Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (GP)-based expectation-maximisation (EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents.
To estimate ocean currents online, one approach is to consider current as a low-frequency disturbance and then apply an extended Kalman filter (EKF) @cite_7 or nonlinear observer @cite_30 in conjunction with acoustic sensors. However, modelling current as a temporal phenomenon clearly overlooks its spatial structure, and acoustic sensors typically require a stationary reference (e.g., the seabed) @cite_13 .
@cite_30 @cite_13 @cite_7
2438306175 1969142081 2267281579
As applications for autonomous ocean vehicles expand into more dynamic and constrained environments, such as shallow, coastal areas, the benefits of using more precise dynamic model for control and estimation become more compelling. This paper presents a nonlinear observer for current estimation based on AUV dynamic model. Here, AUV dynamic model in currents is taken into consideration. Motivated by the design method of high-gain observer, we take the current disturbances as the uncertainties of the vehicle dynamic system and design the observer gain matrix with the goal of making the observer robust to the effect of current disturbances. The nonlinear observer estimates vehicle's relative velocity firstly; current velocity is further calculated in an indirect way. The proposed current estimation method is validated by numerical simulation. Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential. Survey-class autonomous underwater vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid-water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.