{"id":"W4390619641","doi":"10.1016/j.simpat.2024.102893","title":"Autonomous locomotion mode transition in quadruped track-legged robots: A simulation-based analysis for step negotiation","year":2024,"lang":"en","type":"article","venue":"Simulation Modelling Practice and Theory","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Clearpath Robotics (Canada); University of Waterloo","funders":"","keywords":"Robot; Adaptability; Track (disk drive); Mode (computer interface); Terrain; Energy (signal processing); Simulation; Computer science; Climbing; Gait; Engineering; Control engineering; Control theory (sociology); Artificial intelligence; Human–computer interaction; Control (management); Mechanical engineering; Structural engineering","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":[0.0009296512,0.000206135,0.0002584309,0.0005783965,0.0001102631,0.0001866156,0.00005156739,0.0001612498,0.00002991839],"category_scores_gemma":[0.0001120051,0.0002230469,0.0001536534,0.0006035415,0.00001871583,0.0009941489,0.000002942645,0.0002029352,0.000008489858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001338971,"about_ca_system_score_gemma":0.00004121367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000286479,"about_ca_topic_score_gemma":0.00002372589,"domain_scores_codex":[0.998567,0.0002185984,0.0004880005,0.0003295885,0.0001846954,0.0002120992],"domain_scores_gemma":[0.9965729,0.002997234,0.00007019774,0.0001716471,0.0001260128,0.00006201577],"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":[0.0001872056,0.00006371159,0.00001028094,0.0001255481,0.0002162403,0.000001919447,0.00170645,0.9807463,0.0000466839,0.00637176,0.000001116527,0.01052274],"study_design_scores_gemma":[0.0009873364,0.00003621381,0.00003878449,0.00005274653,0.0006371905,4.113022e-7,0.000252585,0.9915202,0.00002378661,0.005961477,0.0002551629,0.0002341464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01195335,0.000474435,0.9858052,0.0003550674,0.0001707129,0.0006536867,0.00001672467,0.0004115356,0.000159275],"genre_scores_gemma":[0.9891009,0.00003459987,0.01029448,0.0001352274,0.0001033134,0.00006910821,0.0001838336,0.00004465665,0.00003391568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9771475,"threshold_uncertainty_score":0.9095588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927996735887121,"score_gpt":0.2890311178249841,"score_spread":0.2697511504661129,"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."}}