{"id":"W3215464764","doi":"10.1109/ccdc52312.2021.9602686","title":"Fast Trajectory Planning for AGV in the Presence of Moving Obstacles: A Combination of 3-dim A* Search and QCQP","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Solver; Mathematical optimization; Motion planning; Sequential quadratic programming; Computer science; Local optimum; Discretization; Quadratic programming; Path (computing); Quadratic growth; Control theory (sociology); Mathematics; Algorithm; Control (management); Artificial intelligence; Robot","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006949228,0.00006234772,0.0001360908,0.00008473899,0.00004159555,0.00003810501,0.0003847214,0.00003315952,0.000001070036],"category_scores_gemma":[0.0002064802,0.00004925123,0.00002465503,0.0003282461,0.00005158579,0.0001957995,0.0001096632,0.00008832226,2.515459e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001194783,"about_ca_system_score_gemma":0.00008872885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006638969,"about_ca_topic_score_gemma":0.000004881189,"domain_scores_codex":[0.999054,0.0001241102,0.0002074334,0.0002018934,0.0002575396,0.0001550912],"domain_scores_gemma":[0.9986506,0.0009070158,0.00005349848,0.0002540683,0.0001130421,0.00002182734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006885472,0.001330429,0.1285343,0.001855538,0.0001277341,0.0002596317,0.282034,0.2648598,0.07942653,0.1190628,0.0008949031,0.1215455],"study_design_scores_gemma":[0.0005208943,0.0001140028,0.08307963,0.0001745192,0.000003247981,0.00001888636,0.003860831,0.8996341,0.01183116,0.0006732638,0.000007540809,0.00008197792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3524518,0.0002633596,0.6462018,0.0002307524,0.00007071069,0.0001927117,0.000002775628,0.00001762098,0.0005683756],"genre_scores_gemma":[0.8576468,0.000003571384,0.1422226,0.00003429898,0.000007060017,0.000009685728,0.00000185426,0.000003081751,0.00007113683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6347743,"threshold_uncertainty_score":0.2008407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04521083658362462,"score_gpt":0.2999556384608785,"score_spread":0.2547448018772538,"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."}}