Behavior generation strategy of artificial behavioral system by self-learning paradigm for autonomous robot tasks

Evren Daǧlarli*, Hakan Temeltaş

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, behavior generation and self-learning paradigms are investigated for the real-time applications of multi-goal mobile robot tasks. The method is capable to generate new behaviors and it combines them in order to achieve multi goal tasks. The proposed method is composed from three layers: Behavior Generating Module, Coordination Level and Emotion -Motivation Level. Last two levels use Hidden Markov models to manage dynamical structure of behaviors. The kinematics and dynamic model of the mobile robot with non-holonomic constraints are considered in the behavior based control architecture. The proposed method is tested on a four-wheel driven and four-wheel steered mobile robot with constraints in simulation environment and results are obtained successfully.

Original languageEnglish
Title of host publicationUnmanned Systems Technology X
DOIs
Publication statusPublished - 2008
EventUnmanned Systems Technology X - Orlando, FL, United States
Duration: 17 Mar 200820 Mar 2008

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6962
ISSN (Print)0277-786X

Conference

ConferenceUnmanned Systems Technology X
Country/TerritoryUnited States
CityOrlando, FL
Period17/03/0820/03/08

Keywords

  • Autonomous robotics
  • Diploid genetic programming
  • Relational fuzzy logic
  • Robot behavior
  • SOM neural networks

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