{"id":"W3180950260","doi":"10.1007/s11590-021-01773-6","title":"An exact penalty function method for optimal control of a dubins airplane in the presence of moving obstacles","year":2021,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Airplane; Computational intelligence; Penalty method; Mathematical optimization; Optimal control; Function (biology); Mathematics; Control (management); Computer science; Variable (mathematics); Control theory (sociology); Engineering; Artificial intelligence; Mathematical analysis; 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":[0.0006929302,0.0008138379,0.0009576319,0.0004080939,0.0004128331,0.0007655611,0.0009744123,0.001355184,0.002130256],"category_scores_gemma":[0.001201782,0.0003854228,0.0003980789,0.0003267662,0.0006721184,0.0007083279,0.0009940478,0.0009368762,0.0003398452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000474926,"about_ca_system_score_gemma":0.001008882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006271498,"about_ca_topic_score_gemma":0.004175144,"domain_scores_codex":[0.9997289,0.00009360955,0.000009378402,0.0000354477,0.00009776789,0.00003496619],"domain_scores_gemma":[0.9996092,0.0001791019,0.00003100302,0.0000282289,0.0001107676,0.00004173696],"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":[0.00007564387,0.00002891261,0.0001222715,0.0000600062,0.00001436465,0.00004930989,0.00003318867,0.9613029,0.002467705,0.01096649,0.0006778471,0.02420137],"study_design_scores_gemma":[0.000002138144,0.000007505908,0.00001323393,0.00000149941,6.687207e-7,0.000002850987,0.000001676619,0.9992945,0.00009729309,0.0003931979,0.0001836508,0.00000171256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01008307,0.0001375925,0.9871573,0.0001005803,0.00007656211,0.00002216604,0.00001934138,0.00009717924,0.002306319],"genre_scores_gemma":[0.6409571,0.0003264374,0.3472745,0.0001431407,0.0001164654,0.0002488084,0.0001441418,0.0002303289,0.0105592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006271498,"threshold_uncertainty_score":0.01247001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01543760058647594,"score_gpt":0.2725781647899529,"score_spread":0.257140564203477,"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."}}