{"id":"W2889292513","doi":"10.1109/icca.2018.8444273","title":"Distributed MPC based Collision Avoidance Approach for Consensus of Multiple Quadcopters","year":2018,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collision avoidance; Quadcopter; Computer science; Collision; Position (finance); Model predictive control; Control theory (sociology); Logarithm; Consensus; Mathematical optimization; Control (management); Multi-agent system; Mathematics; Engineering; Artificial intelligence; Aerospace engineering; 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":[0.0006155329,0.0002256382,0.0003926739,0.00009970206,0.0001603029,0.00009447825,0.001006069,0.0001139194,0.000006901882],"category_scores_gemma":[0.0004483556,0.0001967645,0.0001634023,0.0005062351,0.0001873916,0.0001492332,0.0001120686,0.00006948783,0.0000224115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008438786,"about_ca_system_score_gemma":0.0001340641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001423722,"about_ca_topic_score_gemma":0.00002645672,"domain_scores_codex":[0.9978858,0.0001259161,0.0005681094,0.0005981319,0.0003729083,0.0004490882],"domain_scores_gemma":[0.9974191,0.0006003769,0.000309961,0.0009484199,0.0005714261,0.0001507813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004207528,0.007649746,0.05574463,0.002458688,0.001303078,0.00005183541,0.002674093,0.06854777,0.2969794,0.2065701,0.3137674,0.04004584],"study_design_scores_gemma":[0.003140801,0.0002252171,0.001129726,0.00003064226,0.00001190043,0.00000290007,0.0000680992,0.955296,0.03566756,0.00005489341,0.004133977,0.0002382378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0131932,0.00003086048,0.983174,0.0003719852,0.000427314,0.001109648,0.0004438254,0.0002423508,0.001006837],"genre_scores_gemma":[0.8112533,2.891483e-7,0.1882404,0.0001198566,0.00006183911,0.00009627886,0.0001117335,0.00001148981,0.0001048686],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8867483,"threshold_uncertainty_score":0.8023825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540337444977509,"score_gpt":0.2521972658531205,"score_spread":0.2267938914033454,"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."}}