{"id":"W2960879114","doi":"10.48550/arxiv.1905.00495","title":"A Modular Framework for Motion Planning using Safe-by-Design Motion Primitives","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Workspace; Modular design; Motion planning; Motion (physics); Computer science; Modularity (biology); Robot; Set (abstract data type); Automaton; Motion control; Motion capture; Finite-state machine; Distributed computing; Artificial intelligence; Programming language","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":[0.001569273,0.001339372,0.0007084021,0.0007998919,0.0007678987,0.001054959,0.001988675,0.001047959,0.003479712],"category_scores_gemma":[0.002164206,0.0007076731,0.001413721,0.000506849,0.0026758,0.001459924,0.002295261,0.002120071,0.001043445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007941078,"about_ca_system_score_gemma":0.001683337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001046773,"about_ca_topic_score_gemma":0.001620651,"domain_scores_codex":[0.9989848,0.0002609405,0.00007832472,0.000223292,0.0003533702,0.00009934021],"domain_scores_gemma":[0.9992223,0.0002340058,0.0001075905,0.0002923464,0.00009253746,0.00005129838],"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":[0.00008066275,0.00008989848,0.0005031407,0.0003782561,0.00004302608,0.0002565543,0.000362153,0.4914568,0.02923423,0.3735768,0.001827055,0.1021914],"study_design_scores_gemma":[0.00006688025,0.0002638893,0.0001705549,0.0001031439,0.00004015209,0.000199759,0.00005565934,0.7843771,0.01423953,0.1768231,0.02361795,0.00004223365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008215435,0.0000260031,0.9982746,0.00002417041,0.000005112668,0.00004066206,0.00001514699,0.0002628086,0.0005299647],"genre_scores_gemma":[0.05875328,0.0001018126,0.9396843,0.00003461388,0.00001272422,0.0003273423,0.00009135935,0.000123616,0.0008708433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003479712,"threshold_uncertainty_score":0.01164085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1565249171591528,"score_gpt":0.2387796409666337,"score_spread":0.08225472380748083,"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."}}