{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002792325,0.0003426381,0.0004436284,0.0001584128,0.000617774,0.0002936858,0.0001371938,0.0001567711,0.00002753672],"category_scores_gemma":[0.000004031309,0.0003316683,0.00007663506,0.0002500638,0.0000401786,0.0001379688,0.000001215885,0.0003493167,0.00001273673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002466191,"about_ca_system_score_gemma":0.0000300777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003300656,"about_ca_topic_score_gemma":0.0001006073,"domain_scores_codex":[0.9980713,0.0001252764,0.0005720815,0.0004238049,0.0004363441,0.0003712368],"domain_scores_gemma":[0.9991474,0.0002121826,0.0001198911,0.0002609274,0.0001158194,0.0001438227],"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":[0.00009620231,0.0001734909,0.0001272245,0.0003960122,0.000239998,0.000001256061,0.001412863,0.9811382,0.01346422,0.0005292295,0.00178926,0.0006320819],"study_design_scores_gemma":[0.002682835,0.0006712457,0.00005270429,0.0001889588,0.00008946739,0.0001719954,0.005327577,0.9565833,0.001495153,0.000002600861,0.03218099,0.0005531381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06331845,0.001146586,0.929309,0.00002105582,0.002320894,0.002795114,0.0005436504,0.0003919639,0.0001532956],"genre_scores_gemma":[0.9920016,0.00004053583,0.0003598536,0.00005151349,0.000175283,0.004886916,0.00002593384,0.0001064904,0.002351911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9289491,"threshold_uncertainty_score":0.9999135,"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."}}