{"id":"W2069590011","doi":"10.1109/isie.2006.296067","title":"Development of a Robot with an Intelligent Capability to Keep and Select a Path","year":2006,"lang":"en","type":"article","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Mobile robot; Robot; Computer science; Knowledge base; Robot control; Social robot; Mobile robot navigation; Robot kinematics; Knowledge-based systems; Personal robot; Artificial intelligence; Obstacle avoidance; Obstacle; Collision avoidance; Human–computer interaction; Collision; Computer security","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":[0.0001778028,0.0003159013,0.0002571332,0.0002321506,0.0002739767,0.0003897335,0.0007439393,0.0005900285,0.001992314],"category_scores_gemma":[0.0002613886,0.0003049608,0.000364681,0.00009627192,0.0003718334,0.0005153475,0.0004876667,0.0005477848,0.001093949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001644866,"about_ca_system_score_gemma":0.0008187765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001243143,"about_ca_topic_score_gemma":0.0008990394,"domain_scores_codex":[0.9998945,0.000008911595,0.0000062636,0.00002824775,0.00004860647,0.00001343182],"domain_scores_gemma":[0.9999059,0.00001512012,0.000009201294,0.00001473537,0.00003813863,0.00001692811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001157299,0.0002531005,0.002191293,0.0006145353,0.0000669206,0.000983687,0.0006249294,0.08492217,0.4611751,0.06583138,0.00558506,0.3776362],"study_design_scores_gemma":[0.0001268652,0.001260725,0.003585866,0.0002641243,0.00013385,0.002737864,0.0002487523,0.5160276,0.2576992,0.01354332,0.204237,0.0001348231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02841836,0.000334879,0.9577817,0.0002313058,0.00007306277,0.0001869212,0.00006040036,0.002244031,0.01066924],"genre_scores_gemma":[0.1317146,0.0004717437,0.8582667,0.00008395593,0.00001628311,0.0001945919,0.0001853391,0.00008338031,0.008983527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001992314,"threshold_uncertainty_score":0.006664932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109098324558756,"score_gpt":0.2066556488474785,"score_spread":0.195564665601891,"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."}}