{"id":"W1974835798","doi":"10.1117/12.606483","title":"Control and learning for intelligent mobility of unmanned ground vehicles in complex terrains","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"McGill University","keywords":"Terrain; Robotics; Computer science; Limiting; Robot; Artificial intelligence; Control (management); Perception; Human–computer interaction; Systems engineering; Control engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282779,0.0002498085,0.0004699679,0.0001282799,0.00007658804,0.00009531443,0.001034416,0.0001214301,0.000001453409],"category_scores_gemma":[0.0008032446,0.000220876,0.0003221016,0.0002694184,0.0002362749,0.0005852126,0.000199723,0.0003069011,3.341975e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466416,"about_ca_system_score_gemma":0.00002906986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002022937,"about_ca_topic_score_gemma":2.482369e-7,"domain_scores_codex":[0.9979597,5.218734e-8,0.0007701702,0.0004234329,0.000470448,0.0003762483],"domain_scores_gemma":[0.9981048,0.0004164899,0.0003932657,0.00006496318,0.0009222878,0.00009819082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001663154,0.0004163651,0.003756784,0.0009364988,0.0004164666,1.599627e-7,0.002152636,0.009950248,0.4062672,0.5704505,0.0005597804,0.004927006],"study_design_scores_gemma":[0.001679025,0.0004727903,0.01079182,0.0002394984,0.00004845846,0.00001327692,0.0009144705,0.9648314,0.0181717,0.001629505,0.0009246237,0.000283482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884652,0.00007599879,0.007976891,0.002468488,0.00008563646,0.0006603872,0.00001734145,0.00006417149,0.0001859076],"genre_scores_gemma":[0.6529,0.00001708135,0.3467779,0.00006151693,0.0001101556,0.00008247668,0.000002884251,0.00001867276,0.00002927984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9548811,"threshold_uncertainty_score":0.9007061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922174901622038,"score_gpt":0.2492201508702262,"score_spread":0.2299984018540058,"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."}}