Alberto Emanuele Belvedere
Artificial Potential Fields-based Predictive Control for Autonomous Vehicles in Highway Scenarios.
Rel. Massimo Canale. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021
Abstract
Autonomous driving represents nowadays a great challenge and goal for both the industry and the academic world due to its numerous advantages such as traffic optimization, improved safety, lower emissions and passengers comfort. The key to accomplish this goal is a synergic work between the on-board vision system and sensors, responsible for the interaction with the driving scenario, and the control system, that implements the autonomous tasks. This thesis focuses on the problem of autonomous local navigation within highway scenarios, by assuming that the necessary information coming from on-board sensors is available. Such a problem is divided into two main steps: finding a optimal path and then performing its tracking through a suitable control system.
As a first step, a higher level controller that employs Artificial Potential Fields(APFs) methods for path planning is developed and casted in a Nonlinear Model PredictiveControl (NMPC) framework
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