{"id":"W2133359599","doi":"10.1109/robot.1992.220093","title":"Heuristics for local path planning","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Motion planning; Heuristic; Computer science; Path (computing); Acceleration; Mobile robot; Mathematical optimization; Process (computing); Set (abstract data type); Robot; Product (mathematics); 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":[0.001198876,0.001536094,0.001116514,0.001715836,0.001323609,0.001984464,0.002279687,0.001469066,0.009571927],"category_scores_gemma":[0.004433799,0.0009216834,0.001524799,0.002237493,0.001676161,0.002171506,0.001967072,0.002079505,0.002992345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001531394,"about_ca_system_score_gemma":0.001769048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004277313,"about_ca_topic_score_gemma":0.005587188,"domain_scores_codex":[0.9988965,0.0003558933,0.0000767789,0.0002147735,0.0003175549,0.0001384367],"domain_scores_gemma":[0.9983992,0.001009161,0.00009884814,0.0002765188,0.0001633921,0.00005281322],"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":[0.0001874874,0.000144143,0.0004694219,0.0006864317,0.0001476161,0.0002525199,0.0003752675,0.4157464,0.002702111,0.2880991,0.01637832,0.2748112],"study_design_scores_gemma":[0.0001639977,0.0001357934,0.000210519,0.0001778962,0.0001107061,0.0002736359,0.0001531442,0.6381285,0.00388387,0.2958457,0.06084422,0.00007194266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001818565,0.0009593858,0.9886586,0.0001416955,0.00006940866,0.0001196084,0.0001115222,0.0009443696,0.007176884],"genre_scores_gemma":[0.06451771,0.001152137,0.9287179,0.0001837821,0.00006716982,0.0005228994,0.0005452638,0.000328198,0.003964944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009571927,"threshold_uncertainty_score":0.03202134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03092608992716676,"score_gpt":0.2775606346546577,"score_spread":0.2466345447274909,"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."}}