{"id":"W4404056574","doi":"10.1109/tase.2024.3486040","title":"Motion Planners for Path or Waypoint Following and End-Effector Sway Damping With Dynamic Programming","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Waypoint; Path (computing); Robot end effector; Computer science; Motion planning; Motion (physics); Simulation; Control theory (sociology); Engineering; Robot; Real-time computing; Artificial intelligence; Control (management)","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":[],"consensus_categories":[],"category_scores_codex":[0.0004421661,0.0001516308,0.000138426,0.00031,0.0001795288,0.0002653096,0.00005619038,0.00004511762,0.000003086589],"category_scores_gemma":[0.00001227926,0.0001186793,0.00003123019,0.0004887808,0.00003494336,0.0005493614,7.660125e-7,0.00009760085,0.000003332858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001209131,"about_ca_system_score_gemma":0.00003510328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006834974,"about_ca_topic_score_gemma":0.000006992801,"domain_scores_codex":[0.9991081,0.000005393904,0.0001771245,0.0002291824,0.0002366284,0.0002435951],"domain_scores_gemma":[0.999644,0.0001387083,0.00001260692,0.00009209686,0.00002197868,0.00009062587],"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":[0.000008189022,0.00001137849,0.000005615007,0.001187378,0.00006741927,0.000008416452,0.002728421,0.689715,0.03104889,0.00005514415,0.000008170516,0.275156],"study_design_scores_gemma":[0.000201941,0.00007650765,0.0001687271,0.0005905106,0.00002566984,0.00004759105,0.0002138832,0.9954863,0.002667599,0.000002383109,0.0003518632,0.0001670462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2657612,0.00007846665,0.7325879,0.0000297319,0.000582482,0.0003126923,0.000006348717,0.0006200725,0.00002102597],"genre_scores_gemma":[0.9937869,0.00001991047,0.005956409,0.000006320895,0.00002032006,0.0001633079,0.000002112897,0.00002776195,0.00001691845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7280257,"threshold_uncertainty_score":0.48396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009603150924573568,"score_gpt":0.2349859739523452,"score_spread":0.2253828230277717,"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."}}