{"id":"W4402704011","doi":"10.3390/jmse12091655","title":"Autonomous Underwater Vehicle (AUV) Motion Design: Integrated Path Planning and Trajectory Tracking Based on Model Predictive Control (MPC)","year":2024,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Model predictive control; Control theory (sociology); Trajectory; Motion planning; Computer science; Hazard; Stability (learning theory); Control engineering; Lyapunov function; Nonlinear system; Control (management); Engineering; Robot; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004343418,0.0007552811,0.0006057973,0.0002703562,0.0003657584,0.000561967,0.001060421,0.000755135,0.0007875439],"category_scores_gemma":[0.000751658,0.000364689,0.0004041282,0.0003426099,0.0004870981,0.0007245937,0.0009429331,0.0008484045,0.0001637708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254569,"about_ca_system_score_gemma":0.001480606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005169442,"about_ca_topic_score_gemma":0.003376929,"domain_scores_codex":[0.9996262,0.00005780901,0.00001523471,0.0000904949,0.0001699118,0.00004027976],"domain_scores_gemma":[0.9998124,0.00004807286,0.00004743787,0.0000186608,0.00005942233,0.0000139989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003537512,0.00003145422,0.0003722332,0.0001284492,0.00002437067,0.00008591684,0.00008895187,0.9188552,0.009293963,0.009568066,0.0005809431,0.06093505],"study_design_scores_gemma":[0.000006200937,0.00005536875,0.00009227328,0.00000650273,0.000006269229,0.00001793245,0.000009398571,0.9958462,0.001551114,0.001340435,0.001064356,0.000003888223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00608696,0.0001533716,0.9920472,0.00006043719,0.00002144454,0.00002557874,0.00001034029,0.0001529129,0.001441906],"genre_scores_gemma":[0.8279964,0.0005027939,0.1670744,0.00008424385,0.00004645462,0.0002717403,0.0001035824,0.00005693135,0.00386354],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005169442,"threshold_uncertainty_score":0.0102787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914732795602564,"score_gpt":0.2173534265603566,"score_spread":0.198206098604331,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}