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ADAS control algorithm analysis and design for drivability enhancement of UGVs

Giuseppe Di Mauro

ADAS control algorithm analysis and design for drivability enhancement of UGVs.

Rel. Nicola Amati, Andrea Delmastro. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022


In recent years, with the development of control techniques, sensors and teleoperation system, Unmanned Ground Vehicles (UGVs) are becoming widely employed in both civilian and military fields. The thesis work is made in cooperation with AMET S.r.l.: the main aim is to convert a standard vehicle in a UGV and to develop a shared control algorithm to help human operator in the directly teleoperated driving mode. The state-of-the-art of UGVs is investigated with their architecture, classification and application. Next, an analysis of the communication system is performed, highlighting the resulting chain of delay, with a focus on solutions proposed in the literature to mitigate these latency effects. The UGV is modelled by exploiting a “PKH” architecture useful to clearly distinguish the different parts of the mechatronic system (Plant, Control and Host). The main goal is to build a shared control framework where an autonomous controller, applied on the steering system, adjusts, when needed, the steering angle provided by human operator. The autonomous controller is designed with a model predictive control (MPC) technique that generates the required steering wheel angle. This type of control is critical to reduce the delay introduced by the communication system, which can degrade the driving performance leading the vehicle in unsafe conditions. Main part of the plant is implemented with a conventional vehicle model in TruckSim, while the control algorithm is designed in Simulink: these two software are linked exploiting the co-simulation feature. Lastly, the shared control algorithm is simulated underlying the objectives achieved and the references to future works.

Relators: Nicola Amati, Andrea Delmastro
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 79
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: AMET S.r.l.
URI: http://webthesis.biblio.polito.it/id/eprint/25445
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