{"id":"W2118684378","doi":"10.1109/robio.2006.340133","title":"An Extension of the Distance-Propagating Dynamic System for Robot Path Planning to Safe Obstacle Clearance","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Obstacle; Motion planning; Robot; Path (computing); Computer science; Margin (machine learning); Grid; Representation (politics); Obstacle avoidance; Penalty method; Path length; Mobile robot; Algorithm; Mathematical optimization; Real-time computing; Simulation; Artificial intelligence; Mathematics","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":[0.0006335353,0.0006092663,0.000488428,0.0005138705,0.0004360361,0.0005275611,0.001478605,0.000548088,0.00316001],"category_scores_gemma":[0.001717342,0.0004118438,0.0004632827,0.0005974458,0.000576827,0.001019103,0.001253997,0.001275024,0.0007279106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005003692,"about_ca_system_score_gemma":0.00133749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003266881,"about_ca_topic_score_gemma":0.003219843,"domain_scores_codex":[0.999564,0.00006605138,0.0000219106,0.00008784316,0.000226451,0.00003383292],"domain_scores_gemma":[0.9994501,0.0002082166,0.00005549625,0.0001082115,0.0001421231,0.00003581761],"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":[0.00007147745,0.00004961289,0.0002922055,0.0001185444,0.00002513331,0.0001074521,0.00009228194,0.6295508,0.0137666,0.04240541,0.001599905,0.3119206],"study_design_scores_gemma":[0.00001311248,0.00005040312,0.00007449446,0.000008293914,0.000006457016,0.00005806844,0.00000534786,0.9862207,0.002988715,0.006614353,0.003947917,0.00001219723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001831695,0.00002886772,0.9972837,0.00002128217,0.00001392344,0.00001718188,0.000008857271,0.0002169572,0.0005775143],"genre_scores_gemma":[0.1482193,0.0001898429,0.8482452,0.00006454111,0.00003440135,0.0001528482,0.00009661631,0.00013821,0.002859096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003266881,"threshold_uncertainty_score":0.01057124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329312200825824,"score_gpt":0.2608425543762455,"score_spread":0.2475494323679872,"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."}}