{"id":"W2736316887","doi":"10.1109/icra.2017.7989234","title":"Falling in line: Visual route following on extreme terrain for a tethered mobile robot","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Terrain; Traverse; Robot; Mobile robot; Obstacle; Computer science; Bang-bang robot; Computer vision; Artificial intelligence; Simulation; Search and rescue; Trajectory; Obstacle avoidance; Heading (navigation); Robot kinematics; Engineering; Geodesy; Geology; Aerospace engineering; Physics; Geography","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":[0.0007085644,0.0001955177,0.0002697785,0.0001294612,0.000324124,0.0004407924,0.001397984,0.00009756216,0.000003358082],"category_scores_gemma":[0.0002935244,0.0001738503,0.0001356395,0.00009138337,0.0000231525,0.0005046512,0.000237901,0.0001645433,0.00003263594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007573795,"about_ca_system_score_gemma":0.000066224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002122779,"about_ca_topic_score_gemma":0.00004112103,"domain_scores_codex":[0.9983664,0.00004352483,0.0002961732,0.0005571639,0.0002631704,0.0004736262],"domain_scores_gemma":[0.9986092,0.000271956,0.0001383427,0.0008574395,0.00003006608,0.00009301008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000107402,0.001040587,0.01932119,0.00008687551,0.0001873886,0.0007175252,0.009425161,0.2660102,0.0202364,0.005004696,0.001013083,0.6768495],"study_design_scores_gemma":[0.001124631,0.0003156965,0.004089937,0.0001542249,0.000004417039,0.000004043987,0.00006868322,0.9915532,0.001475374,0.0007091195,0.0002179012,0.000282768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05400972,0.0000326454,0.9421573,0.0004811338,0.0009515298,0.0005201714,0.000001764881,0.0001981286,0.001647553],"genre_scores_gemma":[0.6142036,9.435719e-7,0.3842898,0.0001662809,0.0001080202,0.0001015313,0.000002382091,0.00001582232,0.001111644],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.725543,"threshold_uncertainty_score":0.7089408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06659100822760844,"score_gpt":0.3461917873120153,"score_spread":0.2796007790844068,"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."}}