{"id":"W4246022494","doi":"10.4018/978-1-5225-8060-7.ch023","title":"Optimal Robot Path Planning With Cellular Neural Network","year":2019,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Motion planning; Artificial neural network; Robot; Computer science; Path (computing); Analogy; Mobile robot; Cellular neural network; State space; Artificial intelligence; Topology (electrical circuits); Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002350244,0.0008735704,0.0008640717,0.00007890699,0.0002058766,0.0003833442,0.001952208,0.0005287173,0.000008076629],"category_scores_gemma":[0.000007142279,0.0007805162,0.0002267971,0.00005977146,0.0001204408,0.0001755633,0.0006992387,0.0008165087,0.000348709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002229473,"about_ca_system_score_gemma":0.0004554393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002499861,"about_ca_topic_score_gemma":8.429863e-7,"domain_scores_codex":[0.9961662,0.00004718049,0.0005197197,0.001287953,0.0009398707,0.001039039],"domain_scores_gemma":[0.9974048,0.00008605404,0.000486612,0.001564815,0.0001313508,0.0003263995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003425416,0.000007844765,0.0001316308,0.00003493571,0.0001453338,0.002292779,0.0001305327,0.3984081,0.000005477615,0.5926849,0.003823631,0.002300592],"study_design_scores_gemma":[0.003323503,0.002918064,0.0005965779,0.00477604,0.0004724685,0.002682459,0.00003165511,0.8758201,0.00005816608,0.06515915,0.03736671,0.006795091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.00006885138,0.0006740137,0.3538508,0.00004293933,0.001481094,0.0004228884,0.00002473167,0.0004949547,0.6429397],"genre_scores_gemma":[0.0383567,0.00000239987,0.7752969,0.001518806,0.002567704,0.0000300876,0.00003922709,0.0002644236,0.1819238],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5275258,"threshold_uncertainty_score":0.9994646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01991386139712844,"score_gpt":0.2290320174981853,"score_spread":0.2091181561010568,"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."}}