{"id":"W2730793964","doi":"10.1007/978-3-319-61431-1_15","title":"Improving the Forward Kinematics of Cable-Driven Parallel Robots Through Cable Angle Sensors","year":2017,"lang":"en","type":"book-chapter","venue":"Mechanisms and machine science","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Kinematics; Planar; Fusion; Sensor fusion; Parallel manipulator; Computer science; Closure (psychology); Forward kinematics; Robot; Engineering; Algorithm; Acoustics; Inverse kinematics; Computer vision; Artificial intelligence; Physics; Computer graphics (images)","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":[0.0003126269,0.001206228,0.0005542862,0.0004211251,0.0002169917,0.0006616637,0.001071143,0.0005829925,0.005371111],"category_scores_gemma":[0.00119648,0.0005179718,0.0004505575,0.000659138,0.0004296303,0.001552908,0.001072668,0.001151912,0.002250754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000316082,"about_ca_system_score_gemma":0.0004236854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007303081,"about_ca_topic_score_gemma":0.0009662847,"domain_scores_codex":[0.9995502,0.00004254457,0.00001369357,0.00007977273,0.0002898131,0.00002398197],"domain_scores_gemma":[0.9996288,0.0001075905,0.00003960375,0.00008068956,0.0001301327,0.00001325247],"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":[0.000156589,0.0001099515,0.0005755972,0.0005110531,0.00004563807,0.0001237839,0.0001396838,0.1594679,0.1089187,0.03278638,0.006696,0.6904688],"study_design_scores_gemma":[0.00005664686,0.0005803283,0.001341206,0.0001756768,0.000059848,0.0007260568,0.00009994116,0.7621899,0.1369107,0.03748228,0.06028697,0.00009054507],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01125384,0.0008603242,0.9735323,0.00008181732,0.0001436673,0.00003054428,0.00005599798,0.001283086,0.01275848],"genre_scores_gemma":[0.2675554,0.002629648,0.6969786,0.0001015254,0.0001195263,0.0001014208,0.0003826577,0.0004536632,0.03167758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005371111,"threshold_uncertainty_score":0.01796818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673626492774079,"score_gpt":0.2185726300125405,"score_spread":0.2018363650847997,"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."}}