{"id":"W3018404578","doi":"10.2514/1.g004460","title":"Online Feasible Trajectory Generation for Collision Avoidance in Fixed-Wing Unmanned Aerial Vehicles","year":2020,"lang":"en","type":"article","venue":"Journal of Guidance Control and Dynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fixed wing; Aeronautics; Obstacle avoidance; Trajectory; Collision avoidance; Computer science; Aerospace; Operations research; Artificial intelligence; Engineering; Aerospace engineering; Wing; Collision; Mobile robot; Physics; Robot; 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":[0.0003459767,0.0005324091,0.0006573576,0.0005229027,0.0005527199,0.0006097868,0.000860963,0.0006690851,0.003152041],"category_scores_gemma":[0.001631574,0.0004187414,0.0003430934,0.0004872432,0.0004418232,0.0008299588,0.001086445,0.000701018,0.0003552704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004671557,"about_ca_system_score_gemma":0.001026728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008158332,"about_ca_topic_score_gemma":0.00744556,"domain_scores_codex":[0.9997465,0.00005461423,0.00001093959,0.00005129026,0.00008558789,0.00005109268],"domain_scores_gemma":[0.9993766,0.0003266028,0.00007041382,0.0000574153,0.000125271,0.00004368592],"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":[0.0002883558,0.00007578077,0.0006351842,0.00005968483,0.00002314864,0.0001257495,0.00009188575,0.873536,0.002550062,0.005678842,0.001505543,0.1154298],"study_design_scores_gemma":[0.000007874348,0.00001720711,0.00005023452,0.000001934479,0.000001488009,0.000005718192,0.000004804371,0.9982797,0.0002808336,0.001225356,0.0001233579,0.000001499813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07958201,0.0001817154,0.9156993,0.0001623619,0.00005802121,0.00008048951,0.0001340483,0.0008375821,0.003264447],"genre_scores_gemma":[0.9064421,0.00005288439,0.09157862,0.00003016143,0.00001565262,0.00006893098,0.0002028732,0.00006146479,0.001547222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008158332,"threshold_uncertainty_score":0.0162217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02742335990480154,"score_gpt":0.2610103379780584,"score_spread":0.2335869780732569,"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."}}