{"id":"W3000722010","doi":"10.1109/tsmc.2020.2963889","title":"Visual Regulation of Differential-Drive Mobile Robots: A Nonadaptive Switching Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China; Yancheng Institute of Technology","keywords":"Computer science; Convergence (economics); Controller (irrigation); Mobile robot; Differential (mechanical device); Robot; Control theory (sociology); Monocular; Artificial intelligence; Monocular vision; Computer vision; Control (management); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001310612,0.0002509946,0.0004437423,0.0001442378,0.0001747325,0.0002080869,0.0003035336,0.0000958607,0.000002901807],"category_scores_gemma":[0.000002899716,0.0002282907,0.0000947451,0.0003411737,0.0000484255,0.0003300234,0.000009211896,0.0002224115,0.00001197602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004928704,"about_ca_system_score_gemma":0.00003095207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001292565,"about_ca_topic_score_gemma":0.000001976706,"domain_scores_codex":[0.9979954,0.0001801408,0.0005786419,0.0005418827,0.0004596811,0.0002441978],"domain_scores_gemma":[0.9989805,0.00008349958,0.0002812657,0.0003175371,0.0001333595,0.0002038248],"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":[0.00014487,0.000730963,0.00008717337,0.001504326,0.0003474776,0.00001295086,0.02024508,0.7996342,0.09588821,0.01444656,0.0002298206,0.06672837],"study_design_scores_gemma":[0.0005633672,0.0003084041,0.00008534113,0.0002177439,0.00002479334,0.00003057519,0.001844056,0.9940715,0.002264611,0.00001601731,0.0003273222,0.0002462706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01010164,0.000314407,0.9872403,0.00003980473,0.0007121282,0.0007289651,0.00001065443,0.0001467658,0.0007053678],"genre_scores_gemma":[0.9956284,0.00003153957,0.003763387,0.00003327026,0.00008399868,0.00008807212,0.000002040259,0.00002461774,0.000344719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9855267,"threshold_uncertainty_score":0.9309425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721180324696289,"score_gpt":0.240704113339744,"score_spread":0.2234923100927811,"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."}}