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Record W2490454929

Dynamics And Control Simulator For the Theseus AUV

2000· article· en· W2490454929 on OpenAlexaboutno aff
Mae Seto, George D. Watt

Bibliographic record

VenueThe Proceedings of the ... International Offshore and Polar Engineering Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPayload (computing)Marine engineeringModular designEngineeringTowingSimulationRange (aeronautics)Computer scienceAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

A nonlinear, 6 degree-of-freedom maneuvering dynamics and control simulator for the Theseus autonomous underwater vehicle (AUV) has been validated against several sets of full scale trials and independent predictive methods. The vehicle hydrodynamic coefficients are estimated with standard theoretical and empirical methods augmented with towing tank test results. The control algorithm implemented in the simulator is the same as the one used in the actual vehicle. A propulsion model makes it possible to optimize vehicle range and transit speeds as the battery capacity changes during the mission. The simulated vehicle response compares well against full scale sea trials data. The paper discusses the simulator validation and its use for vehicle design and mission applications. 1.0 INTRODUCTION In 1992, Defence Research Establishment Pacific (now amalgamated with Defence Research Establishment Atlantic (DREA)) contracted ISE Research Ltd. (ISER) to build an AUV capable of laying cable under Arctic ice. ISER designed and built the Theseus AUV shown in Figure 1 for a 450 km range, a cruising speed of 2 m/s, a working depth of 1000 m, and with variable ballast tanks fore and aft. With a 2.44 m long by 1.12 m diameter payload bay and additional ballast tanks to correct for deploying cable, Theseus can lay up to 220 km of fiber-optic cable in its current configuration. The vehicle has a modular design for ease of transport, fault-tolerant control software, navigational accuracy to better than 0.5% of distance traveled (cross track error is much better), acoustic and fiber-optic telemetry systems, and terminal acoustic homing (Ferguson et al, 1995). In April 1995, Theseus went on its first Arctic mission in the ice covered waters offEllesmere Island, Canada. The successful trial verified launch and recovery procedures, tested all vehicle systems in an under-ice environment (navigation, telemetry, cable deployment, etc.), and led to refined techniques for delivering fibre-optic cable under the ice. Four dives accumulated 13 kin of under-ice distance traveled and laid 9 km of fiber-optic cable. , i ~' ~ Cable Packs ., buoyancy Tank Cable Exit Tube ~/ Figure 1: The Theseus AUV is 10.5 m long, 1.3 m diameter, displaces 119 kN, and has a 5 kW electric propulsion motor In April 1996 at Alert, Canada, Theseus met its operational objectives and laid 175 km of cable. The AUV was deployed through a 2 m by 13 m hole in the 1.7 m thick ice, traversed at depths of up to 425 m, delivered the other end of the fiber optic cable by flying through a 200 m loop suspended from the ice surface and returned to the launch site (Ferguson et al, 1999). The vehicle was autonomous for all of the 54 hour, 350 km round trip except when delivering the far end of the cable, when manual control of the vehicle via this same cable was used. This experience, which spans design, construction, and operations, has shown that judicious use of vehicle dy~mics and control models can contribute to many stages of the development process. The models are valuable tools for: vehicle hullfonn and control algorithm design; testing old and developing new maneuvers; evaluating vehicle responses; determining how top speed, endurance, battery power, and propulsion requirements can be met; experimenting with ballast compensation strategies to address payload changes during a mission; analyzing navigational schemes; investigating emergency scenarios, and minimizing costly sea trial time. However, these models need to be validated to be of practical use. ISER has been collaborating with DREA to develop and validate vehicle

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.196
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueThe Proceedings of the ... International Offshore and Polar Engineering ConferenceSame topicUnderwater Vehicles and Communication SystemsFrench-language works237,207