{"id":"W2799553016","doi":"10.1139/tcsme-2011-0030","title":"LENGTH-OPTIMIZED SMOOTH OBSTACLE AVOIDANCE FOR ROBOTIC MANIPULATORS","year":2011,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle avoidance; Acceleration; Control theory (sociology); Obstacle; Path (computing); Displacement (psychology); Motion planning; Computer science; Trajectory; Collision avoidance; Mathematics; Robot; Mobile robot; Artificial intelligence; Collision; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001663651,0.0001977048,0.0002723123,0.00004608913,0.000195245,0.00001513004,0.0003190813,0.0001979939,0.00003966275],"category_scores_gemma":[0.00002425988,0.0001891687,0.0007164771,0.0001854642,0.00002251169,0.00008965519,0.000004356963,0.0002063629,0.000001034483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002835834,"about_ca_system_score_gemma":0.00008019676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001285654,"about_ca_topic_score_gemma":0.0041153,"domain_scores_codex":[0.9989612,0.000004059237,0.0003017136,0.0001728441,0.0001068916,0.0004532985],"domain_scores_gemma":[0.9992707,0.00007930284,0.00003360694,0.0003282302,0.00005388618,0.000234207],"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":[0.000005061932,0.00001024436,2.923267e-7,0.0001570333,0.0001432512,1.246724e-7,0.0001935523,0.9772835,0.001564619,0.02012081,0.00007315057,0.0004483794],"study_design_scores_gemma":[0.0004860399,0.00004020277,0.00001236698,0.00004081643,0.0001323983,0.000002909109,0.00007449539,0.9913498,0.005965508,0.001426983,0.0002441749,0.0002243265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008622942,0.00006958474,0.9970439,0.00006437668,0.0010625,0.000627897,0.0000708982,0.000148748,0.00004981576],"genre_scores_gemma":[0.539539,0.00001692394,0.4600028,0.00004638606,0.00003630425,0.0001475196,0.000005281084,0.00008721816,0.0001185966],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5386767,"threshold_uncertainty_score":0.7714077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114127218439979,"score_gpt":0.1902585225178186,"score_spread":0.1691172503334188,"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."}}