{"id":"W3126409450","doi":"10.1109/cac51589.2020.9326562","title":"UAV Trajectory Generation Based on Integration of RRT and Minimum Snap Algorithms","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Path (computing); Smoothness; Obstacle; Motion planning; Algorithm; Limit (mathematics); Obstacle avoidance; Simulation; Control theory (sociology); Real-time computing; Robot; Mobile robot; Artificial intelligence; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005237153,0.0005334276,0.0006946768,0.0006024409,0.0005216699,0.0004140433,0.0007669634,0.0004143742,0.0007969467],"category_scores_gemma":[0.00167772,0.0002995092,0.0005385289,0.0004874027,0.000419283,0.0006971094,0.0009062461,0.0004884376,0.0001401047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005098918,"about_ca_system_score_gemma":0.0009995206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004778244,"about_ca_topic_score_gemma":0.003484605,"domain_scores_codex":[0.999709,0.00006793848,0.00001871033,0.00007030624,0.00009947349,0.00003451173],"domain_scores_gemma":[0.999516,0.0001814417,0.00006706235,0.00006173432,0.0001357273,0.00003800158],"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":[0.00009893747,0.00002827829,0.0011041,0.00004796199,0.00003175504,0.0001217552,0.00009216641,0.8918418,0.005126266,0.009675981,0.0006216796,0.0912093],"study_design_scores_gemma":[0.000006314335,0.00003278154,0.00008122568,0.000002952951,0.000004776635,0.00002469207,0.000006454262,0.9977705,0.0007085816,0.001017396,0.0003403808,0.000003889169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01855843,0.00009053751,0.9798258,0.00004282525,0.00002214603,0.00004017562,0.00001598881,0.0003990057,0.001005027],"genre_scores_gemma":[0.5942743,0.0001528647,0.4038409,0.00004485578,0.0000190663,0.0001634604,0.0001356217,0.00008964642,0.001279298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004778244,"threshold_uncertainty_score":0.009500861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05667442753150732,"score_gpt":0.2600883755990687,"score_spread":0.2034139480675614,"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."}}