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.
This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model
developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of
architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to
emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also
motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.
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