{"id":"W4402265526","doi":"10.23919/acc60939.2024.10645024","title":"Collision-Free Platooning of Mobile Robots Through a Set-Theoretic Predictive Control Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Control and Dynamics of Mobile Robots","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Mobile robot; Model predictive control; Robot; Computer science; Set (abstract data type); Collision avoidance; Collision; Trajectory; Control (management); Artificial intelligence; Computer security; Programming language","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":[0.0002452878,0.0004433471,0.0004369617,0.000311347,0.0003997506,0.0005322521,0.0007760111,0.0003677904,0.0006910432],"category_scores_gemma":[0.0005408229,0.0002466793,0.0003780527,0.0002511442,0.0007435282,0.0003851477,0.0006428629,0.0005679249,0.0001063521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003640267,"about_ca_system_score_gemma":0.0006261601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005426465,"about_ca_topic_score_gemma":0.003331648,"domain_scores_codex":[0.9998381,0.00002944914,0.000006221841,0.00002483284,0.00008220584,0.00001915068],"domain_scores_gemma":[0.9998192,0.00007589231,0.00003518868,0.00001638264,0.00003964989,0.0000136296],"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":[0.0000299071,0.00002453659,0.0001933775,0.00005853705,0.00002418438,0.00008667989,0.0001366034,0.9468813,0.004797606,0.01851334,0.0004749768,0.02877893],"study_design_scores_gemma":[0.000004515143,0.00002257709,0.00003259151,0.000003116038,0.000003341707,0.000008299266,0.000006043358,0.9969732,0.0005216437,0.001925285,0.0004967261,0.000002732562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01267272,0.0001211242,0.9835035,0.00008522574,0.00003366951,0.00002569686,0.000009746464,0.0001807789,0.00336762],"genre_scores_gemma":[0.9316784,0.0002160102,0.06576575,0.00006659458,0.0000370295,0.0001035383,0.00002927755,0.0000255144,0.00207792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005426465,"threshold_uncertainty_score":0.01078975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004967827862811094,"score_gpt":0.2048320834346133,"score_spread":0.1998642555718022,"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."}}