{"id":"W4322616123","doi":"10.48550/arxiv.2302.12309","title":"Safe and Quasi-Optimal Autonomous Navigation in Sphere Worlds","year":2023,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle; Point (geometry); Computer science; Control theory (sociology); Path (computing); Equilibrium point; Euclidean space; Obstacle avoidance; Space (punctuation); Line segment; Control (management); Exponential stability; Line (geometry); Sight; Line-of-sight; Mathematics; Artificial intelligence; Aerospace engineering; Mobile robot; Engineering; Mathematical analysis; Geography; Geometry; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002957915,0.0004814918,0.0003136649,0.0003375528,0.000325523,0.0006792436,0.0005151991,0.0004586015,0.0007993463],"category_scores_gemma":[0.001004237,0.0002589177,0.0002678582,0.000201558,0.001199996,0.0008247013,0.001073869,0.0004868474,0.0001793056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004606656,"about_ca_system_score_gemma":0.0005970309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003801985,"about_ca_topic_score_gemma":0.002862613,"domain_scores_codex":[0.9997562,0.00006996215,0.000008881813,0.00003497979,0.00009123827,0.00003868676],"domain_scores_gemma":[0.999683,0.0001250481,0.00006099643,0.00003056088,0.00006058435,0.00003985218],"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":[0.0001208995,0.00004193797,0.0004941329,0.0000465005,0.00002316788,0.000181379,0.0001975916,0.8850888,0.009943209,0.07398702,0.0007071352,0.02916817],"study_design_scores_gemma":[0.000009911118,0.00005323779,0.00009572992,0.000002782759,0.000002719951,0.00002351968,0.00002196396,0.9795115,0.0008588502,0.01897309,0.0004415086,0.000005280951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05861957,0.0001456235,0.9382057,0.0001301145,0.00002289657,0.00001465302,0.00001771107,0.0002014877,0.002642299],"genre_scores_gemma":[0.9422583,0.0001572243,0.05584329,0.00004018108,0.00001579861,0.00004620534,0.00004083241,0.00002901754,0.001569025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003801985,"threshold_uncertainty_score":0.007559657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07328689393446554,"score_gpt":0.2062281435606758,"score_spread":0.1329412496262102,"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."}}