{"id":"W2775176094","doi":"10.1504/ijvp.2018.10009700","title":"Optimal path planning for unmanned ground vehicles using potential field method and optimal control method","year":2017,"lang":"en","type":"article","venue":"International Journal of Vehicle Performance","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Motion planning; Optimal control; Field (mathematics); Path (computing); Potential field; Control (management); Computer science; Aerospace engineering; Control theory (sociology); Mathematical optimization; Engineering; Physics; Mathematics; Artificial intelligence; Robot; Geophysics","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":[0.0004074748,0.0006860603,0.0006388431,0.000563381,0.000467484,0.0005347387,0.0006994198,0.0006637615,0.001498544],"category_scores_gemma":[0.0007099586,0.0003713669,0.0005394468,0.0004504352,0.0006047025,0.0006671266,0.0006093857,0.0006241249,0.0001725237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000607799,"about_ca_system_score_gemma":0.001127173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005992908,"about_ca_topic_score_gemma":0.002713617,"domain_scores_codex":[0.9997924,0.00006260395,0.000008359078,0.00003694637,0.00007779368,0.00002194225],"domain_scores_gemma":[0.9997841,0.0001149663,0.00002904464,0.000008637959,0.00004991214,0.00001338394],"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":[0.00001754094,0.00001260136,0.0001339957,0.00005159022,0.00001139304,0.00004359618,0.00003908261,0.9616418,0.001416514,0.01249874,0.000342617,0.02379061],"study_design_scores_gemma":[0.000003675504,0.00001172515,0.00002284614,0.000002960637,0.000001471574,0.000007681777,0.000004011323,0.9969038,0.0001581523,0.002516571,0.0003643129,0.000002772472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004229131,0.0001891201,0.9938445,0.00005970779,0.00001768973,0.00002224917,0.000009857994,0.00007617757,0.001551652],"genre_scores_gemma":[0.5620423,0.0005449199,0.4325175,0.00007269109,0.00004309643,0.000337894,0.00007975259,0.00008316353,0.004278717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005992908,"threshold_uncertainty_score":0.0119161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03373568873278731,"score_gpt":0.3597245771635071,"score_spread":0.3259888884307198,"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."}}