{"id":"W4407389699","doi":"10.2139/ssrn.5134756","title":"Investigation of Stochastic Deep Learning Path Planning Methods for Mobile Robots","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Carleton University; University of Guelph","funders":"","keywords":"Computer science; Mobile robot; Motion planning; Artificial intelligence; Deep learning; Path (computing); Robot; Human–computer interaction; Computer network","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":[0.002051039,0.0006768965,0.0008868846,0.0005678656,0.0004759934,0.0009245229,0.001459063,0.001510716,0.002544134],"category_scores_gemma":[0.009167998,0.0006852552,0.0005392301,0.000638854,0.0009557465,0.001488193,0.001405028,0.00174481,0.0002011902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001583958,"about_ca_system_score_gemma":0.002350049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00750825,"about_ca_topic_score_gemma":0.006711154,"domain_scores_codex":[0.9995354,0.0001716965,0.00002124005,0.00008797018,0.0001318941,0.00005176834],"domain_scores_gemma":[0.9943052,0.004524672,0.0003318376,0.0001786933,0.0004826736,0.0001768349],"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":[0.00003945908,0.00002946784,0.0004858391,0.0000775328,0.00002400856,0.00001582,0.00003194555,0.9516004,0.0004313944,0.02783073,0.0005469156,0.01888654],"study_design_scores_gemma":[0.000002254697,0.00001155134,0.00003287845,0.000006140398,0.000001758163,0.000003585904,0.000002434614,0.9958012,0.00007466674,0.003962862,0.00009952617,0.000001117942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05272616,0.0009927362,0.9399151,0.0008839917,0.00007467893,0.00005786408,0.00006702078,0.0002043107,0.005078204],"genre_scores_gemma":[0.8081252,0.0009483515,0.1836178,0.0003336943,0.0001059169,0.0001787749,0.0001791504,0.0001558153,0.006355393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00750825,"threshold_uncertainty_score":0.01492912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02800016227270666,"score_gpt":0.339964596450845,"score_spread":0.3119644341781383,"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."}}