{"id":"W2161754147","doi":"","title":"Incremental Phi*: incremental any-angle path planning on grids","year":2009,"lang":"en","type":"article","venue":"Scholarly Commons (University of Pennsylvania)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Laboratory; Army Research Office; University of Alberta; Northrop Grumman; National Science Foundation","keywords":"Grid; Motion planning; Any-angle path planning; Vertex (graph theory); Path (computing); Computer science; Tree (set theory); Algorithm; Series (stratigraphy); Reuse; Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence; Combinatorics; Geometry; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006984674,0.0002823854,0.0003799614,0.0004441096,0.0006858703,0.0002809005,0.002367507,0.0001509781,0.00005031556],"category_scores_gemma":[0.00005983078,0.0003586959,0.0001583118,0.0007220068,0.0001110789,0.003140752,0.0005723105,0.0007004869,0.000166384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002363974,"about_ca_system_score_gemma":0.000107811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001799481,"about_ca_topic_score_gemma":0.00001178906,"domain_scores_codex":[0.9975846,0.0002177787,0.0002360409,0.0006095691,0.0008214256,0.0005305574],"domain_scores_gemma":[0.9983062,0.0001167328,0.0002505984,0.0009191517,0.000134138,0.0002731697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001373041,0.008413371,0.2074585,0.000197922,0.00113171,0.0101308,0.06397512,0.008424161,0.22048,0.1322874,0.11534,0.230788],"study_design_scores_gemma":[0.007961548,0.005781821,0.9034094,0.0008272059,0.0001510656,0.0002471049,0.007296415,0.05744326,0.00386435,0.004884644,0.005854329,0.002278921],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8471909,0.0001584771,0.1317848,0.001871595,0.0004620117,0.0003736762,0.00005521997,0.0004774896,0.01762583],"genre_scores_gemma":[0.8963493,0.000005221812,0.1030234,0.0003286136,0.00005202331,2.495295e-7,0.00002425888,0.00001113262,0.0002058291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6959508,"threshold_uncertainty_score":0.9998865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02923367094946445,"score_gpt":0.2392544537485168,"score_spread":0.2100207827990524,"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."}}