{"id":"W2070914248","doi":"10.1115/detc2002/mech-34361","title":"Mobile Robot for Uneven Terrain","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University; Arizona State University; University of Pennsylvania","keywords":"Mobile robot; Microcontroller; Climb; Robot; Servomotor; Terrain; DC motor; Computer science; Motion control; Linkage (software); Remote control; Robot control; Frame (networking); Servo control; Simulation; Control engineering; Embedded system; Servo; Engineering; Artificial intelligence; Computer hardware; Electrical engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009444127,0.000427124,0.0003184215,0.0003701895,0.0005065536,0.0004250209,0.0004880516,0.0004296559,0.00992862],"category_scores_gemma":[0.0002737024,0.000154027,0.0002142114,0.0001777974,0.0003108825,0.0006039097,0.0008728798,0.0005413141,0.004186961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000242134,"about_ca_system_score_gemma":0.0003143657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009990118,"about_ca_topic_score_gemma":0.00176167,"domain_scores_codex":[0.9998825,0.00001292124,0.00000342436,0.00001959754,0.00005698353,0.0000246024],"domain_scores_gemma":[0.9999279,0.000009795736,0.000009818772,0.00001372079,0.0000223746,0.00001639411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000285065,0.0001452924,0.001919803,0.0005048967,0.00004634654,0.001849855,0.0004194318,0.02793761,0.1548506,0.07623377,0.03961917,0.6961881],"study_design_scores_gemma":[0.0002076562,0.0008774602,0.003428571,0.000206177,0.00005003913,0.003071307,0.0003400101,0.1881174,0.02891492,0.02092023,0.7537771,0.00008910339],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08155859,0.00426777,0.7805756,0.001722608,0.001375542,0.0005008169,0.0004465152,0.009079635,0.1204729],"genre_scores_gemma":[0.4736612,0.001732297,0.4300153,0.0005611969,0.0001926151,0.000421175,0.0006772627,0.0002644935,0.09247459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00992862,"threshold_uncertainty_score":0.03321451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193362739189148,"score_gpt":0.2008297629913668,"score_spread":0.1888961355994754,"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."}}