Karthikeyan Manohar
Path planning with Rapid Exploring Random Tree for autonomous race vehicle.
Rel. Nicola Amati, Andrea Tonoli, Angelo Bonfitto. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2020
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Abstract
Autonomous vehicle research and development is emerging in recent years globally aimed to provide a highly reliable, secure, mobile, and intelligent transportation system. The procedure involves integration of multiple systems together to ensure safe driving. The functional reference standard architecture of the autonomous driving system is classified into three main categories: perception, decision & control, and actuation. Mainly the challenging part is decision making that requires high-level planning and vehicle control to accomplish the driving mission. This thesis work is a small contribution to design and develop an autonomous formula student racing vehicle, and it mainly focuses on the study of decision making strategies and vehicle control system.
The experimentation aimed at the performance evaluation of a model-based control system and defining motion planning strategies for better vehicle navigation
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