{"id":"W3216729193","doi":"10.1002/rnc.5908","title":"Hybrid‐triggered formation tracking control of mobile robots without velocity measurements","year":2021,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Asynchronous communication; Control theory (sociology); Computer science; Transmission (telecommunications); Event (particle physics); Mobile robot; Observer (physics); Sampling (signal processing); Position (finance); Control (management); Relative velocity; Robot; Stability (learning theory); Real-time computing; Detector; Artificial intelligence; Computer network; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009906174,0.0001648103,0.0004889262,0.0001830833,0.00005763394,0.0002211895,0.0006718397,0.00005954623,0.00001381043],"category_scores_gemma":[0.0003519901,0.0001468414,0.0002080055,0.0001070372,0.00003506381,0.001065585,0.00005764954,0.0001945902,0.000003878901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107532,"about_ca_system_score_gemma":0.0002244361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001164514,"about_ca_topic_score_gemma":0.00000765803,"domain_scores_codex":[0.9974316,0.0002048295,0.0009854634,0.0001925649,0.0009872398,0.0001983199],"domain_scores_gemma":[0.995692,0.0001711175,0.001041775,0.0001936562,0.002776769,0.0001246461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002847484,0.002859348,0.06645461,0.0002621856,0.007204505,0.001443968,0.001941091,0.2987698,0.2278742,0.002501142,0.00126362,0.386578],"study_design_scores_gemma":[0.0174532,0.0002221769,0.005593643,0.0003285011,0.0001371287,0.0009282282,0.00008169014,0.9556126,0.01750133,0.0001690019,0.001709903,0.0002626242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08965964,0.001082052,0.9069495,0.0008199881,0.001113909,0.0002056193,0.00007293328,0.00001870133,0.00007768723],"genre_scores_gemma":[0.9894853,0.00004616033,0.009871545,0.0001979018,0.0003483943,0.0000064787,0.00001110153,0.000009256288,0.00002389497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8998256,"threshold_uncertainty_score":0.5988017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03374138308675026,"score_gpt":0.2706794668765874,"score_spread":0.2369380837898372,"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."}}