{"id":"W2048255852","doi":"10.1109/wcica.2014.7052858","title":"A potential field-based PSO approach for cooperative target searching of multi-robots","year":2014,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Flexibility (engineering); Particle swarm optimization; Computer science; Swarm robotics; Field (mathematics); Fitness function; Potential field; Mobile robot; Function (biology); Artificial intelligence; Swarm behaviour; Machine learning; Mathematics; Genetic algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0007237267,0.0001594894,0.0002992584,0.00009160721,0.0001186656,0.0001257504,0.0008325608,0.00007921596,0.00001076455],"category_scores_gemma":[0.0002855527,0.0001321092,0.0001382405,0.0001872397,0.00003127128,0.0002300506,0.0001132927,0.0001051974,0.000008462371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002349033,"about_ca_system_score_gemma":0.00008810163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001094692,"about_ca_topic_score_gemma":0.000005945282,"domain_scores_codex":[0.9983807,0.0002035116,0.0003632084,0.0004255901,0.0002818568,0.0003451072],"domain_scores_gemma":[0.9987264,0.0002729297,0.0001330599,0.0005067352,0.0002529844,0.0001078637],"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":[0.0002197131,0.00170566,0.001320334,0.0005081587,0.0003048035,0.000007346312,0.001319722,0.7739752,0.06713194,0.1144608,0.006255694,0.03279063],"study_design_scores_gemma":[0.002125885,0.0001866732,0.0001528614,0.0000130901,0.000006527822,0.000001506505,0.00003158229,0.9804349,0.01646486,0.0000648702,0.0003625391,0.0001547044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007913019,0.00001822216,0.9966236,0.0004200144,0.0002124949,0.0007252882,0.00001808546,0.0001214755,0.001069546],"genre_scores_gemma":[0.6242395,1.147312e-7,0.3752055,0.0002102414,0.0000436107,0.00006304312,0.00002018897,0.000006892767,0.0002109681],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6234482,"threshold_uncertainty_score":0.5387258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02136146213270044,"score_gpt":0.267081346397038,"score_spread":0.2457198842643375,"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."}}