{"id":"W2401571671","doi":"10.2316/journal.206.2015.3.206-4230","title":"POSITION-BASED VISUAL SERVOING IN ROBOTIC CAPTURE OF MOVING TARGET ENHANCED BY KALMAN FILTER","year":2015,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual servoing; Kalman filter; Computer vision; Artificial intelligence; Computer science; Position (finance); Extended Kalman filter; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000252703,0.0002887589,0.0003453699,0.0001871455,0.0001904954,0.000234944,0.000433175,0.0004114061,0.0005613493],"category_scores_gemma":[0.000577271,0.0002134884,0.0002955615,0.0002304346,0.0002195316,0.0004744009,0.0003499253,0.0003902618,0.0002051297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002599382,"about_ca_system_score_gemma":0.000385071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005151591,"about_ca_topic_score_gemma":0.004258356,"domain_scores_codex":[0.999843,0.00001920194,0.000009831141,0.00003717388,0.00007059666,0.00002022497],"domain_scores_gemma":[0.9998166,0.00007399386,0.00002671607,0.00001572198,0.00005939317,0.000007650911],"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":[0.0002972442,0.00007788541,0.00192519,0.0003628596,0.00009517281,0.0002227601,0.0003027025,0.34647,0.2198376,0.008746587,0.001208261,0.4204538],"study_design_scores_gemma":[0.00001383046,0.0001416882,0.001317488,0.00001113781,0.0000227314,0.00009590376,0.00001355302,0.9772558,0.01893276,0.0008490653,0.001329076,0.00001691863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01496402,0.000318835,0.9834846,0.00002634614,0.00002266188,0.00001282147,0.000007473857,0.0002379827,0.0009253336],"genre_scores_gemma":[0.8616265,0.0005788173,0.1346164,0.00004887247,0.0000384844,0.00005726197,0.00004901259,0.00003986569,0.002944734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005151591,"threshold_uncertainty_score":0.01024324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170792619163774,"score_gpt":0.2672259480799232,"score_spread":0.2555180218882855,"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."}}