{"id":"W1881619528","doi":"10.1007/978-3-642-21538-4_3","title":"A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Real-time computing; Navigation system; RSS; Sensor fusion; Mobile robot; Mobile robot navigation; Robot; Encoder; Computer vision; Artificial intelligence; Embedded system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000286023,0.0004895306,0.000624334,0.0006182854,0.000572132,0.001002789,0.001230044,0.0007735799,0.002476816],"category_scores_gemma":[0.0005144942,0.0002456274,0.0009486367,0.0006459976,0.0003854427,0.000750374,0.0004263389,0.0005889991,0.0005586285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009264174,"about_ca_system_score_gemma":0.0006755707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01030676,"about_ca_topic_score_gemma":0.01104415,"domain_scores_codex":[0.9997281,0.0000366616,0.00001877774,0.00006924689,0.0001170539,0.00003003079],"domain_scores_gemma":[0.9998854,0.00003583547,0.00001070384,0.00000967378,0.00005278516,0.00000547396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001238398,0.0001534984,0.0006918039,0.0003710616,0.0001050949,0.0006466895,0.000386722,0.4052599,0.02882049,0.1482627,0.003675069,0.4115032],"study_design_scores_gemma":[0.00001092854,0.00007822362,0.0002400796,0.00003673782,0.00004375241,0.0001905816,0.00006868596,0.9614252,0.003080397,0.03027972,0.004515103,0.00003060443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003027002,0.0002385123,0.9921799,0.00005287488,0.00004916731,0.00001922544,0.00002446436,0.00009062849,0.004318309],"genre_scores_gemma":[0.3519006,0.0008935946,0.6337802,0.0001843981,0.0001159629,0.0001473594,0.0001248283,0.00003705889,0.01281596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01030676,"threshold_uncertainty_score":0.02049357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02679784763739915,"score_gpt":0.2437392841792283,"score_spread":0.2169414365418291,"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."}}