{"id":"W4294167364","doi":"10.1109/tsmc.2022.3199112","title":"A Novel SMMS Teleoperation Control Framework for Multiple Mobile Agents With Obstacles Avoidance by Leader Selection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Teleoperation; Obstacle avoidance; Control theory (sociology); Nonholonomic system; Controller (irrigation); Maxima and minima; Computer science; Mobile robot; Collision avoidance; Control engineering; Trajectory; Engineering; Robot; Artificial intelligence; Control (management); Mathematics; Collision","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":[0.0002161659,0.0005426785,0.0003271511,0.0002066172,0.0004524131,0.0004049263,0.0008943608,0.000428915,0.00127204],"category_scores_gemma":[0.0002028403,0.0001481204,0.0004052426,0.0001646756,0.0003773993,0.0004982677,0.0006936352,0.0004930954,0.0001826888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003774507,"about_ca_system_score_gemma":0.0007665473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004857989,"about_ca_topic_score_gemma":0.003707119,"domain_scores_codex":[0.9998069,0.00002638322,0.00001107626,0.00006098607,0.00007091212,0.00002365446],"domain_scores_gemma":[0.9999242,0.000009925915,0.00001881313,0.000007882289,0.00003053589,0.000008705698],"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":[0.0001559001,0.00007779259,0.001273959,0.0002497292,0.00006551618,0.0004738665,0.0004850366,0.7339817,0.05562732,0.03616514,0.00290279,0.1685413],"study_design_scores_gemma":[0.00002167209,0.000123246,0.000204529,0.000006567692,0.000008412745,0.00005818351,0.00003062452,0.9926796,0.002310947,0.001767357,0.002779941,0.000008894514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01709137,0.000231274,0.9782503,0.0001126251,0.00005852249,0.00003759718,0.00001774969,0.0003251707,0.003875391],"genre_scores_gemma":[0.8934847,0.0002519993,0.1003709,0.00007118916,0.00006134724,0.0002040138,0.00006063287,0.00001905868,0.005476089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004857989,"threshold_uncertainty_score":0.00965941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390410531047904,"score_gpt":0.2109397238247926,"score_spread":0.1970356185143136,"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."}}