Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

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 <100 m over paths of length >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.

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

“…For example, in [11,12] the authors presented an autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation to avoid these areas. In [13] authors present an application for the practical use of priors and predictions for large-scale ocean sampling. 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.…”

Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

“…This system became redundant after the introduction of GPS, although it is still a useful navigational aid for GPS-denied environments, e.g., underwater. Until recently, the utility of this TBN for underwater vehicles was low due to the poor resolution of bathymetric maps; however, updated bathymetry maps with higher resolution provide motivation for revisiting the application of this method for low-power, accurate navigation underwater, see [SKS16]. One clearly identified shortcoming of TBN in the aquatic environment is the lack of accurate, high-resolution maps of the sea floor in many regions.…”

“…An approach to implementing a traditional terrain-based navigation methodology for localization is described in [SKS16]. In this work, maps provided by the Southern Cal- The ocean is a vast and complex environment and the most important question we can ask is when and where to sample so that the data is valuable for further understanding of the dynamically evolving phenomena that occur.…”

“…Updated bathymetric maps with higher resolution provide motivation for revisiting the application of this method for low-power, accurate navigation underwater, see e.g. [SKS16]. It is also important to highlight that TBN for autonomous underwater vehicles differ from the application by aircrafts due to the vehicles dynamics, sensor capabilities and terrain variability.…”

“…In [KEW06], it is concluded that improved navigation will enable new missions that would previously have been considered infeasible or impractical. Updated bathymetric maps with higher resolution provide motivation for revisiting the application of this method for low-power, accurate navigation underwater, see, e.g., [SKS16].…”