{"id":"W2441361126","doi":"10.1109/syscon.2016.7490605","title":"Formation reconfiguration of cooperative UAVs via Learning Based Model Predictive Control in an obstacle-loaded environment","year":2016,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Control reconfiguration; Flocking (texture); Obstacle; Robustness (evolution); Model predictive control; Computer science; Obstacle avoidance; Reinforcement learning; Convergence (economics); Control (management); Control theory (sociology); Control engineering; Engineering; Artificial intelligence; Mobile robot; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003739041,0.0003418468,0.0003866568,0.0002048857,0.0003810906,0.0004926811,0.0006102997,0.0004183644,0.0003295951],"category_scores_gemma":[0.001116883,0.0002479011,0.0002663219,0.000180599,0.0006903256,0.0004392166,0.0007868558,0.0004561276,0.00007024342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004025344,"about_ca_system_score_gemma":0.0006330521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007187078,"about_ca_topic_score_gemma":0.0044425,"domain_scores_codex":[0.999848,0.00003938717,0.000006178725,0.00003302284,0.0000376678,0.00003565822],"domain_scores_gemma":[0.9996274,0.0001507299,0.00009487124,0.00004013923,0.00004822075,0.0000386837],"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":[0.0000218517,0.00001337765,0.0003078151,0.000008379283,0.000005574699,0.00005788663,0.00004490716,0.9928377,0.001320134,0.0007830018,0.00007043966,0.004528857],"study_design_scores_gemma":[0.000004072703,0.00001525892,0.00006102013,7.796551e-7,0.000001618506,0.000005337562,0.000007628065,0.9991906,0.0002479803,0.0004089917,0.0000554739,0.000001117941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3000536,0.0001502525,0.6955022,0.0002331199,0.00003568983,0.00004185054,0.0000174141,0.0003891519,0.003576722],"genre_scores_gemma":[0.9926639,0.0000256583,0.006868221,0.00001241299,0.0000038451,0.00001798477,0.000007710635,0.000004899756,0.0003954876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007187078,"threshold_uncertainty_score":0.01429045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672130355076133,"score_gpt":0.2172177421889012,"score_spread":0.2004964386381399,"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."}}