Energy efficient navigational methods for autonomous underwater gliders in surface denied regions
Bibliographic record
Abstract
Autonomous underwater gliders routinely perform long duration profiling missions while characterizing the chemical, physical and biological properties of the water column. These measurements have opened up new ways of observing the ocean’s processes and their interactions with the atmosphere across time and length scales which were not previously possible. Extending these observations to ice-covered regions is of importance due to their role in ocean circulation patterns, increased economic interest in these areas and a general sparsity of observations. This thesis develops an energy optimal depth controller, a terrain aided navigation method and a magnetic measurement method for an autonomous underwater glider. A review of existing methods suitable for navigation in underwater environments as well as the state of the art in magnetic measurement and calibration techniques is also presented. The energy optimal depth controller is developed and implemented based on an integral state feedback controller. A second order linear time invariant system is identified from field data and used to compute the state feedback controller gains through an augmented linear quadratic regulator. The resulting gains and state feedback controller methodology are verified through field trials and found to control the depth of the vehicle while losing less than one percent of the vehicle’s propulsive load to control inputs or lift induced drag. The terrain aided navigation method is developed based on a jittered bootstrap algorithm which is a type of particle filter that makes use of the vehicle’s deadreckoned navigation solution, onboard altimeter and a local digital parameter model (DPM). An evaluation is performed through post-processing offline location estimates from field trials which took place in Holyrood Arm, Newfoundland, overlapping a previously collected DPM. During the post-processing of these trials, the number of particles, jittering variance and DPM grid cell size were varied. Online open loop field trials were performed through integrating a new single board computer. In these trials the localization error remained bounded and improved on the dead reckoning error, validating the filter despite the large dead-reckoned errors, single beam altitude measurements, and short test duration. Terrain aided navigation methods perform poorly in regions of flat terrain or in deep water where the seafloor is beyond the range of the altimeter. Magnetic measurements of the Earth’s main field have been proposed previously to augment terrain aided navigation algorithms in these regions. To this end a low power magnetic instrumentation suite for an underwater glider has been developed. Two calibration methodologies were also developed and compared against regional digital models of the magnetic field. The calibration methods include one for which the actuators in the vehicle were kept in fixed locations and a second for which the calibration coefficients were parameterized for the actuator locations. Both methods were found to agree with the low frequency content in the a-priori regional magnetic anomaly grids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".