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Abstract
In this thesis, we state the collision avoidance problem as a vertex covering problem, then we consider a distributed framework in which a team of cooperating Unmanned Vehicles (UVs) aim to solve this optimization problem cooperatively to guarantee collision avoidance between group members. For this purpose, we implement a distributed control scheme based on a robust Set-Theoretic Model Predictive Control ( ST-MPC) strategy, where the problem involves vehicles with independent dynamics but with coupled constraints, to capture required cooperative behavior.
Abstract
In this thesis, we state the collision avoidance problem as a vertex covering problem, then we consider a distributed framework in which a team of cooperating Unmanned Vehicles (UVs) aim to solve this optimization problem cooperatively to guarantee collision avoidance between group members. For this purpose, we implement a distributed control scheme based on a robust Set-Theoretic Model Predictive Control ( ST-MPC) strategy, where the problem involves vehicles with independent dynamics but with coupled constraints, to capture required cooperative behavior.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Rafiei, Mehrdad
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Distributed Optimization,Constraint-coupled Optimization,Cooperative Robotics,Collision Avoidance,Traffic Control,Autonomous Vehicles
Data di discussione della Tesi
5 Ottobre 2022
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Rafiei, Mehrdad
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Distributed Optimization,Constraint-coupled Optimization,Cooperative Robotics,Collision Avoidance,Traffic Control,Autonomous Vehicles
Data di discussione della Tesi
5 Ottobre 2022
URI
Gestione del documento: