{"id":"W4407016712","doi":"10.52202/078368-0047","title":"Machine Learning-Based Model Predictive Control Motion Planning for Autonomous On-Orbit Assembly","year":2024,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Model predictive control; Computer science; Motion planning; Motion control; Orbit (dynamics); Control (management); Control engineering; Motion (physics); Control theory (sociology); Artificial intelligence; Engineering; Robot; Aerospace engineering","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":[0.0003965761,0.0004956973,0.0007278905,0.0004475437,0.0004544305,0.0006328063,0.0005898406,0.0005745219,0.001309738],"category_scores_gemma":[0.0009960675,0.0004501435,0.0003908787,0.0004378611,0.000571341,0.0004963569,0.0006052375,0.0008744002,0.0002113918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007105786,"about_ca_system_score_gemma":0.001093331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0182584,"about_ca_topic_score_gemma":0.01404023,"domain_scores_codex":[0.9998,0.00004659764,0.000008135135,0.00003580036,0.00007399532,0.00003550513],"domain_scores_gemma":[0.9996628,0.0001518736,0.00006647094,0.00002539614,0.00007608136,0.00001740905],"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":[0.00002005924,0.00001001279,0.00008012441,0.000009738436,0.000005117519,0.000008729944,0.000009268858,0.9890584,0.000454246,0.0005657444,0.0001932396,0.009585313],"study_design_scores_gemma":[0.000001234531,0.000006186709,0.0000314813,7.498655e-7,6.304283e-7,8.684036e-7,0.000001016589,0.9995201,0.0000982694,0.0002944245,0.00004425523,7.597502e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0703579,0.0003921732,0.9223643,0.0002844177,0.00009235753,0.00005042189,0.00007163703,0.000756301,0.005630454],"genre_scores_gemma":[0.9691358,0.00009312462,0.02869035,0.00003310511,0.00001489617,0.00005436192,0.00005987984,0.0000350252,0.001883513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0182584,"threshold_uncertainty_score":0.03630424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123987700263979,"score_gpt":0.2276362471875192,"score_spread":0.2163963701848794,"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."}}