{"id":"W2006991255","doi":"10.1109/auv.2014.7054405","title":"An obstacle avoidance system for autonomous underwater vehicles: A reflexive vector field approach utilizing obstacle localization","year":2014,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Submarine Engineering (Canada)","funders":"","keywords":"Obstacle avoidance; Obstacle; Underwater; Collision avoidance; Computer science; Terrain; Real-time computing; Artificial intelligence; Mobile robot; Robot; Collision; Geography; Computer security","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":[0.0002444385,0.0005318439,0.0004279265,0.000310166,0.0003153623,0.0005099985,0.0008014357,0.000520515,0.0008460508],"category_scores_gemma":[0.0002444509,0.0001960301,0.0003262958,0.0001865526,0.0003187392,0.000517522,0.0006519942,0.0005859952,0.000365145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003366811,"about_ca_system_score_gemma":0.0006556257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003168328,"about_ca_topic_score_gemma":0.0021816,"domain_scores_codex":[0.9998485,0.00002222448,0.000007997614,0.00003676648,0.0000685765,0.00001602445],"domain_scores_gemma":[0.9999022,0.00001819001,0.00001244082,0.000008161791,0.00004789925,0.00001105578],"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":[0.0001073858,0.0001518385,0.0007178221,0.0002070221,0.00007545055,0.0002521337,0.0003610932,0.4609035,0.138579,0.01881527,0.002717282,0.3771123],"study_design_scores_gemma":[0.00002077905,0.0002017559,0.0002254069,0.00001182171,0.00001514086,0.00004807526,0.00003276992,0.9834742,0.008791712,0.00183496,0.005322031,0.0000213826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008594555,0.0001352162,0.9886614,0.00008360083,0.00004724267,0.00006694831,0.00001363279,0.0004659212,0.001931425],"genre_scores_gemma":[0.5404818,0.000458173,0.4460823,0.0001750703,0.00005866683,0.0003943021,0.0001582519,0.00009007277,0.0121013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003168328,"threshold_uncertainty_score":0.006299734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02404568215190603,"score_gpt":0.2473651610557918,"score_spread":0.2233194789038858,"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."}}