{"id":"W1989052211","doi":"10.1007/s10846-005-9010-8","title":"A Novel Approach for Mobile Robot Navigation with Dynamic Obstacles Avoidance","year":2005,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Obstacle avoidance; Mobile robot; Obstacle; Computer science; Trajectory; Robot; Path (computing); Motion planning; Representation (politics); Mobile robot navigation; Real-time computing; Artificial intelligence; Control engineering; Robot control; Engineering","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":[0.0001324013,0.000697317,0.0008543122,0.0005429661,0.0006527024,0.0006746993,0.001391112,0.0008807434,0.001920414],"category_scores_gemma":[0.0002842392,0.0003324315,0.0006623757,0.00060239,0.0003418784,0.0008838603,0.001526302,0.0006902201,0.0009345079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000270401,"about_ca_system_score_gemma":0.0005331894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001370588,"about_ca_topic_score_gemma":0.002232918,"domain_scores_codex":[0.9998105,0.00001527663,0.000007683858,0.0000510434,0.00009472507,0.00002084486],"domain_scores_gemma":[0.9999065,0.00001509365,0.000008488616,0.00001568333,0.00004049291,0.00001368491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001699065,0.0002222942,0.0007790216,0.0003113647,0.0001880091,0.0006928753,0.0002567499,0.111271,0.1345247,0.07750003,0.009405426,0.6646788],"study_design_scores_gemma":[0.00004275622,0.0001452741,0.0002956999,0.00001292597,0.0000636484,0.0007027605,0.00004116911,0.9529098,0.01081994,0.01725427,0.0176684,0.00004346575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004032769,0.0001803559,0.9926196,0.00007813675,0.000146544,0.00002459887,0.0000201182,0.0003093435,0.00258853],"genre_scores_gemma":[0.1510585,0.0005951505,0.8340384,0.0002071237,0.0001895123,0.0002029383,0.0001493422,0.0001248384,0.01343419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001920414,"threshold_uncertainty_score":0.006424367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02320134368818244,"score_gpt":0.2689790018510827,"score_spread":0.2457776581629003,"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."}}