{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004259199,0.0006871975,0.0007385232,0.0006133485,0.0004110487,0.0004401699,0.0008604082,0.0008928095,0.001109598],"category_scores_gemma":[0.0007932025,0.0002885806,0.0007007733,0.0005393806,0.0004016973,0.0007999444,0.0006706201,0.0007931991,0.0002498261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000305434,"about_ca_system_score_gemma":0.0005735885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002020567,"about_ca_topic_score_gemma":0.001354927,"domain_scores_codex":[0.9997935,0.00006636423,0.00001159198,0.0000353562,0.00007711919,0.00001614549],"domain_scores_gemma":[0.9998391,0.00006828713,0.00001695871,0.00001324859,0.00004777797,0.00001460695],"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":[0.00004542241,0.000061307,0.0006418795,0.0001665979,0.00008167453,0.0001522101,0.00009642849,0.8770509,0.007734457,0.02251527,0.001545546,0.08990833],"study_design_scores_gemma":[0.000008133928,0.00002498773,0.00007434313,0.000004244025,0.000005848556,0.00002723365,0.00000556786,0.9970239,0.0002906807,0.001796465,0.0007329718,0.000005672714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003612343,0.0003851115,0.9936996,0.0000801189,0.00005912676,0.00002873365,0.000007527698,0.00006446164,0.002062987],"genre_scores_gemma":[0.4785868,0.001248339,0.5138917,0.0001833092,0.0001551332,0.0004283566,0.00009285447,0.00007053669,0.005342873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002020567,"threshold_uncertainty_score":0.004017532,"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."}}