{"id":"W4390022972","doi":"10.18280/mmep.100622","title":"Enhanced Obstacle Avoidance and Intelligent Navigation for Mobile Robots: An Integrated Approach Using Fuzzy Logic and an Optimized APF Method","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Obstacle avoidance; Fuzzy logic; Mobile robot; Computer science; Collision avoidance; Artificial intelligence; Robot; Obstacle; Control engineering; Mobile robot navigation; Human–computer interaction; Computer vision; Engineering; Robot control; Computer security; Geography","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":[0.001189886,0.0002454459,0.0003635917,0.0001266435,0.0001464677,0.0002633113,0.0002052904,0.0001192885,2.640608e-7],"category_scores_gemma":[0.00005729097,0.0002163752,0.00002881242,0.0003112175,0.00003705417,0.0004143514,0.00009278107,0.0001892595,9.40418e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002772347,"about_ca_system_score_gemma":0.00001436454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000103513,"about_ca_topic_score_gemma":3.472138e-8,"domain_scores_codex":[0.9984781,0.00006017481,0.0003548347,0.0005736905,0.0001565706,0.0003765939],"domain_scores_gemma":[0.9990891,0.0002962298,0.00006989055,0.0002826572,0.00006652658,0.0001956478],"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":[0.00000432664,0.00004127139,4.76519e-7,0.0004496401,0.0000142325,0.000001250222,0.003088755,0.9812875,0.00224807,0.006497357,3.898946e-7,0.006366696],"study_design_scores_gemma":[0.0002648673,0.0001297704,9.735005e-7,0.0002641437,0.00001770454,0.00003785869,0.0001651455,0.976065,0.000752361,0.02203407,0.000003262179,0.0002647815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03140138,0.000215819,0.9672608,0.00001534641,0.00006456523,0.0005508436,0.000003534823,0.0004783461,0.00000930282],"genre_scores_gemma":[0.07245179,0.00003527893,0.9272557,0.000007722209,0.00002666734,0.0001544643,0.00002029527,0.00003063128,0.00001748216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04105041,"threshold_uncertainty_score":0.8823523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07282447467198265,"score_gpt":0.3095756378501471,"score_spread":0.2367511631781644,"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."}}