{"id":"W2127711722","doi":"10.1109/cimsa.2008.4595843","title":"Mobile robot navigation using particle swarm optimization and noisy RFID communication","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Particle swarm optimization; Computer science; Radio-frequency identification; Mobile robot; Robot; Trajectory; Noise (video); Scheme (mathematics); Real-time computing; Position (finance); Tracking (education); Swarm behaviour; Computer vision; Radio frequency; Artificial intelligence; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001891547,0.00008411205,0.00009290762,0.00003509742,0.0002939394,0.00007293881,0.0002811232,0.00004419159,0.000003928784],"category_scores_gemma":[0.00001776944,0.00008318173,0.00001535338,0.0002805736,0.00006187259,0.0006944912,0.0001507844,0.00007386285,0.00001155973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000388224,"about_ca_system_score_gemma":0.00003237439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008362038,"about_ca_topic_score_gemma":3.825049e-7,"domain_scores_codex":[0.9992086,0.00007579289,0.0001813968,0.0002127068,0.0001655332,0.0001559641],"domain_scores_gemma":[0.9992707,0.00005235638,0.00007781213,0.0004502773,0.0000813154,0.00006754221],"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":[9.817466e-7,0.00003113894,0.001774627,0.000003412937,0.000003935404,0.000004695963,0.001486213,0.9933556,0.001069522,0.0006122473,0.00003594781,0.001621687],"study_design_scores_gemma":[0.0001975832,0.00004135179,0.001157702,0.00001762694,0.000003430586,0.0001122208,0.00004113452,0.9937548,0.004443024,0.0001059374,0.00001941512,0.0001057595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1654589,0.0001771637,0.8337446,0.00007936227,0.00005921973,0.0001274261,3.239328e-7,0.0001643911,0.0001886249],"genre_scores_gemma":[0.4519493,0.0000320776,0.5478808,0.00005068438,0.00001001162,0.00000722976,0.000005175796,0.0000042109,0.00006047355],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2864905,"threshold_uncertainty_score":0.3392053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03885897123193791,"score_gpt":0.273174518642514,"score_spread":0.2343155474105761,"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."}}