{"id":"W2521613767","doi":"10.1109/tmech.2016.2612689","title":"Integrated Path Planning and Tracking Control of an AUV: A Unified Receding Horizon Optimization Approach","year":2016,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":262,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Control theory (sociology); Motion planning; Model predictive control; Kinematics; Trajectory; Path (computing); Computer science; Controller (irrigation); Tracking (education); Control engineering; Scheme (mathematics); Nonlinear system; Engineering; Control (management); Robot; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001086719,0.0009083092,0.0009952793,0.0004804161,0.000395714,0.0008030078,0.001263745,0.0008660961,0.0009606339],"category_scores_gemma":[0.0009514409,0.0005489602,0.0007297701,0.0004102869,0.0007594755,0.0008761775,0.0009677727,0.001020172,0.0002307737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007490299,"about_ca_system_score_gemma":0.001345925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00769082,"about_ca_topic_score_gemma":0.00397892,"domain_scores_codex":[0.9995102,0.0001366296,0.00002201402,0.00009757811,0.000172604,0.00006097739],"domain_scores_gemma":[0.999729,0.00009360671,0.00005823386,0.00002554211,0.00007434138,0.00001919539],"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":[0.00001629105,0.00001068139,0.000070681,0.00003124229,0.00001118852,0.00002305123,0.00003388357,0.9829432,0.001166464,0.005107637,0.0001225013,0.01046325],"study_design_scores_gemma":[0.000003361438,0.00002362057,0.00002389159,0.000002275966,0.000002969787,0.000003835516,0.000003797061,0.9987877,0.0002532236,0.0006773999,0.0002150721,0.000002798771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003931719,0.0001177753,0.994422,0.00003776938,0.00001117048,0.00001759073,0.000007386544,0.00008461058,0.001370061],"genre_scores_gemma":[0.7333894,0.0004797885,0.261758,0.00007848483,0.00005938555,0.0002915031,0.0000804326,0.00006660882,0.003796404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00769082,"threshold_uncertainty_score":0.01529211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01947097067289543,"score_gpt":0.2246544058943231,"score_spread":0.2051834352214277,"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."}}