{"id":"W2950061144","doi":"10.48550/arxiv.1812.04755","title":"Aerial navigation in obstructed environments with embedded nonlinear model predictive control","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"KU Leuven; Vlaamse regering; European Commission; Queen's University; Fonds Wetenschappelijk Onderzoek; Fonds De La Recherche Scientifique - FNRS; Queen's University Belfast","keywords":"Model predictive control; Control theory (sociology); Obstacle avoidance; Computer science; Nonlinear system; Simple (philosophy); Obstacle; Reduction (mathematics); Nonlinear model; Control engineering; Control (management); Engineering; Mathematics; Artificial intelligence; Mobile robot; Robot; Physics; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001744888,0.0004852564,0.0003156899,0.0001416233,0.0002249444,0.0003191534,0.0005037503,0.0003531574,0.0006569812],"category_scores_gemma":[0.0004637312,0.0002034677,0.00020034,0.0001455898,0.0004407411,0.0003883925,0.0007718807,0.0004726266,0.0001206618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002192453,"about_ca_system_score_gemma":0.0005002913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003959764,"about_ca_topic_score_gemma":0.003634496,"domain_scores_codex":[0.9999027,0.00001780691,0.000003470238,0.00001809998,0.00004588377,0.00001197451],"domain_scores_gemma":[0.9998717,0.0000470438,0.00002938633,0.00001758678,0.00002692281,0.000007260279],"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":[0.00002152495,0.00001348948,0.0002988587,0.00003603348,0.00001174531,0.00005023052,0.000038285,0.9642547,0.005159961,0.00448921,0.000281605,0.0253443],"study_design_scores_gemma":[0.000002577149,0.00001286853,0.00003924075,0.00000125245,0.000001226525,0.000004560969,0.000002530399,0.998482,0.0005333007,0.0007053537,0.0002137736,0.000001371677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02384849,0.0001148428,0.9739222,0.00006228882,0.00002109454,0.00001553096,0.0000123891,0.0002112802,0.001791937],"genre_scores_gemma":[0.858621,0.0001363229,0.1388753,0.00003860678,0.00002725694,0.00008551592,0.0000422179,0.00003372974,0.002140066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003959764,"threshold_uncertainty_score":0.007873476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160319127967215,"score_gpt":0.1538738084536939,"score_spread":0.1378418956569724,"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."}}