@proceedings {antonelo2006, title = {Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors}, journal = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, year = {2006}, month = {Jul.}, pages = {498-505}, publisher = {IEEE}, address = {Vancouver, BC}, abstract = {

Classical reinforcement learning mechanisms and a modular neural network are unified to conceive an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Different design apparatuses are considered to compose a system to tackle with these navigation difficulties, for instance: 1) neuron parameter to simultaneously memorize neuron activities and function as a learning factor, 2) reinforcement learning mechanisms to adjust neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures.

}, keywords = {reinforcement learning}, isbn = {0-7803-9490-9}, doi = {10.1109/IJCNN.2006.246723}, url = {https://ieeexplore.ieee.org/document/1716134/}, author = {Eric A Antonelo and Albert-Jan Baerlvedt and Thorsteinn Rognvaldsson and Mauricio Figueiredo} } @proceedings {292, title = {Evolutionary fuzzy system for architecture control in a constructive neural network}, journal = {6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {541-546}, publisher = {IEEE}, abstract = {

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi- Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554333}, url = {https://ieeexplore.ieee.org/abstract/document/1554333/}, author = {Rodrigo Calvo and Eric A Antonelo and Mauricio Figueiredo} } @proceedings {antonelo2005, title = {Intelligent autonomous navigation for mobile robots: spatial concept acquisition and object discrimination}, journal = {Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {553-557}, publisher = {IEEE}, address = {Helsinki, Finland}, abstract = {

An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554335}, url = {https://ieeexplore.ieee.org/document/1554335/}, author = {Eric A Antonelo and Mauricio Figueiredo and Albert-Jan Baerlvedt and Rodrigo Calvo} } @conference {293, title = {Autonomous intelligent systems applied to robot navigation: spatial concept acquisition and object discrimination}, booktitle = {2nd National Meeting of Intelligent Robotics (II ENRI) in the Congress of the Brazilian Computer Society (in Portuguese)}, year = {2004}, abstract = {

In this work, it is proposed an autonomous system capable of constructing its navigation strategy for mobile robots coherently with the acquired environmental knowledge: spatial proximity concept and color patterns associated to each type of object. The autonomous system, based on neural networks, doens\’t have innate behaviors of target seeking or obstacle avoidance, but it learns these specific behaviors for each type of object as the robot interacts with the environment. Simulation results are showed and confirm the system\’s potenciality, as well as its generalization capacity.

}, author = {Eric A Antonelo and Mauricio Figueiredo} } @conference {294, title = {Neural structures for modeling of biological declarative memory applied to robot autonomous navigation systems}, booktitle = {4th Forum of Informatics and Technology of Maring{\'a} (FITEM) (in Portuguese)}, year = {2002}, abstract = {

, pp. (2002)

}, author = {Eric A Antonelo and Mauricio Figueiredo} }