Despite advances in underwater technology, human divers are still employed to carry out a wide variety of dangerous underwater tasks. To enable a diver to complete tasks safer and more efficiently, it is proposed that an Underwater Robotic Assistant (UWRA) could act as a robotic dive buddy. For example, the UWRA could ferry tools from the surface, carry scientific samples, and provide illumination in dark environments. However, one of the major hurdles to operating autonomous systems below the water's surface is that high-frequency radio signals are greatly attenuated by water. This severely limits the type of commands a diver can issue to the robot. Thus, one of the contributions of this paper is an analysis of the types of Underwater Human-Robot Communication (UHRC) that are possible between a diver and a UWRA. Following the analysis, we discuss implementation of a few of the proposed methods of UHRC where an underwater robot assists a diver during the installation of an underwater mechanical platform.

Underwater environments hidden beneath a layer of ice prove challenging to explore for both humans and unmanned underwater vehicles. Exploration of such areas is a topic of interest to many scientific communities. In this paper, we discuss details on the approach taken to obtain sonar and video data of under ice topography in a Colorado mountain lake using a VideoRay Pro IV unmanned underwater vehicle. Video from the vehicle along with acoustic sonar data from a BlueView forward looking sonar sensor were recorded concurrently for use in future research. The approach taken, challenges encountered, results obtained, and lessons learned during the data collection process are described herein.

Navigation through underwater environments is challenging given the lack of accurate positioning systems. The determination of underwater vehicle movement using an integrated acoustic sonar sensor would provide underwater vehicles with greatly increased autonomous navigation capabilities. A forward looking sonar sensor may be used for determining autonomous vehicle movement using filtering and optical flow algorithms. Optical flow algorithms have shown excellent results for vision image processing. However, they have been found difficult to implement using sonar data due to the high level of noise present, as well as the widely varying appearances of objects from frame to frame. For the bottom tracking applications considered, the simplifying assumption can be made that all features move with an equivalent direction and magnitude between frames. Statistical analysis of all estimated feature movements provides an accurate estimate of the overall shift, which translates directly to the vehicle movement. Results using acoustic sonar data are presented which illustrate the effectiveness of this methodology.

An underwater robotic assistant could help a human diver by illuminating work areas, fetching tools from the surface, or monitoring the diver's activity for abnormal behavior. However, in order for basic Underwater Human-Robot Interaction (UHRI) to be successful, the robotic assistant has to first be able to detect and track the diver. This paper discusses the detection and tracking of a diver with a high-frequency forward-looking sonar. The first step in the diver detection involves utilizing classical 2D image processing techniques to segment moving objects in the sonar image. The moving objects are then passed through a blob detection algorithm, and then the blob clusters are processed by the cluster classification process. Cluster classification is accomplished by matching observed cluster trajectories with trained Hidden Markov Models (HMM), which results in a cluster being classified as either a diver or clutter. Real-world results show that a moving diver can be autonomously distinguished from stationary objects in a noisy sonar image and tracked.

This paper presents a robust extended Kalman filter (REKF) applied to the simultaneous localization and mapping (SLAM) problem. Conventional Kalman filter methods suffer from the assumption of Gaussian noise statistics, which often lead to failures when these assumptions do not hold. Additionally, the linearization errors associated with the implementation of the standard EKF can also severely degrade the performance of the localization estimate. Currently, stochastic mapping provides a framework for the concurrent mapping of landmarks and localization of the vehicle with respect to the landmarks. However, the stochastic map is essentially an augmented EKF with the limitations thereof. This research addresses the linearization and Guassian assumption errors as they relate to the SLAM problem by proposing a new method, robust stochastic mapping. The robust stochastic map uses a Robust EKF (REKF) in order to address these limitations through the implementation of the bounded Hinfin norm. Experimental data are presented to illustrate the advantage of the localization using the proposed estimation procedure

This paper presents a six degree-of-freedom nonlinear H-/sub /spl infin// controller for autonomous underwater vehicles. The controller makes use of Euler parameters, or unit quaternion, which have been used extensively with spacecraft and aircraft as opposed to the more popular Euler angle representation. Euler angles suffer as with other 3-parameter representations from singular points. One of the main challenges associated with designing an underwater control is the unknowns of the model. The nonlinear H-/sub /spl infin// controller attempts to address this problem such that the closed loop system has L/sub 2/ - gain /spl les/ /spl gamma/ from torque disturbances to a penalty variable for a fixed positive constant /spl gamma/.

The control of autonomous underwater robotic vehicles includes many challenges. The highly nonlinear, time-varying dynamic behavior of the robot continually changes the parameters of the system model. The uncertainties in hydrodynamic coefficient make system identification difficult. Disturbances acting on the robot, such as ocean currents, continually affect the system. The paper presents a non-regressor based adaptive control scheme for use on an underwater robotic vehicle. The control scheme is non-model based and, thus, does not require any knowledge of the system. The adaptive control algorithm estimates the control gains defined by the combinations of the bounded constants of system parameter matrices. The performance of the algorithm was evaluated through simulation. Additionally, experimental data using the ODIN vehicle was analyzed.