{"id":"W4401244323","doi":"10.3390/drones8080371","title":"Pseudospectral-Based Rapid Trajectory Planning and Feedforward Linearization Guidance","year":2024,"lang":"en","type":"article","venue":"Drones","topic":"Spacecraft Dynamics and Control","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Control theory (sociology); Trajectory; Feed forward; Computer science; Pseudospectral optimal control; Flatness (cosmology); Linearization; Trajectory optimization; Discretization; Feedback linearization; Motion planning; Mathematical optimization; Mathematics; Pseudo-spectral method; Control engineering; Optimal control; Control (management); Engineering; Robot; Nonlinear system; Artificial intelligence","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":[0.0000552008,0.00009138923,0.00008665773,0.00006819137,0.00002928839,0.00007509925,0.00003780588,0.00004467002,0.0000230019],"category_scores_gemma":[0.000005152841,0.00009032593,0.0000275834,0.00009737774,0.00001394971,0.0000657358,0.000004049831,0.00007996986,0.0000117423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002304374,"about_ca_system_score_gemma":0.00001141893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004565489,"about_ca_topic_score_gemma":0.00000412396,"domain_scores_codex":[0.9995994,0.000005252806,0.00008806082,0.0001154359,0.00006343985,0.0001284024],"domain_scores_gemma":[0.9998472,0.0000309571,0.000005851356,0.00007497483,0.000007669328,0.00003331252],"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":[0.00006190222,0.00004584429,0.01947591,0.001455525,0.0004037303,0.0001992009,0.002424122,0.587509,0.2766937,0.01062624,0.00608352,0.0950213],"study_design_scores_gemma":[0.0001490778,0.00001816154,0.004484843,0.00006612135,0.00001394261,0.000002922517,0.00001793289,0.9897632,0.0009183069,0.00012862,0.004321145,0.0001157043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3646382,0.03680242,0.59037,0.0007299195,0.001417891,0.0002536554,0.0000282501,0.001701778,0.004057879],"genre_scores_gemma":[0.9988805,0.00005629278,0.0007058299,0.0000362065,0.0001408438,0.00001408602,0.00001041295,0.00002855299,0.0001272535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6342423,"threshold_uncertainty_score":0.3683384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004974429924649424,"score_gpt":0.2023127865543851,"score_spread":0.1973383566297357,"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."}}