{"id":"W4321385045","doi":"10.1109/cistem55808.2022.10043876","title":"Kinematic Navigation Control of Differential Drive Agricultural Robot","year":2022,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Real Time Kinematic; Robot; Skid (aerodynamics); Agriculture; Kinematics; Footprint; Mobile robot; Agricultural machinery; Sustainability; Agricultural engineering; Greenhouse; Population; Robot kinematics; Sustainable agriculture; Engineering; Computer science; Control engineering; Artificial intelligence; Geography; GNSS applications","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":[0.0002087981,0.0004211126,0.0002781635,0.0002224611,0.0003688934,0.0003908199,0.0004364708,0.0002972984,0.001279034],"category_scores_gemma":[0.000384119,0.0002391703,0.0002000045,0.0001540834,0.0003916057,0.0002014009,0.0004085875,0.0002080768,0.0002958748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522353,"about_ca_system_score_gemma":0.0006064675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003715917,"about_ca_topic_score_gemma":0.002322516,"domain_scores_codex":[0.9998422,0.00002237464,0.000009835258,0.0000468815,0.00005769196,0.00002094392],"domain_scores_gemma":[0.9998426,0.00002478426,0.00003430033,0.00001267141,0.00007401512,0.00001152064],"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":[0.0006222465,0.0001375365,0.005075477,0.0006518961,0.00008359993,0.0009953415,0.000731402,0.6020939,0.1706606,0.01277882,0.002433386,0.2037358],"study_design_scores_gemma":[0.0001072015,0.0006564974,0.002408797,0.00002824605,0.00003589221,0.0002997153,0.00006313606,0.9732207,0.01515826,0.001270964,0.006723549,0.00002696846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1356983,0.0003084956,0.8507403,0.0001662911,0.0001583678,0.0001186875,0.00007816991,0.001337581,0.01139374],"genre_scores_gemma":[0.9704825,0.0001399637,0.02548758,0.00003043321,0.00001296811,0.0001056191,0.0000642939,0.00001252128,0.003664139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003715917,"threshold_uncertainty_score":0.007388592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007240311307222528,"score_gpt":0.1813021045262685,"score_spread":0.174061793219046,"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."}}