<p>In this work new artificial learning and innate control mechanisms are proposed for application</p><p>in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots)</p><p>existent in the literature is enhanced with respect to its capacity of exploring the environment and</p><p>avoiding risky configurations (that lead to collisions with obstacles even after learning). The</p><p>particular autonomous system is based on modular hierarchical neural networks. Initially,the</p><p>autonomous system does not have any knowledge suitable for exploring the environment (and</p><p>capture targets œ foraging). After a period of learning,the system generates efficientobstacle</p><p>avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous</p><p>system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky</p><p>configurations) are discussed and the new learning and controltechniques (applied to the</p><p>autonomous system) are verified through simulations. It is shown the effectiveness of the</p><p>proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and</p><p>decrease their probability of appearance in the future and the number of collisions in risky</p><p>situations is significantly decreased. Experiments also consider maze environments (with targets</p><p>distant from each other) and dynamic environments (with moving objects).</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:hh-287 |
| Date | January 2006 |
| Creators | Aislan Antonelo, Eric |
| Publisher | Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Högskolan i Halmstad/Sektionen för Informationsvetenskap, Data- och Elektroteknik (IDE) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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