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Record W4384787996 · doi:10.1109/tcst.2023.3292466

Energy-Efficient Integrated Motion Planning and Control for Unmanned Surface Vessels

2023· article· en· W4384787996 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Control Systems Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsMotion controlMotion planningEnergy (signal processing)Unmanned surface vehicleMotion (physics)Control (management)Computer scienceAerospace engineeringControl engineeringEngineeringPhysicsRobotArtificial intelligenceMarine engineering

Abstract

fetched live from OpenAlex

This brief studies the online simultaneous motion planning and control of unmanned surface vessels (USVs) with multiple practical constraints. An online economic model predictive control (EMPC)-based integrated planning and control framework is developed to greatly reduce energy consumption. In particular, a novel heuristic terminal cost guarantees both the planning control performance and facilitates the online optimization, and an improved cross-entropy (CE)-based optimization algorithm speeds up the solving of the nonconvex economic optimization problem. Experimental results show that the proposed integrated planning and control approach can be implemented in real-time with the online optimization frequency of 100 Hz, and comparative studies indicate that it can save energy up to almost 18%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.834

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 science0.0000.000
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 head0.220
Teacher spread0.211 · 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