{"id":"W2128247685","doi":"10.1109/icsmc.1995.537808","title":"Robot path planning using genetic algorithms","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion planning; Computer science; Genetic algorithm; Path (computing); Robot; Artificial intelligence; Algorithm; Machine learning; Computer network","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":[0.0004148415,0.0006647006,0.000568751,0.0007121144,0.0004164034,0.000790865,0.0008035015,0.0007968757,0.001883248],"category_scores_gemma":[0.001024766,0.0003221615,0.0005117815,0.0009022085,0.0007552548,0.0006009784,0.0006158553,0.0006452758,0.0007210989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005104447,"about_ca_system_score_gemma":0.0008537421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003843303,"about_ca_topic_score_gemma":0.003343919,"domain_scores_codex":[0.9997249,0.00007752603,0.00001283188,0.00005151474,0.0001108478,0.00002232564],"domain_scores_gemma":[0.9997962,0.0001035849,0.0000233326,0.00002610781,0.00004226335,0.000008416521],"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":[0.00002517927,0.00002509867,0.0002926723,0.0001058663,0.00004377645,0.00008573727,0.00009283848,0.8045851,0.004087011,0.0362058,0.001536867,0.1529141],"study_design_scores_gemma":[0.00002865372,0.00005660332,0.0001610318,0.00005079292,0.00002700567,0.00009550768,0.00002928728,0.9352697,0.002876623,0.04484276,0.01653749,0.00002460429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004565902,0.0006198573,0.9879374,0.0001138375,0.00003126784,0.00005039065,0.00003290143,0.0007626005,0.005885865],"genre_scores_gemma":[0.1456559,0.001774727,0.8475071,0.0001058536,0.00003679392,0.0002381414,0.0001569737,0.0001622765,0.004362207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003843303,"threshold_uncertainty_score":0.007641852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07233880266214743,"score_gpt":0.2767000770325934,"score_spread":0.204361274370446,"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."}}