{"id":"W2099605854","doi":"10.1109/iros.2006.282504","title":"Vision based trajectory tracking controller for a B21R mobile robot","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Trajectory; Mobile robot; Computer science; Tracking (education); Sonar; Computer vision; Obstacle; Artificial intelligence; Controller (irrigation); Monocular vision; Robot; Obstacle avoidance; Robot control; Tracking system; Control theory (sociology); Control (management); Kalman filter; Geography","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":[0.0003956786,0.0001570432,0.000222096,0.00011477,0.0001381354,0.0001678583,0.0005006133,0.00007683823,0.00001452846],"category_scores_gemma":[0.00002963962,0.0001324974,0.0001267109,0.0002002212,0.00002528187,0.0003067067,0.00003461108,0.00008370889,0.00003179445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004665136,"about_ca_system_score_gemma":0.00006313442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006847056,"about_ca_topic_score_gemma":0.000004601053,"domain_scores_codex":[0.9986362,0.00004804244,0.0002714159,0.0004251163,0.000251221,0.0003680109],"domain_scores_gemma":[0.9990208,0.0003972702,0.00008109483,0.000341699,0.00009339126,0.00006581644],"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":[0.00004528082,0.0005147006,0.0007151237,0.00004221022,0.00002494475,0.00003404335,0.0002062305,0.8415361,0.01975006,0.005528863,0.0153553,0.1162472],"study_design_scores_gemma":[0.001535464,0.0002257539,0.003733298,0.00002134941,0.00000616821,0.000005217583,0.000007729236,0.9890359,0.003130932,0.0005083755,0.001593318,0.0001964727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001148576,0.0001076462,0.9957067,0.0002374416,0.0003370352,0.0005291126,0.000002917839,0.0004022034,0.001528312],"genre_scores_gemma":[0.4241216,2.148224e-7,0.5745113,0.0003162007,0.0001252309,0.0001156425,0.000005120088,0.00001264201,0.0007921222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.422973,"threshold_uncertainty_score":0.5403089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379128543385524,"score_gpt":0.2654498928267752,"score_spread":0.25165860739292,"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."}}