{"id":"W2098519927","doi":"10.1109/tro.2006.870668","title":"Modified Newton's method applied to potential field-based navigation for mobile robots","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Gradient descent; Obstacle; Newton's method; Oscillation (cell signaling); Descent (aeronautics); Potential field; Mobile robot; Computer science; Control theory (sociology); Field (mathematics); Obstacle avoidance; Robot; Mathematical optimization; Control engineering; Artificial intelligence; Mathematics; Engineering; Aerospace engineering; Control (management); Physics; Artificial neural 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002803941,0.0002736531,0.0002873894,0.0002511261,0.0003337052,0.0001611914,0.0006026178,0.0001874192,0.000004958325],"category_scores_gemma":[0.00000516083,0.0002942395,0.0001720755,0.0005415967,0.00001973274,0.0001618646,0.000003712197,0.0002484542,0.00004590311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077758,"about_ca_system_score_gemma":0.0001078513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007592883,"about_ca_topic_score_gemma":0.000004762435,"domain_scores_codex":[0.9980618,0.00006421687,0.0003938127,0.0006135285,0.0003948034,0.0004717851],"domain_scores_gemma":[0.9985408,0.0003881037,0.0001055027,0.0006722881,0.0001379659,0.0001553761],"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":[0.00004070661,0.0002035319,5.011136e-7,0.00001903634,0.00001619891,0.000007058914,0.00005800069,0.9748513,0.003035808,0.001300846,0.0005896855,0.01987732],"study_design_scores_gemma":[0.0008250925,0.0003293037,0.00002012759,0.0000288988,0.00004380363,0.000009616777,0.000009698313,0.9473703,0.04982987,0.001139367,0.00007011485,0.0003238054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002288383,0.000005748478,0.995387,0.001347386,0.001433362,0.00100894,0.0000205149,0.0004134592,0.0001547357],"genre_scores_gemma":[0.1727831,3.708501e-7,0.8258122,0.0005296674,0.0001169633,0.000303573,0.00001463026,0.00002909698,0.00041033],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1725543,"threshold_uncertainty_score":0.9999509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517967350413403,"score_gpt":0.2785286964162237,"score_spread":0.2633490229120897,"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."}}