{"id":"W3202915137","doi":"10.1109/rtcsa52859.2021.00028","title":"Optimal Recharging of Teams of Mobile Robots","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ford Motor Company (Canada)","funders":"","keywords":"Robot; Computer science; Mobile robot; Energy consumption; A priori and a posteriori; Motion planning; Task (project management); Real-time computing; Set (abstract data type); Path (computing); Distributed computing; Artificial intelligence; Computer network; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002212932,0.00006767671,0.0001883472,0.00005220076,0.00002008258,0.00001495552,0.0004192375,0.00003783011,0.00003737439],"category_scores_gemma":[0.00007203578,0.00006295709,0.00005178677,0.0003602401,0.00002572666,0.0001459693,0.0002246118,0.00006743609,0.00001244137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001320536,"about_ca_system_score_gemma":0.0001182645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002432485,"about_ca_topic_score_gemma":2.376271e-7,"domain_scores_codex":[0.9991342,0.00004087516,0.0002422896,0.0002160583,0.0002131139,0.0001534537],"domain_scores_gemma":[0.9991331,0.00009436024,0.0001323797,0.0004664926,0.0001303319,0.00004338057],"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":[0.000004707938,0.000381139,0.003606733,0.0001214447,0.00008161375,0.0001844823,0.003426075,0.795804,0.09482289,0.02111252,0.001839171,0.07861517],"study_design_scores_gemma":[0.0002494791,0.0001058494,0.001192016,0.00008177674,0.000005455096,0.00006002775,0.0002213155,0.6480132,0.3495816,0.0002109867,0.0001388588,0.0001393437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0315374,0.0001715969,0.9621179,0.00008606636,0.0002259826,0.0000519565,0.000001176023,0.00006587667,0.005742034],"genre_scores_gemma":[0.152067,0.000005529381,0.8469303,0.00002880189,0.00001493773,0.000003620092,0.000001207686,0.000003993417,0.0009445694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2547587,"threshold_uncertainty_score":0.2567316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368879848595081,"score_gpt":0.2586969841806994,"score_spread":0.2450081856947486,"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."}}