{"id":"W3014780816","doi":"10.1061/(asce)as.1943-5525.0001143","title":"Real-Time Autonomous Obstacle Avoidance for Fixed-Wing UAVs Using a Dynamic Model","year":2020,"lang":"en","type":"article","venue":"Journal of Aerospace Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Obstacle avoidance; Trajectory; Kinematics; Solver; Control theory (sociology); Model predictive control; Computer science; Fidelity; Obstacle; Collision avoidance; Nonlinear system; Key (lock); Fixed wing; Control engineering; Wing; Engineering; Aerospace engineering; Mobile robot; Artificial intelligence; Collision; Control (management); Robot","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003588755,0.0002047611,0.0003853854,0.0001056671,0.00008604638,0.0001454869,0.0007003304,0.0000735257,8.555593e-7],"category_scores_gemma":[0.000247964,0.0002105089,0.0001639457,0.0002972256,0.00001131479,0.0007276568,0.000112842,0.0003063811,0.000004370363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002268369,"about_ca_system_score_gemma":0.000177653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002660398,"about_ca_topic_score_gemma":6.463198e-8,"domain_scores_codex":[0.9986019,0.00001596939,0.0004405459,0.0002411395,0.0003000843,0.0004004158],"domain_scores_gemma":[0.9989059,0.0001577814,0.0003194637,0.0002295569,0.0001364979,0.0002508035],"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":[0.000005586094,0.000008750024,0.00001396169,0.00003593951,0.00002572731,0.00003847135,0.000970265,0.862439,0.1359598,0.00009281871,0.00008686786,0.0003227853],"study_design_scores_gemma":[0.00043481,0.0001209872,0.00009880075,0.0001527699,0.00001870639,0.0001344917,0.00002047525,0.9969499,0.001781136,0.00003391621,0.0000357904,0.0002182278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09310412,0.0001550394,0.9053071,0.0008174754,0.0003570874,0.0001050403,0.000003308975,0.000137332,0.00001354079],"genre_scores_gemma":[0.1846068,0.000009770884,0.8150945,0.00005224076,0.0001546172,0.000001823399,3.957429e-7,0.00003208782,0.00004773902],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1345108,"threshold_uncertainty_score":0.8584306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001234508072966,"score_gpt":0.2445157588895424,"score_spread":0.2245034138088127,"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."}}