Autonomous vehicle parking using finite state automata learned by J-CC artificial neural nets

Osório, Fernando Santos; Heinen, F.J.; Fortes, Luciano L.S.

doi:10.1109/sbrn.2002.1181472

Cited by 4 publications

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“…Trabalhos mais recentes sobre o aprendizado de autômatos finitos por redes neurais aplicados em problemas de robótica (veículos autônomos) foram desenvolvidos principalmente por Fernando Osório, Farlei Heinen, Milton Heinen e Luciane Fortes (Heinen et al, 2007) (Osorio et al, 2002). Estes trabalhos, no entanto, foram mais focados em uma aplicação específica, o estacionamento de um veículo autônomo em uma vaga paralela.…”

Section: Aprendizado De Autômatos Finitos Por Redes Neurais Artificiaisunclassified

NeuroFSM: aprendizado de Autômatos Finitos através do uso de Redes Neurais Artificiais aplicadas à robôs móveis e veículos autônomos

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Autonomous navigation is a fundamental task in mobile robotics. In order to accurately perform this task it is necessary an intelligent navigation and control system associated to the sensorial system. This project presents the development of a control system for autonomous mobile robots and vehicles navigation. The adopted approach uses Artificial Neural Networks for Finite State Machine learning, allowing the robots to deal with sensorial data even when this data is not precise and correct. Simultaneously, it allows the robots to consider the different situations and states they are inserted in (context detection). This way, it is possible to decide how to proceed with motion control and then execute navigation and control tasks from the most simple ones until the most complex and high level tasks. So, this work uses Artificial Neural Networks to recognize the robot's current state (context) at the environment where it is inserted. Once the state is detected, including identification of robot's position according to environment elements, the robot will be able to determine the action/behavior to be executed. The navigation and control system implements a Finite State Machine deciding the current action from current state, being able to identify state changes, alternating between different previously defined behaviors. In order to validade this approach, many experiments were performed with the use of a robotic simulator (Player-Stage), and carrying out tests with real robots (Pioneer P3-AT, SRV-1 and autonomous vehicles).

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Section: Aprendizado De Autômatos Finitos Por Redes Neurais Artificiaisunclassified