{"id":"W2128044034","doi":"10.1017/s0263574713000714","title":"Comparison of two calibration methods for a small industrial robot based on an optical CMM and a laser tracker","year":2013,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Laser tracker; Calibration; Industrial robot; Linearization; Jacobian matrix and determinant; Robot calibration; Robot; Laser; Coordinate-measuring machine; Computer science; Kinematics; Accuracy and precision; Computer vision; Artificial intelligence; Engineering; Optics; Mathematics; Robot kinematics; Physics; Mechanical engineering; Nonlinear system; Mobile robot","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001769068,0.0006728593,0.0004134481,0.0009939901,0.0003632471,0.0004908639,0.0008729292,0.000769613,0.002041979],"category_scores_gemma":[0.004214996,0.0003728797,0.0003618223,0.0006251381,0.000425362,0.0005467453,0.0006604182,0.0003513319,0.0005326142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004847943,"about_ca_system_score_gemma":0.0006997035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001459544,"about_ca_topic_score_gemma":0.001489139,"domain_scores_codex":[0.9987847,0.0002645742,0.00004716215,0.0001856107,0.000656706,0.00006121918],"domain_scores_gemma":[0.9978729,0.0007985068,0.0002691596,0.0003798518,0.0006282848,0.00005114349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001610399,0.0004054781,0.01041788,0.0008804892,0.0001637707,0.0001806709,0.0005528306,0.1289617,0.3021266,0.004520497,0.002245639,0.5479341],"study_design_scores_gemma":[0.0002539788,0.002055744,0.03103163,0.0001540896,0.0001875979,0.0008537113,0.0002274746,0.6804911,0.2689079,0.00137522,0.01426832,0.0001932047],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1694812,0.0007063426,0.823061,0.0001497202,0.0001213674,0.0001403745,0.00007116283,0.002436826,0.003831975],"genre_scores_gemma":[0.8164185,0.0001701218,0.1813055,0.00005415952,0.00001573981,0.0001112483,0.00009847754,0.0001282666,0.001698018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002041979,"threshold_uncertainty_score":0.009355843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06982094760631988,"score_gpt":0.3464212520832728,"score_spread":0.2766003044769529,"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."}}