{"id":"W1589297171","doi":"10.1109/robot.1999.769998","title":"Kinodynamic motion planning for all-terrain wheeled vehicles","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Terrain; Motion planning; Robot; Computer science; Task (project management); Mobile robot; Motion (physics); Set (abstract data type); State space; Tree (set theory); Artificial intelligence; Robot kinematics; Actuator; Control theory (sociology); Engineering; Control (management); Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001875947,0.0003001247,0.0004278106,0.0002494681,0.0004070722,0.0004766787,0.0006758222,0.0003808734,0.001073419],"category_scores_gemma":[0.0005956778,0.0002899136,0.0002705253,0.0002587864,0.0004454492,0.0007213438,0.001020423,0.0003698383,0.0001606095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000602732,"about_ca_system_score_gemma":0.0005852258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00519697,"about_ca_topic_score_gemma":0.006746117,"domain_scores_codex":[0.9998873,0.00002697468,0.00000670151,0.00002266192,0.00003623369,0.0000200412],"domain_scores_gemma":[0.9998702,0.00005962928,0.00002568047,0.00001417487,0.00001678097,0.00001351002],"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":[0.00003127514,0.00001671206,0.0003895914,0.00004158444,0.00001119757,0.00008019801,0.0001110875,0.9494044,0.003030528,0.01325884,0.0002869209,0.03333763],"study_design_scores_gemma":[0.000005647927,0.00001200973,0.0001167402,0.000002051079,0.000002107592,0.00001457621,0.00001561055,0.9916682,0.0005714064,0.007093583,0.000494855,0.000003204097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03363877,0.00007118068,0.964217,0.00004713841,0.00000560806,0.00002950901,0.00002910151,0.0002951136,0.00166652],"genre_scores_gemma":[0.6672359,0.0001186338,0.3298636,0.00002776804,0.000005386741,0.0001393005,0.0001187904,0.00006898106,0.002421582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00519697,"threshold_uncertainty_score":0.01033342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03144756726827081,"score_gpt":0.2864834926470622,"score_spread":0.2550359253787914,"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."}}