{"id":"W2072345862","doi":"10.1109/iccse.2014.6926440","title":"Autonomous robot navigation with self-learning for collision avoidance with randomly moving obstacles","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Robot; Controller (irrigation); Computer science; Obstacle avoidance; Reinforcement learning; Collision avoidance; Robot control; Artificial intelligence; Obstacle; Motion planning; Visual servoing; Motion control; Mobile robot; Robot learning; Mobile robot navigation; Computer vision; Robot kinematics; Collision; Control theory (sociology); Control (management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003060595,0.0003288083,0.0003238225,0.000214992,0.0002866534,0.0002704276,0.0006794665,0.000364012,0.0007670866],"category_scores_gemma":[0.0006640403,0.0001794908,0.0002908988,0.0001544848,0.0004543088,0.0003225146,0.0004636639,0.0004447989,0.0001533733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004062047,"about_ca_system_score_gemma":0.0007453728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00404468,"about_ca_topic_score_gemma":0.003120588,"domain_scores_codex":[0.9998357,0.00003213367,0.000007382466,0.00003154901,0.0000743713,0.00001884665],"domain_scores_gemma":[0.9997417,0.00009948444,0.00003871979,0.00002222778,0.00007977182,0.00001804533],"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":[0.00004125564,0.00009220689,0.0007289969,0.00007792907,0.00003517627,0.00008227733,0.0001278897,0.8649111,0.01882846,0.009614361,0.0005396988,0.1049207],"study_design_scores_gemma":[0.000006378297,0.00003602956,0.00009385092,0.000001866176,0.00000318946,0.00001108113,0.000003083143,0.9976329,0.001109031,0.0007849855,0.0003150838,0.00000252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01949466,0.0001092305,0.9788417,0.00003419167,0.00001551392,0.00003158122,0.000004172547,0.0002729414,0.001195983],"genre_scores_gemma":[0.8548036,0.0001150816,0.142903,0.00006461225,0.00002052471,0.0001334838,0.00002355186,0.00003804662,0.001898076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00404468,"threshold_uncertainty_score":0.008042276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005518795616060557,"score_gpt":0.2285336731935279,"score_spread":0.2230148775774674,"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."}}