{"id":"W1591375184","doi":"10.1002/rob.21587","title":"Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking","year":2015,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Institute for Christian Studies; Toronto Rehabilitation Institute; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Defence Research and Development Canada","keywords":"Model predictive control; Computer science; Global Positioning System; Controller (irrigation); A priori and a posteriori; Ranging; Artificial intelligence; Trajectory; Control theory (sociology); Mobile robot; Computer vision; Terrain; Motion planning; Gaussian process; Path (computing); Robot; Gaussian; Control (management); Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004622241,0.0006135207,0.0005370217,0.0003240618,0.0003323143,0.0004830935,0.0009252618,0.0004022363,0.0009673241],"category_scores_gemma":[0.001628068,0.0002934587,0.0002679701,0.0002875655,0.0004670731,0.0005630742,0.000662783,0.0008855442,0.0003101601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005680248,"about_ca_system_score_gemma":0.0007126126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009684177,"about_ca_topic_score_gemma":0.008986546,"domain_scores_codex":[0.9997719,0.00002886141,0.00001098085,0.00005821426,0.00009423972,0.00003578782],"domain_scores_gemma":[0.9994954,0.0001913905,0.00007332792,0.00005303037,0.0001665705,0.00002035348],"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":[0.00005761624,0.00007654291,0.0004725182,0.00004882893,0.00001859378,0.00004040103,0.00006078087,0.8933163,0.00492612,0.001517145,0.0008570675,0.09860806],"study_design_scores_gemma":[0.000003885536,0.00001997915,0.00007009928,0.000001453595,0.000002162952,0.000005105488,0.000001918391,0.9987767,0.0006813244,0.0002770712,0.0001583952,0.000001854972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02409373,0.0001972294,0.9723997,0.0000825171,0.00004674807,0.00003218705,0.00001908198,0.001029677,0.002099041],"genre_scores_gemma":[0.9169947,0.0001102837,0.08061499,0.00007856301,0.00003161459,0.00006505863,0.00006952747,0.00007144489,0.001963813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009684177,"threshold_uncertainty_score":0.01925564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007119618220976049,"score_gpt":0.2275475846701291,"score_spread":0.2204279664491531,"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."}}