{"id":"W4313643885","doi":"10.1109/tnse.2023.3234720","title":"Connectivity Preservation and Collision Avoidance in Multi-Agent Systems Using Model Predictive Control","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Collision avoidance; Computer science; Position (finance); Control theory (sociology); Distributed computing; Controller (irrigation); Multi-agent system; Field (mathematics); Scheme (mathematics); Topology (electrical circuits); Control (management); Collision; Artificial intelligence; Engineering; Mathematics; Computer security","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":[0.001343236,0.0001712438,0.0002244893,0.0003144527,0.0003451939,0.0002681804,0.0003071406,0.00006981008,1.103116e-7],"category_scores_gemma":[0.00003735251,0.0001738796,0.00002495784,0.001829662,0.00007369796,0.001414974,0.0000102597,0.0001897736,0.00000190392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002111974,"about_ca_system_score_gemma":0.0001089016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008947479,"about_ca_topic_score_gemma":0.00001919308,"domain_scores_codex":[0.9982507,0.00004743732,0.0002640528,0.0005244072,0.0004283066,0.0004851005],"domain_scores_gemma":[0.9992075,0.0001925905,0.00006281261,0.0002755208,0.0001184316,0.0001431701],"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":[0.000009506947,0.0000211391,0.0001502533,0.00002466992,0.000008161878,0.000004110123,0.0002316637,0.9930293,0.00574856,0.0003172294,0.000007513157,0.0004478789],"study_design_scores_gemma":[0.0007808828,0.00003821699,0.002832237,0.0001667443,0.000007519815,0.000006959436,0.00003979313,0.995712,0.0002223551,0.00001452489,0.00001370401,0.0001650033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1931723,0.0001007279,0.8052964,0.00005052652,0.0007026076,0.0004580459,0.00001498978,0.000198573,0.000005797914],"genre_scores_gemma":[0.9973006,0.00005228684,0.002475042,0.00001969472,0.00002940703,0.00009541694,4.494036e-7,0.00001015693,0.00001691649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8041283,"threshold_uncertainty_score":0.7090605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265230708582384,"score_gpt":0.244379077081941,"score_spread":0.2117267699961172,"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."}}