In-situ observation of the deep-sea floor is a fundamental need for marine sciences and ecosystem monitoring. This work proposes a novel robotic approach for benthic observations in the deep sea using underwater gliders. The glider is equipped with a downward looking camera system to acquire high resolution optical images of the seafloor. The system works fully autonomous and has the potential for long-endurance missions at low deployment costs. Key factors for long battery lifetime are a low power consumption during idle phases, a timely activation of the main imaging system and an efficient lighting setup. The results of our initial trials at sea using a 1000m glider show the applicability of the approach for marine science applications. We achieved optical image quality utilizable for seafloor classification and 3D reconstruction of underwater objects. Keeping the vertical zig-zag motion of underwater gliders in mind, our findings substantiate the feasibility of multi-week seafloor observation missions at long operating ranges.

An adaptive sensing method is presented to control the pinging interval of a downward-looking sonar on an Autonomous Underwater Vehicle. The goal is to conserve energy via adjusting the pinging rate automatically without reducing the localization accuracy when using terrain-aided navigation (TAN). In this paper, the TAN is implemented using a particle filter and a bias velocity estimator developed based on a Kalman filter. The adaptation on the sonar pinging interval is determined based on the depth variation of local seafloor topography which is quantified using a modified Teager Kaiser energy operator. As a result, more measurements are collected on high relief regions, and less measurements are obtained on relatively flat and smooth regions. We evaluated the adaptive sensing method in a simulated environment and applied it to a field data set. The results show that the adaptive sensing method produces an improved navigational accuracy compared to the missions with fixed sonar pinging rates. In the offline field missions, the energy consumed by the altimeter is reduced to about 30% in the adaptive sensing missions compared to continuously sensing missions where the altimeter is pinging consistently without switching off.

This paper introduces two well-accepted path-following control methods, namely Vector Field Method (VF) and Line-Of-Sight Method (LOS) for Unmanned Surface Vehicles (USVs). We provide a comprehensive study of each algorithm, which includes investigating their mathematical origins, performing simulation evaluations and carrying out real-world field tests in adverse weather conditions. We compare different characteristics of the two methods, and the successful field trial results demonstrate their accuracy and robustness considering unexpected environmental influences. The presented work can assist USV practitioners to decide on a proper strategy for completion of a specific USV mission.

It is important to register oceanographic data into a geo-referenced coordinate system. Knowing the location of the sampling is critical. For a marine robotic network, the location of Unmanned Surface Vessels (USVs) can be measured using a Global Positioning System (GPS), however the navigation of Autonomous Underwater Vehicles (AUVs) is more challenging. In this paper, we present a method for determining the position of underwater vehicles from a moving USV using the relative range information provided by an Ultra-Short Baseline (USBL)/acoustic modem. The navigation method uses an Extended Kalman Filter (EKF) to update the states predicted from a model-based dead-reckoning technique. Since the vehicle model is relative to the surrounding fluid, we have introduced two environmental states in the state matrix. Such a modification allows us to quantify the effects induced by the ocean current on the vehicle's speed. Beyond that, the method uses a limited number of sensors, only attitude sensors and an USBL/acoustic modem, offering an alternative for AUVs without expensive instruments such as a Doppler Velocity Log (DVL) and an Inertial Measurement Unit (IMU). Experiments are conducted to evaluate the range-based navigation method on a hybrid Slocum underwater glider with an USV. As a result from the reference trial, the estimated glider position stays within the error of 15 meters comparing to the measured position of a surface buoy where the glider is attached. In the open-water trial, trajectories estimated from the range-based navigation are compared with dead-reckoning paths and current-compensated dead-reckoning paths (reference). As a result, the distance errors are bounded with the proposed navigation method while the dead-reckoning errors grow without bound.

The finite-dimensional nonlinear thruster models (Bachmayer et al. (2000)) employing empirically determined lift/drag curves have been shown to accurately model both the transient and steady state response of marine thrusters. This paper first reports on the development of an asymptotically stable open-loop thrust tracking controller that exploits this new nonlinear thruster model. To the best of our knowledge, this is the first stable model based thrust control law reported for this class of marine thrusters.

This paper investigates the benefits of applying simple PID+nonlinear controller to subsea robot. This control structure has the advantage of only adding a supplementary nonlinear feedback loop to the existing linear PID-regulator and at the same time improving both the transient and stability margins. Several simulations and experiments have been performed on the experimental subsea robot (VORTEX) and have demonstrated the improvements of the PID+nonlinear controller over the simple PID.>