Open this publication in new window or tab >>2006 (English)In: International Joint Conference on Neural Networks, 2006. IJCNN '06, Piscataway, N.J.: IEEE Press, 2006, p. 498-505Conference paper, Published paper (Refereed)
Abstract [en]
Classical reinforcement learning mechanisms and a modular neural network are unified for conceiving 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. Distinct design apparatuses are considered for tackling these navigation difficulties, for instance: 1) neuron parameter for memorizing neuron activities (also functioning as a learning factor), 2) reinforcement learning mechanisms for adjusting 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.
Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2006
Series
IEEE International Joint Conference on Neural Networks (IJCNN), ISSN 1098-7576
Keywords
mobile robots, neurocontrollers, path planning
National Category
Basic Medicine Computer and Information Sciences
Identifiers
urn:nbn:se:hh:diva-2112 (URN)10.1109/IJCNN.2006.246723 (DOI)000245125900073 ()2-s2.0-40649114292 (Scopus ID)2082/2507 (Local ID)0-7803-9490-9 (ISBN)2082/2507 (Archive number)2082/2507 (OAI)
Conference
International Joint Conference on Neural Networks, 2006. IJCNN '06, Vancouver
Note
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2008-11-072008-11-072022-09-13Bibliographically approved