Optimal trajectory generation in ocean flows

Inanc, Tamer; Shadden, Shawn C.; Marsden, Jerrold E.

doi:10.1109/acc.2005.1470035

Cited by 81 publications

(73 citation statements)

References 16 publications

(23 reference statements)

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“…Fortunately, the ever-increasing computational power and the emergence of multiple in-situ ocean-sampling techniques have contributed to the recent progress in ocean general circulation models (OGCMs) that simulate the general movement of large-scale ocean currents. By closing this knowledge gap, researchers have been utilizing the background flow dynamics in various aspects of underwater robotics, including path planning and vehicle guidance [20]- [22], multi-vehicle cooperative control [23]- [25], optimal sensing [26]- [29], sensor network mobility analysis [30], [31], and mobile sensor allocation [32]. Nonetheless, to the best of our knowledge, the background flow dynamics has not been well exploited for underwater localization and navigation problems with only a few exceptions [33]- [37].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Song

Mohseni

2018

IEEE J. Oceanic Eng.

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A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in longduration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this concept through mid-depth, underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local, ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller-scale, turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved, small-scale flow perturbations are present. For a 6-hour navigation with an automotive-grade inertial navigation system, the currentaided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a turbulent, meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-hour mission, positioning result with under 25% UDT for a 24-hour navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.

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Section: Introductionmentioning

confidence: 99%

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Song

Mohseni

2018

IEEE J. Oceanic Eng.

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“…This chapter is to extend the previously proposed method [11] for quickly determining near optimal glider trajectories between two fixed points in the ocean based on approximate ocean current data. It will show that optimal trajectories computed using NTG corresponds to LCS obtained using the Direct Lyapunov Exponent method [51].…”

Section: E Dissertation Outlinementioning

confidence: 99%

“…The efficiency and sustainability of the glider operation are important considerations for the control of the glider. Therefore, the ability to quickly determine the most efficient trajectory for the glider is important [11].…”

Section: A Motivationmentioning

confidence: 99%

“…There are two parts are tackled in this chapter. One is to improve the previous analytical ocean flows model [11], which is required in the NTG formulation, to a 3D model using B-spline functions. The other is to establish a new dynamical model of the glider.…”

Section: E Dissertation Outlinementioning

confidence: 99%

“…As shown in Figure 1 by NOAA (National Oceanic and Atmosphere Administration), scientists and engineers are deploying gliders for data collection. As another example of robotic explorer application, the Autonomous Ocean Sampling Network (AOSN) [10] project, [11], [12], [13] aims to advance the ability to observe and predict the ocean by bringing together sophisticated new robotic vehicles (gliders) with advanced ocean models. In the AOSN project, two types of gliders are employed, which are the Slocum [4], [14] and the Spray [15] .…”

Section: A Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Optimal trajectory generation with DMOC versus NTG : application to an underwater glider and a JPL aerobot.

Zhang¹

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Energy‐optimal path planning in the coastal ocean

2017

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We integrate data‐driven ocean modeling with the stochastic Dynamically Orthogonal (DO) level‐set optimization methodology to compute and study energy‐optimal paths, speeds, and headings for ocean vehicles in the Middle‐Atlantic Bight (MAB) region. We hindcast the energy‐optimal paths from among exact time‐optimal paths for the period 28 August 2006 to 9 September 2006. To do so, we first obtain a data‐assimilative multiscale reanalysis, combining ocean observations with implicit two‐way nested multiresolution primitive‐equation simulations of the tidal‐to‐mesoscale dynamics in the region. Second, we solve the reduced‐order stochastic DO level‐set partial differential equations (PDEs) to compute the joint probability of minimum arrival time, vehicle‐speed time series, and total energy utilized. Third, for each arrival time, we select the vehicle‐speed time series that minimize the total energy utilization from the marginal probability of vehicle‐speed and total energy. The corresponding energy‐optimal path and headings are obtained through the exact particle‐backtracking equation. Theoretically, the present methodology is PDE‐based and provides fundamental energy‐optimal predictions without heuristics. Computationally, it is 3–4 orders of magnitude faster than direct Monte Carlo methods. For the missions considered, we analyze the effects of the regional tidal currents, strong wind events, coastal jets, shelfbreak front, and other local circulations on the energy‐optimal paths. Results showcase the opportunities for vehicles that intelligently utilize the ocean environment to minimize energy usage, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.

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Optimal trajectory generation in ocean flows

Cited by 81 publications

References 16 publications

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Optimal trajectory generation with DMOC versus NTG : application to an underwater glider and a JPL aerobot.

Energy‐optimal path planning in the coastal ocean

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