{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007842283,0.001136325,0.001048822,0.0004132001,0.0006772865,0.0007515223,0.001442145,0.0009534141,0.002712611],"category_scores_gemma":[0.002025044,0.0005696838,0.000726046,0.0003672492,0.000728648,0.001209939,0.001522982,0.0008073455,0.0003626912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005537178,"about_ca_system_score_gemma":0.000999906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004027769,"about_ca_topic_score_gemma":0.003461569,"domain_scores_codex":[0.9994838,0.0001388525,0.00002553355,0.000151883,0.00007175048,0.0001282325],"domain_scores_gemma":[0.9992653,0.0003304046,0.0001473315,0.00008853825,0.00007854624,0.00008993278],"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":[0.00006535622,0.00003006436,0.0005049292,0.00003402868,0.00002116373,0.00008317685,0.00008034943,0.9758354,0.0009675974,0.002383488,0.0004072282,0.01958724],"study_design_scores_gemma":[0.00002164327,0.00008271363,0.0001410581,0.000005042591,0.000008356043,0.00002660356,0.00006801878,0.995249,0.00044286,0.003332307,0.0006175363,0.000004814869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07986403,0.000232617,0.9148931,0.0002255295,0.00006331346,0.00009127003,0.00004507727,0.000345684,0.004239471],"genre_scores_gemma":[0.8865505,0.0001095201,0.1099366,0.00005422909,0.00002623764,0.0001481694,0.00009146854,0.00006248234,0.003020803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004027769,"threshold_uncertainty_score":0.009074569,"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."}}