{"id":"W4390481218","doi":"10.3390/biomimetics9010016","title":"Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models","year":2024,"lang":"en","type":"article","venue":"Biomimetics","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Swarm robotics; Swarm behaviour; Foraging; Artificial intelligence; Swarm intelligence; Robot; Ant robotics; Robustness (evolution); Adaptability; Flocking (texture); Computer science; Flexibility (engineering); Robotics; Collective behavior; Artificial neural network; Fish <Actinopterygii>; Machine learning; Mobile robot; Ecology; Robot control; Particle swarm optimization; Biology; Fishery; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001289568,0.0003471889,0.0004904442,0.000301593,0.0003600689,0.0005401923,0.0005822295,0.0005438018,0.0005878051],"category_scores_gemma":[0.0004117837,0.0002868882,0.0005030255,0.0002122388,0.000707432,0.0006848025,0.0006937952,0.000338275,0.0001292618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004546662,"about_ca_system_score_gemma":0.0004734455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002728386,"about_ca_topic_score_gemma":0.001937264,"domain_scores_codex":[0.999947,0.00001469356,0.000002540863,0.000009058918,0.00002048058,0.000006269307],"domain_scores_gemma":[0.9999365,0.0000224117,0.00001387555,0.000006484133,0.00001102944,0.000009730996],"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":[0.00001676676,0.00002078319,0.0008976143,0.00005448727,0.00004258152,0.0001748003,0.0001660983,0.9234559,0.005685202,0.05751822,0.0006532731,0.01131424],"study_design_scores_gemma":[0.000003182726,0.000007466397,0.00009572723,0.000002717477,0.000003564777,0.00001941542,0.00000960848,0.9937688,0.000137021,0.00531584,0.0006319445,0.000004803794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.109343,0.001206994,0.8701249,0.0006287866,0.0001271732,0.00006796485,0.00004592754,0.0001829931,0.01827224],"genre_scores_gemma":[0.9330741,0.001083289,0.05821637,0.00008585499,0.00003816538,0.0001643353,0.00003886109,0.00005835498,0.007240642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002728386,"threshold_uncertainty_score":0.005425036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208969964440991,"score_gpt":0.2224976008317465,"score_spread":0.2016006043876474,"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."}}