{"id":"W2116160357","doi":"10.5772/56346","title":"The Path Planning of AUV Based on D-S Information Fusion Map Building and Bio-Inspired Neural Network in Unknown Dynamic Environment","year":2014,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Obstacle avoidance; Motion planning; Occupancy grid mapping; Artificial intelligence; Artificial neural network; Path (computing); Obstacle; Sensor fusion; Computer vision; Robot; Mobile robot; Geography","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":[0.0001667788,0.0002702613,0.0002879825,0.0002821772,0.0003558982,0.0003865832,0.0004416835,0.0005514028,0.000408103],"category_scores_gemma":[0.0004298329,0.0002242867,0.0003579458,0.000307245,0.0004441199,0.000586317,0.0005415497,0.0003197616,0.00007062936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004911997,"about_ca_system_score_gemma":0.00060729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00763225,"about_ca_topic_score_gemma":0.00442788,"domain_scores_codex":[0.9999037,0.0000166049,0.000005203978,0.00003244924,0.00003206312,0.000009945117],"domain_scores_gemma":[0.9999065,0.00003862883,0.00001403227,0.000009453564,0.00002432958,0.000006977659],"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":[0.00003854781,0.00001764514,0.0008835184,0.00005263116,0.00002716474,0.0001154891,0.00008999953,0.9167832,0.01045328,0.007966069,0.0003644056,0.06320817],"study_design_scores_gemma":[0.000002584389,0.00001444991,0.0001970857,0.000001995479,0.000004377769,0.0000203383,0.000008734091,0.9959027,0.001363148,0.002241947,0.0002388235,0.000003856101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04576008,0.0002764168,0.9511967,0.000154322,0.00002948328,0.00001846657,0.00002607521,0.0002574197,0.002280891],"genre_scores_gemma":[0.8896652,0.0002202763,0.1084717,0.00004530015,0.000009996617,0.00005572008,0.00004145969,0.00001358055,0.001476703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00763225,"threshold_uncertainty_score":0.01517564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004208054309956764,"score_gpt":0.2071433697464027,"score_spread":0.2029353154364459,"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."}}