{"id":"W2133118325","doi":"10.1109/tsmcc.2007.897499","title":"Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Fuzzy logic; Mobile robot; Trajectory; Artificial intelligence; Obstacle avoidance; Robot; Artificial neural network; State (computer science); Control engineering; Control theory (sociology); Control (management); Engineering; 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":[0.0006610413,0.0001995865,0.0002881932,0.0001760494,0.0001810078,0.0001019466,0.0002453288,0.00008864426,8.754702e-7],"category_scores_gemma":[0.000001518996,0.000181368,0.00004433106,0.0004603931,0.00005462434,0.00008955817,0.000005039465,0.0001867269,0.00005116225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004670826,"about_ca_system_score_gemma":0.00001528638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002617755,"about_ca_topic_score_gemma":0.000002986795,"domain_scores_codex":[0.9983727,0.0001028412,0.0005247541,0.0005397464,0.0002001072,0.0002598214],"domain_scores_gemma":[0.9990414,0.00007637417,0.000119029,0.0005361789,0.00002019001,0.0002068731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002063782,0.001643089,0.0002636712,0.0006766677,0.0000408378,0.0000113183,0.001352463,0.3939812,0.002275752,0.007326975,0.0003994787,0.5920079],"study_design_scores_gemma":[0.001304413,0.0005116048,0.001970753,0.0008837086,0.00008225322,0.000132074,0.0001772795,0.3159897,0.001421244,0.0001039874,0.6763421,0.001080792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001308917,0.001663535,0.9941404,0.00005765559,0.0001695617,0.002035247,0.000006949829,0.00004704722,0.0005706586],"genre_scores_gemma":[0.9051428,0.002148526,0.08598121,0.0003943244,0.0001010949,0.004509718,0.00001997703,0.00003919825,0.001663185],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9081592,"threshold_uncertainty_score":0.7395973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259140461650461,"score_gpt":0.2612784282424843,"score_spread":0.2386870236259797,"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."}}