Abstract
Effective study of ocean processes requires sampling over the duration of long (weeks to months) oscillation patterns. Such sampling requires persistent, autonomous underwater vehicles, that have a similarly long deployment duration. The spatiotemporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. In this paper, we consider the combination of two methods for reducing navigation and localization error; a predictive approach based on ocean model predictions and a prior information approach derived from terrain-based navigation. The motivation for this work is not only for real-time state estimation, but also for accurately reconstructing the actual path that the vehicle traversed to contextualize the gathered data, with respect to the science question at hand. We present an application for the practical use of priors and predictions for large-scale ocean sampling. This combined approach builds upon previous works by the authors, and accurately localizes the traversed path of an underwater glider over long-duration, ocean deployments. The proposed method takes advantage of the reliable, short-term predictions of an ocean model, and the utility of priors used in terrain-based navigation over areas of significant bathymetric relief to bound uncertainty error in dead-reckoning navigation. This method improves upon our previously published works by 1) demonstrating the utility of our terrain-based navigation method with multiple field trials, and 2) presenting a hybrid algorithm that combines both approaches to bound navigational error and uncertainty for long-term deployments of underwater vehicles. We demonstrate the approach by examining data from actual field trials with autonomous underwater gliders, and demonstrate an ability to estimate geographical location of an underwater glider to 2 km. Utilizing the combined algorithm, we are able to prescribe an uncertainty bound for navigation, and instruct the glider to surface if that bound is exceeded during a given mission.
Highlights
Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), and their supporting infrastructure play a major role in the collection of oceanographic data (e.g., Godin et al (2006), Paley et al (2008), and Whitcomb et al (1999))
To address the issues of poor ocean model predictions along the shelf-break region, and to provide a method for localization while underwater, we present a least-squares optimization method for determining the path traversed by the glider based on gathered depth data, a terrain-based navigation approach
We examined multiple missions that were executed by autonomous gliders in coastal regions in southern California
Summary
Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), and their supporting infrastructure play a major role in the collection of oceanographic data (e.g., Godin et al (2006), Paley et al (2008), and Whitcomb et al (1999)). We propose to utilize ocean model predictions within a UKF to improve planning and navigation capabilities while incorporating a terrain-based navigation method for localization during plan execution. We combine a predictive approach that incorporates glider motion derived from an IMU and ocean model predictions into an UKF with a real-time, terrain-based navigation (TBN) algorithm. The presented approach builds upon prior work by the authors in both the incorporation of ocean models for planning and the use of terrain-based navigation for real-time localization and path reconstruction (Stuntz et al, 2015). 3. We present an algorithm that combines the predictive ocean model-based UKF method with the TBN algorithm to provide a robust methodology for maintaining a fixed navigational uncertainty for underwater gliders. Results are presented that demonstrate a significant increase in navigational accuracy compared with previously reported results
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