{"id":"W4287882695","doi":"10.1109/isie51582.2022.9831670","title":"Formation Shaping Control for Multi-Agent Systems with Obstacle Avoidance and Dynamic Leader Selection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Collision avoidance; Obstacle avoidance; Obstacle; Computer science; Mobile robot; Process (computing); Collision; Controller (irrigation); Control theory (sociology); Displacement (psychology); Robot; Trajectory; Control engineering; Control (management); Real-time computing; Artificial intelligence; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001126437,0.0003743675,0.0004034507,0.0002641913,0.000804667,0.0005576898,0.001044029,0.0001553872,0.00001933654],"category_scores_gemma":[0.00007199899,0.000385087,0.0001317171,0.0004915578,0.00003104545,0.0008203179,0.0001172725,0.0008035609,0.00001256112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002780861,"about_ca_system_score_gemma":0.0002776484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009527238,"about_ca_topic_score_gemma":0.00005744154,"domain_scores_codex":[0.9962518,0.0003429748,0.0007263134,0.0008306212,0.001107419,0.0007409172],"domain_scores_gemma":[0.9982812,0.0002781442,0.0006395587,0.0003634257,0.000295843,0.0001417763],"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":[0.004132103,0.001742078,0.001427627,0.0001677798,0.002101615,0.00005865003,0.002395148,0.6312969,0.2068623,0.1267293,0.008294026,0.01479243],"study_design_scores_gemma":[0.008301726,0.001077812,0.00003644213,0.00005437031,0.00004716761,0.0001919094,0.0002198686,0.967788,0.00259974,0.0000388866,0.01920666,0.0004373464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06336519,0.0004562823,0.9236127,0.003215031,0.005323215,0.003101794,0.0004481312,0.0003233156,0.0001543614],"genre_scores_gemma":[0.9963754,0.00002962568,0.0003655188,0.000330026,0.0003977672,0.001615248,0.0001868126,0.0000475014,0.0006520713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9330102,"threshold_uncertainty_score":0.9998601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04161474487076511,"score_gpt":0.2659374443167727,"score_spread":0.2243226994460076,"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."}}