{"id":"W1991446466","doi":"10.1007/s10846-006-9055-3","title":"A Fuzzy–Braitenberg Navigation Strategy for Differential Drive Mobile Robots","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Obstacle avoidance; Robot; Mobile robot navigation; Fuzzy logic; Scheme (mathematics); Engineering; Computer science; Differential (mechanical device); Artificial intelligence; Robot control; Control engineering; Real-time computing; Mathematics","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":[0.0002826578,0.0005187124,0.0006431625,0.0003042038,0.0005462492,0.0005820221,0.0009877204,0.0006864118,0.001396267],"category_scores_gemma":[0.0003644236,0.0002470427,0.000273513,0.0002712536,0.0004080346,0.0005118657,0.000618957,0.0003818084,0.0002628423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005835635,"about_ca_system_score_gemma":0.0005614078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004794796,"about_ca_topic_score_gemma":0.004539253,"domain_scores_codex":[0.9998685,0.00002002809,0.000009309231,0.00003497018,0.00004393037,0.00002316635],"domain_scores_gemma":[0.9998864,0.00002800603,0.00001244804,0.000008604475,0.00005124986,0.00001336261],"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":[0.000381403,0.0001760729,0.0008124138,0.0003236598,0.00008155168,0.0004512205,0.0003812754,0.6519017,0.05527055,0.05835902,0.00208737,0.2297737],"study_design_scores_gemma":[0.00002522825,0.0001892197,0.0002004221,0.00000948051,0.00001342036,0.00006683334,0.00002100018,0.9902058,0.002744422,0.005205038,0.001303555,0.00001553284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05757006,0.0002964215,0.9345876,0.0001149378,0.00007871426,0.00005008706,0.00002486548,0.0001439441,0.007133296],"genre_scores_gemma":[0.9465153,0.0001141347,0.04839793,0.00004961113,0.00001355598,0.00006855529,0.0000327193,0.00001159284,0.004796497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004794796,"threshold_uncertainty_score":0.009533823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02482876956848496,"score_gpt":0.2769876364824002,"score_spread":0.2521588669139152,"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."}}