{"id":"W4238925767","doi":"10.32920/ryerson.14668500","title":"Real time autonomous collision avoidance for unmanned aerial vehicles","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Guidance and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Terrain; Obstacle; Computer science; Trajectory optimization; Obstacle avoidance; Collision avoidance; Flight planning; Aerospace engineering; Control theory (sociology); Control (management); Collision; Engineering; Artificial intelligence; Robot; Mobile robot; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000188411,0.0003032436,0.0005664693,0.00005285302,0.0000592648,0.0001833649,0.0002784626,0.0004009915,0.00006098899],"category_scores_gemma":[0.00002318152,0.000307772,0.0002293701,0.0000570712,0.00001406077,0.00006990242,0.0001376633,0.0002148249,0.00008989726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001655879,"about_ca_system_score_gemma":0.0001155754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001962188,"about_ca_topic_score_gemma":0.00007383196,"domain_scores_codex":[0.9986504,0.00003046578,0.0004355414,0.0003922847,0.0001560592,0.0003352885],"domain_scores_gemma":[0.9991965,0.00007595195,0.00006930635,0.0004776269,0.000103398,0.00007725236],"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":[0.0002185733,0.00009903745,0.00009446999,0.002281959,0.0007231351,0.00004862793,0.0009514721,0.6117063,0.3152488,0.00113992,0.05721523,0.01027247],"study_design_scores_gemma":[0.002055922,0.00009049332,0.000706628,0.0006673075,0.0001169241,0.000008246151,0.0001563659,0.937801,0.03011386,0.000568203,0.02649885,0.001216173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539425,0.00129982,0.01327474,0.0001667357,0.005360466,0.00172907,0.0001640535,0.001653922,0.02240869],"genre_scores_gemma":[0.9880847,0.0001952062,0.003532984,0.00003075662,0.001641436,0.000592458,0.0002463368,0.0001060731,0.005570018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3260947,"threshold_uncertainty_score":0.9999374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009259748121594897,"score_gpt":0.2177712203547376,"score_spread":0.2085114722331427,"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."}}