{"id":"W4206322160","doi":"10.1109/icjece.2021.3120324","title":"3-D Path Planning Using Improved RRT* Algorithm for Robot-Assisted Flexible Needle Insertion in Multilayer Tissues","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Motion planning; Path (computing); Computer science; Robot; Algorithm; Biomedical engineering; Artificial intelligence; Engineering; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003099961,0.0006233566,0.0005870277,0.0005330281,0.0003465878,0.0005215006,0.0008349121,0.0006955846,0.001757114],"category_scores_gemma":[0.0009493424,0.0003998217,0.0006468066,0.0005254732,0.0003513495,0.0005003237,0.0007743093,0.0005928311,0.0003427406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004738857,"about_ca_system_score_gemma":0.001286697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007127379,"about_ca_topic_score_gemma":0.006616333,"domain_scores_codex":[0.9998053,0.00003436128,0.00001275192,0.00005362964,0.00006540518,0.00002855687],"domain_scores_gemma":[0.9997329,0.000108995,0.00003627449,0.00002786313,0.00007424808,0.00001977813],"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":[0.00009390643,0.00003354198,0.0007440287,0.00007631903,0.00003348915,0.0001582005,0.00008774437,0.8370904,0.005836383,0.003973322,0.001354845,0.1505179],"study_design_scores_gemma":[0.000008599805,0.00002340029,0.00007096849,0.000002873407,0.000004671434,0.00003993219,0.000007218264,0.9977078,0.0007984783,0.0007433468,0.0005873317,0.000005284925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01377838,0.0001686087,0.9843634,0.000063663,0.00002353203,0.00004205621,0.00002969447,0.0006547453,0.0008759417],"genre_scores_gemma":[0.2618359,0.000239093,0.7352729,0.00006038644,0.00001302952,0.0001807647,0.000208781,0.0001460805,0.002043073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007127379,"threshold_uncertainty_score":0.01417178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02300798949795707,"score_gpt":0.2414718567876902,"score_spread":0.2184638672897331,"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."}}