{"id":"W2102499212","doi":"10.1109/tmech.2003.816818","title":"A behavior-based mobile robot with a visual landmark-recognition system","year":2003,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Landmark; Mobile robot; Artificial intelligence; Obstacle avoidance; Computer vision; Computer science; Mobile robot navigation; Robot; Fuzzy logic; Robot control","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004164864,0.0003492678,0.0003195413,0.000298458,0.0003603199,0.0001675918,0.0004338199,0.0001787015,0.00003161522],"category_scores_gemma":[0.0000045305,0.0003199329,0.0001471874,0.0006760143,0.00004426595,0.0003720968,0.00000254913,0.0004755938,0.0002590345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003579392,"about_ca_system_score_gemma":0.0003673985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003280585,"about_ca_topic_score_gemma":0.00001301177,"domain_scores_codex":[0.9975683,0.0002332953,0.0003472131,0.0006714372,0.0005892482,0.0005905018],"domain_scores_gemma":[0.9986935,0.0001361577,0.0001427623,0.0006680289,0.0001444277,0.0002151422],"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":[0.000132138,0.001740858,0.00007065529,0.000136055,0.0001435592,0.0002385487,0.0004532972,0.9255078,0.001665654,0.001392439,0.00004173764,0.06847724],"study_design_scores_gemma":[0.004695568,0.004688925,0.00008200757,0.0005096989,0.0003487413,0.0005736707,0.0005662914,0.9203075,0.0658846,0.0001030544,0.000754273,0.001485696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02045172,0.00004766822,0.9769365,0.00003714166,0.0008486735,0.0006810321,0.00003362683,0.0007150745,0.0002485558],"genre_scores_gemma":[0.7680455,0.000004609751,0.2311607,0.00006695464,0.00002399283,0.0005649169,0.00001160449,0.00003948876,0.0000821928],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7475938,"threshold_uncertainty_score":0.9999253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01523731774162473,"score_gpt":0.2398786505395749,"score_spread":0.2246413327979502,"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."}}