of maximization of data delivery ratio and minimization of energy consumption of underwater sensor nodes, this paper proposes a new underwater routing scheme, namely AURP (AUV-aided underwater routing protocol), which uses not only heterogeneous acoustic communication channels but also controlled mobility of multiple autonomous underwater vehicles (AUVs). In AURP, the total data transmissions are minimized by using AUVs as relay nodes, which collect sensed data from gateway nodes and then forward to the sink. Moreover, controlled mobility of AUVs makes it possible to apply a short-range high data rate underwater channel for transmissions of a large amount of data. To the best to our knowledge, this work is the first attempt to employ multiple AUVs as relay nodes in a multi-hop UASN to improve the network performance in terms of data delivery ratio and energy consumption. Simulations, which are incorporated with a realistic underwater acoustic communication channel model, are carried out to evaluate the performance of the proposed scheme, and the results indicate that a high delivery ratio and low energy
consumption can be achieved.
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.
Networks of autonomous robots will someday transform warfare, but significant hurdles remain
Why haven’t we seen a fully autonomous robot that can sense for itself, decide for itself, and seamlessly interact with people and other machines? Unmanned systems still fall short in three key areas: sensing, testing, and interoperability. Although the most advanced robots these days may gather data from an expansive array of cameras, microphones, and other sensors, they lack the ability to process all that information in real time and then intelligently act on the results. Likewise, testing poses a problem, because there is no accepted way to subject an autonomous system to every conceivable situation it might encounter in the real world. And interoperability becomes an issue when robots of different types must interact; even more difficult is getting manned and unmanned systems to interact.More
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 benets of the addition of a second AUV are explored and quantied. 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.More
As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for the use of underwater vehicle systems has become more apparent. Underwater vehicles represent a fast-growing research area and promising industry as advanced technologies in various subsystems develop and potential application areas are explored. Great efforts have been made in developing autonomous underwater vehicles (AUVs) to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, research and development activities in the AUV community have increased.
The Georgia Institute of Technology (GIT) is actively involved in three major research efforts: underwater vehicle sensing, underwater communications, and underwater vehicle autonomy including heterogeneous multi-vehicle collaboration. In order to test and experimentally validate the research, GIT has developed a new small man-portable Autonomous Underwater Vehicle called the Yellowfin. This new AUV provides a testbed for real world testing and experimentation of the advanced algorithm development. This paper will show the GIT development in this area.More
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, testbedMore
Groups of four UUVs have been used to validate a plume source localization algorithm and map the 3-D movement of a salinity front over time. These missions have demonstrated that not only are multi-vehicle deployments possible, but by using teams of cooperating vehicles, difficult tasks such as plume source localization can be performed quickly and that small volumes of the ocean can be simultaneously sampled with unprecedented spatial and temporal resolution.
The key to the success of these missions is the small, inexpensive and easy tooperate Ranger MicroUUV from Nekton Research. We report on the results of recent multi-agent missions and provide information about the UUVs used during these missions.More
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.More