Banca de QUALIFICAÇÃO: ÁLVARO PINTO FERNANDES DE NEGREIROS

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : ÁLVARO PINTO FERNANDES DE NEGREIROS
DATE: 18/12/2020
TIME: 09:00
LOCAL: Sala Virtual PPgEEC
TITLE:

An adapted subsumption architecture based on learning to improve autonomous water navigation techniques

KEY WORDS:

Autonomous Navigation, Autonomous Sailboats, Adapted Subsumption Architecture, Machine Learning

PAGES: 70
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Processamento Gráfico (Graphics)
SUMMARY:

Autonomous sailing navigation faces a different paradigm when searching for better efficiency in route planning. Unlike land navigation, where the best path is a result of the shortest route, with less traffic or obstacles, in the water other factors harden this dynamic. It is still unknown for the aquatic robotic's community the development of an efficient navigation technique, involving high-level control for such vehicles. Then, the biggest issue presented in this work is how to program and choose a control and behavior architecture for completely autonomous water vehicles. Nevertheless, also being effective and easy to use. Thus, we established as an objective to improve autonomous aquatic navigation techniques for robotics vessels through the use of subsumption architecture, combined with deep machine learning techniques. Hence, the great control problem is divided into several tasks that are isolated in behaviors, to be solved independently by machine learning. The big difference between the control algorithms usually adopted and of this proposal is based on the use of a reactive model, resulting in quick decision making - essential to autonomous aquatic navigation- combined with a machine-learning technique, allows a pursuit for the best parameters of behaviors and control architecture. Therefore, the hypothesis raised in this work is that through a behavioral architecture control dynamically adaptable to the environment, enables obtaining better results consistently than when in static architectures.

BANKING MEMBERS:
Externo à Instituição - DAVI HENRIQUE DOS SANTOS - UFRN
Externo à Instituição - JOAO MORENO VILAS BOAS DE SOUZA SILVA - IFRN
Externo à Instituição - JUSTO EMÍLIO ALVAREZ JÁCOBO - UERN
Presidente - 1345674 - LUIZ MARCOS GARCIA GONCALVES
Interno - 1242315 - PABLO JAVIER ALSINA