{"id":"W4413074153","doi":"10.1109/tie.2025.3585028","title":"Neurodynamics-Based Visual Servo Predictive Control for Improving Smooth Movement of Logistics Omnidirectional Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Elevator Systems and Control","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Omnidirectional antenna; Robot; Computer science; Visual servoing; Servo; Servo control; Mobile robot; Model predictive control; Artificial intelligence; Control (management); Control engineering; Computer vision; Control theory (sociology); 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":[0.0001866332,0.0004288302,0.0002629822,0.0001776545,0.0002434867,0.0003372366,0.0004505223,0.0002922481,0.0004363899],"category_scores_gemma":[0.0003586431,0.0001959897,0.0001931977,0.0001670296,0.0003548488,0.0003044118,0.0004693971,0.0003718215,0.00009098181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382395,"about_ca_system_score_gemma":0.0006751145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005815475,"about_ca_topic_score_gemma":0.005153786,"domain_scores_codex":[0.9999036,0.00001393026,0.000004613969,0.000021343,0.00004354192,0.0000130069],"domain_scores_gemma":[0.9998907,0.00002829529,0.00003239739,0.00000837622,0.00003318441,0.000007121807],"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":[0.0001023922,0.00003884308,0.0006194064,0.0001038926,0.00002600115,0.0001553452,0.0001141362,0.8995015,0.03470416,0.005450894,0.0008064962,0.05837695],"study_design_scores_gemma":[0.000006869775,0.00004779674,0.0002450877,0.000003165667,0.00000464278,0.0000170247,0.000008230431,0.9966069,0.00183807,0.0006710875,0.0005468929,0.000004229945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05506822,0.0003985976,0.9383399,0.0001636088,0.00004653312,0.00003747391,0.00002247394,0.0002851853,0.005637926],"genre_scores_gemma":[0.9786661,0.0001854203,0.01906615,0.00005234895,0.00001169197,0.0000464465,0.00002184399,0.00001290409,0.001937081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005815475,"threshold_uncertainty_score":0.01156324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009375600639232,"score_gpt":0.2268493412966064,"score_spread":0.2167555852902141,"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."}}