{"id":"W4286973027","doi":"10.48550/arxiv.2109.08185","title":"Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning","year":2021,"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":"","funders":"Mitacs","keywords":"Heuristics; Computer science; Path (computing); Motion planning; Mathematical optimization; Regular polygon; Line (geometry); Robot; Set (abstract data type); Line segment; Time complexity; Quality (philosophy); Algorithm; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002695192,0.0003121911,0.0004949688,0.0001833496,0.0001465372,0.0001124588,0.001279217,0.0002828944,0.0000122923],"category_scores_gemma":[0.00005009737,0.0004045097,0.000255364,0.000249415,0.0000936561,0.0003757144,0.001402306,0.0004095447,0.00001183351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001493018,"about_ca_system_score_gemma":0.0001944525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002692434,"about_ca_topic_score_gemma":4.191756e-7,"domain_scores_codex":[0.9979843,0.00008521134,0.0003059665,0.001062852,0.0001400181,0.0004216543],"domain_scores_gemma":[0.998142,0.0002199633,0.0004531642,0.0009658734,0.00006979304,0.0001492085],"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":[0.00001707794,0.00008149853,0.003422791,0.0001005863,0.0001273037,0.0004004768,0.0008139217,0.9937833,0.0003727865,0.0006109677,0.0001143233,0.0001549827],"study_design_scores_gemma":[0.0007239092,0.0000880015,0.004155818,0.0003600325,0.00007487875,0.000008011412,0.0001542657,0.9915182,0.001715747,0.0006937808,0.00009435201,0.000412975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2947885,0.0000537753,0.7040211,0.00001514898,0.0005278566,0.0002310889,0.00002592544,0.00004890527,0.0002877043],"genre_scores_gemma":[0.9118577,0.00002678839,0.08734487,0.00004378386,0.00006628582,0.000003290615,0.00007418869,0.00002023262,0.0005628561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6170692,"threshold_uncertainty_score":0.9998407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06343477976045878,"score_gpt":0.2063550469856692,"score_spread":0.1429202672252105,"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."}}