{"id":"W2081493644","doi":"10.1163/156855307781035664","title":"An evolutionary algorithm for simultaneous localization and mapping (SLAM) of mobile robots","year":2007,"lang":"en","type":"article","venue":"Advanced Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Simultaneous localization and mapping; Robustness (evolution); Robot; Computer science; Heuristics; Mobile robot; Artificial intelligence; Algorithm; Evolutionary algorithm; Computer vision; Genetic algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000112373,0.0001479637,0.0001931212,0.0001109346,0.00008340461,0.00001141794,0.00006827653,0.0001077156,0.000002278736],"category_scores_gemma":[0.00003866269,0.000166386,0.00003298199,0.0002121786,0.00004677945,0.0001476801,0.00001026767,0.00006441315,8.464726e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006008771,"about_ca_system_score_gemma":0.00001121175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000029226,"about_ca_topic_score_gemma":0.000005600843,"domain_scores_codex":[0.9990879,0.00000975868,0.0003477411,0.0001768706,0.0001274159,0.0002502815],"domain_scores_gemma":[0.9993082,0.0001808574,0.00006136086,0.0001772171,0.0001843508,0.00008799524],"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":[0.000006891134,0.00003355347,0.00009150596,0.00008221972,0.0000134337,0.000003378073,0.0001146899,0.9078234,0.0035531,0.0006107177,0.00001050117,0.08765657],"study_design_scores_gemma":[0.0004177134,0.0001467589,0.0001067658,0.0000392257,0.0000178696,0.000006213509,0.0001920166,0.9948885,0.002971431,0.0005010161,0.0005203384,0.0001921245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00323224,0.0005971521,0.9953194,0.000004915928,0.0002552581,0.0003697122,0.00001590678,0.0001451215,0.00006030261],"genre_scores_gemma":[0.5155574,0.0001631281,0.4840061,0.00002215326,0.00006704305,0.000006861558,0.0001128044,0.0000426827,0.00002177182],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5123252,"threshold_uncertainty_score":0.6785024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006765387331309362,"score_gpt":0.2361221896195198,"score_spread":0.2293568022882104,"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."}}