{"id":"W2795190293","doi":"10.1109/tmech.2018.2821600","title":"Dynamic Path Tracking of Industrial Robots With High Accuracy Using Photogrammetry Sensor","year":2018,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Robot; Computer science; Artificial intelligence; Photogrammetry; Industrial robot; Kalman filter; Path (computing); Motion planning; Tracking (education); Task (project management); Visual servoing; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002325115,0.000277253,0.000310233,0.0003163595,0.0003468047,0.0001133779,0.0005611842,0.0001369414,0.00004732105],"category_scores_gemma":[0.00001849666,0.0002479619,0.0001279616,0.0009252465,0.0001259105,0.0008599206,0.00001074017,0.0005300738,0.00001996358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001848357,"about_ca_system_score_gemma":0.0002321012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001216774,"about_ca_topic_score_gemma":0.00004776139,"domain_scores_codex":[0.9979988,0.00008779172,0.000399377,0.0005404913,0.0004739386,0.0004996067],"domain_scores_gemma":[0.9985138,0.0001505429,0.000255798,0.000727749,0.0002144473,0.0001376214],"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":[0.0002336122,0.0005321995,0.00003417508,0.00002999719,0.0001361599,0.00002051438,0.0007161005,0.09072283,0.06347933,0.001242605,0.00001504919,0.8428375],"study_design_scores_gemma":[0.001899977,0.0007489315,0.00001892092,0.0002435888,0.00006114384,0.00006430097,0.0002543987,0.8599862,0.1352427,0.0006799786,0.0003448963,0.0004549448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0767807,0.00002706687,0.9216325,0.0001421703,0.0009260214,0.0002607241,0.00001711611,0.0001816267,0.00003202413],"genre_scores_gemma":[0.7167495,0.00002262102,0.283032,0.00009094352,0.00004456857,0.000005327558,0.000001376412,0.00002739643,0.00002630829],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8423825,"threshold_uncertainty_score":0.9999973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03359213438251555,"score_gpt":0.2932844112745729,"score_spread":0.2596922768920573,"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."}}