{"id":"W3207097712","doi":"10.1109/icra48506.2021.9561054","title":"Model Predictive Control for Cooperative Hunting in Obstacle Rich and Dynamic Environments","year":2021,"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":"University of Toronto","funders":"","keywords":"Obstacle; Obstacle avoidance; Maxima and minima; Computer science; Collision avoidance; Model predictive control; Planner; Reciprocal; Control (management); Collision; Trajectory; Control theory (sociology); Motion planning; Robot; Artificial intelligence; Mobile robot; Mathematics; Geography; 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":[0.0003147844,0.0005731818,0.0004634912,0.0001951308,0.00037789,0.0006330126,0.0006460804,0.0004483778,0.0006980537],"category_scores_gemma":[0.0006763351,0.0002251129,0.0002534404,0.0003085886,0.0004898394,0.0003953399,0.0005491564,0.0007195056,0.0001419989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003625108,"about_ca_system_score_gemma":0.0006256466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006780985,"about_ca_topic_score_gemma":0.00592302,"domain_scores_codex":[0.999868,0.0000279634,0.000004335456,0.00002937893,0.00004880468,0.00002153467],"domain_scores_gemma":[0.9997838,0.0001074749,0.00004333093,0.00001404402,0.00003901224,0.0000122827],"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":[0.00003061365,0.00002976432,0.0002318182,0.00005501362,0.00001827163,0.00008355449,0.0000734531,0.9681886,0.002703956,0.006853385,0.0004659175,0.02126565],"study_design_scores_gemma":[0.000005495337,0.00003025239,0.00008214453,0.000003211638,0.00000380656,0.000009193948,0.000009465112,0.9975463,0.0003293986,0.001514565,0.0004636579,0.000002485025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02579324,0.0005764982,0.9678096,0.0001605132,0.00006457892,0.00002789519,0.0000193105,0.0002457584,0.005302622],"genre_scores_gemma":[0.9740049,0.0003943293,0.0227539,0.00005008203,0.00003030649,0.00007375024,0.00003382875,0.00001395758,0.002644955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006780985,"threshold_uncertainty_score":0.01348305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275607977527688,"score_gpt":0.2354667616102744,"score_spread":0.2227106818349975,"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."}}