{"id":"W4385451899","doi":"10.23919/ecc57647.2023.10178280","title":"Optimal Target Capture and Station Keeping Control of Mobile Agents without Global Position Information","year":2023,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Beacon; Mobile robot; Position (finance); Optimal control; Convergence (economics); Motion control; Motion planning; Global Positioning System; Control theory (sociology); Mathematical optimization; Real-time computing; Control (management); Artificial intelligence; Mathematics; Robot; Telecommunications","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":[0.000546855,0.0005943184,0.0004497298,0.0002962761,0.0003712612,0.0006501616,0.0007059012,0.0006314628,0.000648677],"category_scores_gemma":[0.001388116,0.0003016692,0.0004509393,0.000224608,0.0009596431,0.0006577505,0.001041699,0.0005873907,0.00009496704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005789497,"about_ca_system_score_gemma":0.0006698808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006925482,"about_ca_topic_score_gemma":0.003562206,"domain_scores_codex":[0.9997093,0.00006769817,0.00001066809,0.00006911587,0.00008250011,0.00006066064],"domain_scores_gemma":[0.9994209,0.0002575875,0.0001586462,0.00003985948,0.00008899626,0.00003396595],"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":[0.00006005847,0.0000287164,0.0003020602,0.0000276463,0.00001182998,0.00005328339,0.00009545669,0.9808285,0.00445986,0.005766814,0.0001647652,0.008201092],"study_design_scores_gemma":[0.000006343559,0.00004362146,0.00008093794,0.000001400584,0.000002649051,0.000004548625,0.00001021809,0.9983663,0.0004784207,0.0009130188,0.00008995553,0.000002665131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1068619,0.0001466909,0.8895615,0.0001183654,0.00002250021,0.0000335415,0.00001755249,0.0001165679,0.003121346],"genre_scores_gemma":[0.9856825,0.00005853234,0.01272961,0.00001701511,0.00000669482,0.00003097041,0.00001610926,0.000008641089,0.001449922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006925482,"threshold_uncertainty_score":0.01377034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007685103527996069,"score_gpt":0.2453209227220304,"score_spread":0.2376358191940344,"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."}}