{"id":"W3146841301","doi":"10.1109/lra.2021.3068554","title":"A Gravity-Referenced Moving Frame for Vehicle Path Following Applications in 3D","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Reference frame; Frame (networking); Trajectory; Path (computing); Curvature; Moving frame; Gravitational field; Motion planning; Computer vision; Control theory (sociology); Artificial intelligence; Mathematics; Control (management); Physics; Geometry; Classical mechanics; Robot","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":[0.0002933949,0.0005693937,0.0003136452,0.0006278043,0.0004349026,0.0005705949,0.0007420042,0.0005360459,0.002283912],"category_scores_gemma":[0.0007071634,0.0001827668,0.0003891458,0.0008730877,0.0005549869,0.0006278953,0.0008080878,0.0006822892,0.001165799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004563485,"about_ca_system_score_gemma":0.0006275169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003729514,"about_ca_topic_score_gemma":0.004657289,"domain_scores_codex":[0.9998159,0.00004162188,0.00000911068,0.00003623936,0.00008440532,0.00001279958],"domain_scores_gemma":[0.9998344,0.00002152984,0.00002583854,0.00003482249,0.00007044084,0.00001284263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009483856,0.00004299263,0.001004038,0.0001871193,0.00002823045,0.0003014788,0.0004176204,0.2648354,0.04785226,0.3399769,0.008422236,0.336837],"study_design_scores_gemma":[0.00001916221,0.0001757897,0.0005930361,0.00005681986,0.00001868722,0.0001980042,0.00009657801,0.8878165,0.01195448,0.0295424,0.06948845,0.00004004851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002271923,0.00008631921,0.9960452,0.00002845471,0.00004079862,0.00001976857,0.00004331533,0.0001359931,0.001328258],"genre_scores_gemma":[0.1941743,0.0008301043,0.8001753,0.00007056863,0.0001123662,0.0002386031,0.0004187216,0.0001455972,0.003834465],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003729514,"threshold_uncertainty_score":0.007640421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0163750319681119,"score_gpt":0.2579588083586978,"score_spread":0.2415837763905859,"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."}}