Advances in Underwater Robot Vehicles for Deep Ocean Exploration: Navigation, Control, and Survey Operations

Whitcomb, Louis L.; Yoerger, D.; Singh, Hanumant; Howland, Jonathan C.

doi:10.1007/978-1-4471-0765-1_53

Cited by 94 publications

(68 citation statements)

References 10 publications

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“…Although these sensors are common on underwater vehicles, many factors can hurt their accuracy [6,7]:…”

Section: Compassmentioning

confidence: 99%

“…This is typically O(10 − 100 m) for sonar and < 10 m for photography 6 . In cases without an a priori map, TRN will produce a self-consistent vehicle trajectory and map for the mission, but there will still be uncertainty in the geographic location unless there is another external position measurement 7 .…”

Section: Terrain-relative Navigationmentioning

confidence: 99%

“…In the KF framework, the filter doesn't really care about the exact value of any given measurement-it really only wants to know how far off the prediction was-this is the key that will enable us to handle measurements with delays. The update step of the KF applies the optimal gain to the innovation to compute a posteriori estimates of the state and covariance: 6) which brings us to the second key concept: the optimal Kalman gain.…”

Section: Adjusting the Prediction: Innovationsmentioning

confidence: 99%

“…The standard LBL system for field deployments in the deep ocean has a nominal operating frequency of 12 kHz 2 [3,4], with range up to 10 km and precision 3 of 0.1-10 m [6,7]. The update rate and measurement latency in this system depend on the range and local sound velocity profile, typically varying from 1 to 20 seconds.…”

Section: Acoustic Positioning Systemsmentioning

confidence: 99%

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Contributions to automated realtime underwater navigation

Stanway¹

2012

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This dissertation presents three separate-but related-contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field-some have been demonstrated in all three arenas.The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors.The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current-the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. 3Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework-without altering the filter structure or requiring any ad-hoc adjust...

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“…Although these sensors are common on underwater vehicles, many factors can hurt their accuracy [6,7]:…”

Section: Compassmentioning

confidence: 99%

Section: Terrain-relative Navigationmentioning

confidence: 99%

Section: Adjusting the Prediction: Innovationsmentioning

confidence: 99%

Section: Acoustic Positioning Systemsmentioning

confidence: 99%

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Contributions to automated realtime underwater navigation

Stanway¹

2012

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“…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)). Actively actuated (propeller-driven) underwater vehicles have proven effective in multiple sampling scenarios; however, they have limited deployment endurance.…”

Section: Introductionmentioning

confidence: 99%

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

2016

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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.

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A Terrain-Aided Tracking Algorithm for Marine Systems

Williams¹,

Mahon²

2006

Springer Tracts in Advanced Robotics

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Advances in Underwater Robot Vehicles for Deep Ocean Exploration: Navigation, Control, and Survey Operations

Cited by 94 publications

References 10 publications

Contributions to automated realtime underwater navigation

Contributions to automated realtime underwater navigation

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

A Terrain-Aided Tracking Algorithm for Marine Systems

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