{"id":"W4314946892","doi":"10.1109/cdc51059.2022.9993370","title":"Leader-follower bearing-based distributed pose estimation for multi-vehicle networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 61st Conference on Decision and Control (CDC)","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Observer (physics); Control theory (sociology); Scheme (mathematics); Nonlinear system; Stability (learning theory); Pose; Estimation; Computer science; Position (finance); Exponential stability; Property (philosophy); Bearing (navigation); State (computer science); Angular velocity; Exponential function; Mathematics; Algorithm; Engineering; Artificial intelligence; Machine learning; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00115657,0.0004523284,0.0006609394,0.0002586293,0.000944864,0.0006801959,0.001363298,0.0001574217,0.0001465724],"category_scores_gemma":[0.0003282001,0.0004433677,0.0002696198,0.0005880664,0.00007922029,0.0003924878,0.0002190246,0.0005046714,0.00004104661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002168268,"about_ca_system_score_gemma":0.0002287088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005913744,"about_ca_topic_score_gemma":0.00005458093,"domain_scores_codex":[0.9960435,0.0003534665,0.0007792354,0.001135285,0.0009395616,0.0007489653],"domain_scores_gemma":[0.9970425,0.0008748504,0.0004026781,0.001030891,0.000313325,0.0003356968],"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":[0.003178617,0.002034914,0.001731072,0.00005946145,0.0003307743,0.0001555241,0.0003936315,0.4637329,0.007186678,0.04092303,0.01694231,0.4633311],"study_design_scores_gemma":[0.01155997,0.0005815511,0.002031571,0.00004882928,0.00004440041,0.000009273066,0.0001050174,0.979816,0.00009995549,0.0006784505,0.00454145,0.0004835518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008493639,0.0001991803,0.9850816,0.001789215,0.001946867,0.001569384,0.0005461879,0.0003013429,0.00007265105],"genre_scores_gemma":[0.9921714,0.000008068302,0.004941343,0.001406032,0.00009462599,0.0008751087,0.0001801892,0.00003518408,0.0002880709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9836777,"threshold_uncertainty_score":0.9998018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04581486039239296,"score_gpt":0.2845109741130211,"score_spread":0.2386961137206282,"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."}}