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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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Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper focus in the road identification problem, which is an important capability to autonomous vehicle drive. Our approach is based on image processing, template matching classification, and finite state machines processing. The proposed system allows to train an image segmentation algorithm in order to identify navigable and non-navigable regions (inside/outside roads), generating as output the steering control for an Electric Autonomous Vehicle, that should stay following the road. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.