{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001950744,0.0001354039,0.0002360586,0.00003422526,0.00007437398,0.0001281486,0.0002429517,0.00006002508,0.000003637278],"category_scores_gemma":[0.0001118251,0.0001311434,0.00003354701,0.0001481824,0.00002451547,0.0004395767,0.0001249382,0.00008072475,0.000005927732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008976224,"about_ca_system_score_gemma":0.00006422877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009836222,"about_ca_topic_score_gemma":0.00003948628,"domain_scores_codex":[0.9987105,0.00007966461,0.0002634904,0.0004927038,0.0001623956,0.0002912571],"domain_scores_gemma":[0.9993827,0.0001743188,0.0000689461,0.0002324873,0.00005717905,0.00008436454],"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":[0.000178904,0.0007412281,0.02249111,0.0001212727,0.0004333818,0.00008907307,0.007401346,0.8119057,0.08927906,0.04969018,0.0004392205,0.01722947],"study_design_scores_gemma":[0.002962514,0.00004683523,0.002333885,0.00001381529,0.000008617269,0.000002776959,0.0002439627,0.9933453,0.0006558277,0.0001539014,0.00009730461,0.0001352206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04067362,0.0001739793,0.9572436,0.0006894654,0.00006096295,0.0005761712,0.0000774111,0.0000443964,0.000460372],"genre_scores_gemma":[0.9902306,0.000005886132,0.008567878,0.0004268856,0.00001094268,0.0001306451,0.00001439087,0.000008522508,0.0006042948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9495569,"threshold_uncertainty_score":0.5347875,"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."}}