{"id":"W2169226396","doi":"10.1109/ccece.1999.804896","title":"An efficient neural network model for path planning of car-like robots in dynamic environment","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Workspace; Computer science; Motion planning; Robot; Path (computing); Convergence (economics); Obstacle avoidance; Obstacle; Collision; Collision avoidance; Stability (learning theory); Lyapunov function; Lyapunov stability; Artificial intelligence; Mobile robot; Control (management); Machine learning; 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":[0.0002226835,0.0006768507,0.000597956,0.0003028302,0.0003653287,0.000572736,0.001314788,0.001024005,0.001721556],"category_scores_gemma":[0.0005550002,0.0003547156,0.000444128,0.0004789766,0.0004865027,0.0009864725,0.000514366,0.001000193,0.0003486865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007815452,"about_ca_system_score_gemma":0.0009232537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007411351,"about_ca_topic_score_gemma":0.006786212,"domain_scores_codex":[0.9998981,0.00001907978,0.000004864557,0.0000258553,0.00003564515,0.00001644414],"domain_scores_gemma":[0.9998837,0.00004971762,0.00001665379,0.000007702468,0.00003305786,0.0000092008],"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":[0.00001045998,0.00000679648,0.00005498209,0.00002071411,0.000006846245,0.00002826853,0.00001011881,0.9880135,0.001040287,0.005362425,0.0001491408,0.005296505],"study_design_scores_gemma":[0.000001922672,0.000007205756,0.00001649219,0.00000152073,0.000002190766,0.000006051084,0.000001246767,0.9982165,0.0001241555,0.001425767,0.0001949796,0.000001930519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01013018,0.000333409,0.9866143,0.000126583,0.00003339068,0.00002510972,0.00005477474,0.0001686602,0.002513475],"genre_scores_gemma":[0.7476698,0.001091001,0.2395384,0.00009903369,0.000068576,0.0005868076,0.0002838866,0.00008549117,0.01057709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007411351,"threshold_uncertainty_score":0.01473641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141639022817431,"score_gpt":0.2590086966213445,"score_spread":0.2375923063931701,"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."}}