{"id":"W4236083144","doi":"10.2316/j.2021.206-0563","title":"SELF-COMPETITION LEADER–FOLLOWER MULTI-AUV FORMATION CONTROL BASED ON IMPROVED PSO ALGORITHM WITH ENERGY CONSUMPTION ALLOCATION","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Energy consumption; Competition (biology); Consumption (sociology); Control (management); Energy (signal processing); Mathematical optimization; Algorithm; Artificial intelligence; Engineering; Mathematics; Sociology; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001046518,0.0001325048,0.0001464803,0.000160657,0.00006426244,0.000103088,0.00008336901,0.00006483845,0.00001275866],"category_scores_gemma":[0.00001551598,0.0001211413,0.00005268911,0.00009063514,0.00002014822,0.0004636876,0.000006544792,0.0001220704,0.000003400185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145421,"about_ca_system_score_gemma":0.00004236992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001629438,"about_ca_topic_score_gemma":0.000007680047,"domain_scores_codex":[0.9990778,0.00002576613,0.0003798623,0.0001070025,0.0003050585,0.0001045172],"domain_scores_gemma":[0.998949,0.00006309455,0.0002324829,0.00008549597,0.0006097955,0.00006013343],"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":[0.00002927667,0.0002284445,0.00007514113,0.00004069394,0.0001538891,0.00001786608,0.0001084269,0.9022307,0.006973505,0.00312331,0.00004979324,0.08696894],"study_design_scores_gemma":[0.001978606,0.00007850528,0.001262135,0.0001130259,0.0000454254,0.0001014852,0.00004810844,0.9902954,0.004815165,0.0001740097,0.0009546205,0.0001335318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003128842,0.0001347208,0.9953176,0.0008029158,0.0003625851,0.00008385206,0.00002064876,0.00008412456,0.00006467135],"genre_scores_gemma":[0.7998713,0.0002848461,0.1993328,0.0001832316,0.0001658612,0.00001182183,0.0001121643,0.00001911099,0.00001890932],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7967424,"threshold_uncertainty_score":0.494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006682811710769555,"score_gpt":0.2240183132167868,"score_spread":0.2173355015060172,"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."}}