An Underactuated Vehicle Localization Method in Marine Environments

Abstract

The underactuated vehicles are apposite for the longterm deployment and data collection in spatiotemporally varying marine environments. However, these vehicles need to estimate their positions (states) with intrinsic sensing in their long-term trajectories. In previous studies, autonomous underwater vehicles have commonly used vision and range sensors for autonomous state estimation. Inspired by the intrinsic sensing and the persistent deployment, we investigate the localization problem (state estimation) for an inexpensive and underactuated drifting vehicle called a drifter. In this paper, we present a localization method for the drifter making use of the observations of a proprioceptive sensor, i.e., compass. We create the water flow pattern within a given region from ocean model predictions, develop a stochastic motion model, and analyze the persistent water flow behavior. Given a distribution of initial deployment states of the drifter at a particular depth of the water column within the region and the water flow pattern, our method finds attractors and their transient groups at the given depth as the persistent behavior of the water flow. A most-likely localized trajectory of the drifter for a sequence of compass observations is generated based on the persistent behavior of the water flow and hidden Markov model. Our simulation results based on data from ocean model predictions substantiate good performance of our proposed localization method with a low error rate of the state estimation in the long-term trajectory of the drifter.

Towards Energy-Aware Feedback Planning for Long-Range Autonomous Underwater Vehicles

“…In our previous work ( Alam et al, 2018a , 2020 ), we have proposed an open-loop approach for solving the problem of deploying a set of minimally-actuated drifters for persistent monitoring of an aquatic environment. In our another work ( Alam et al, 2018b ), we predicted the localized trajectory of a drifter for a sequence of compass observations during its deployment in a marine environment. We presented a closed-loop approach ( Alam et al, 2018b ) when an AUV has a considerable unpredictability of executing its action in a dynamic marine environment.…”

Development of environmental niche models for use in underwater vehicle navigation

“…[25]. Further research on ATBN includes an examination on how to intelligently update our maps [34], and ways to predict spatiotemporal dynamics of the region [42], possibly through the incorporation of predictive ocean models [46]. Given enough deployments in the same region, covering a sufficient spatiotemporal epoch, one could investigate applications of machine or deep learning to incorporate dynamics onto the ATBN maps.…”

Feedback Motion Planning for Long-Range Autonomous Underwater Vehicles

“…In our previous work [15], we proposed an open-loop approach for solving the problem of deploying a set of minimally-actuated drifters for persistent monitoring of an aquatic environment. In our recent work [16], we predicted the localized trajectory of a drifter for a sequence of compass observations during its deployment in a marine environment. We presented a closedloop approach in [16] when an AUV has a considerable unpredictability of executing its action in a dynamic marine environment.…”

“…In our recent work [16], we predicted the localized trajectory of a drifter for a sequence of compass observations during its deployment in a marine environment. We presented a closedloop approach in [16] when an AUV has a considerable unpredictability of executing its action in a dynamic marine environment. Moreover, the previous studies [7], [17] on the Tethys AUV described the mission and other capabilities of the vehicle.…”