{"id":"W2943764792","doi":"10.1109/tie.2019.2913813","title":"Robust Vision-Based Tube Model Predictive Control of Multiple Mobile Robots for Leader–Follower Formation","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Quadratic programming; Computer science; Robot; Control theory (sociology); Model predictive control; Kinematics; Artificial intelligence; Computer vision; Camera resectioning; Artificial neural network; Calibration; Mobile robot; Controller (irrigation); Control engineering; Engineering; Control (management); Mathematics; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005240162,0.0003257808,0.0005361302,0.0002716206,0.0001870271,0.00009972721,0.000713739,0.0004165286,0.00001384557],"category_scores_gemma":[0.00003117059,0.0003248905,0.000383384,0.0004905793,0.0000423929,0.0008588045,0.000002546959,0.0005436226,0.00002724051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000519443,"about_ca_system_score_gemma":0.0007110671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002546272,"about_ca_topic_score_gemma":0.0000326756,"domain_scores_codex":[0.9973118,0.000144231,0.0007484946,0.0005592691,0.0005731073,0.0006630946],"domain_scores_gemma":[0.997763,0.0006366645,0.0003654975,0.0007404365,0.0003588237,0.0001355686],"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":[0.0007033403,0.0003989949,0.000007909004,0.00002287304,0.0001135346,3.193913e-7,0.00009086945,0.9839258,0.008383259,0.0002815677,0.0002440733,0.005827489],"study_design_scores_gemma":[0.01177038,0.001826733,0.000001884916,0.00007113314,0.00008255058,0.00000190537,0.00004179565,0.9430903,0.04236121,0.00005780512,0.0004210919,0.0002731949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008016984,0.00006340195,0.9861926,0.0002378495,0.001195559,0.003580329,0.0004496792,0.0002073733,0.00005617538],"genre_scores_gemma":[0.9971669,0.000005528917,0.001648947,0.00009356473,0.00006814808,0.0007649749,0.00003220677,0.00003398483,0.0001856936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.98915,"threshold_uncertainty_score":0.9999203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03289098497819935,"score_gpt":0.2432857266253821,"score_spread":0.2103947416471828,"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."}}