EXPERIENCED TASK-BASED MULTI ROBOT TASK ALLOCATION

Year 2017, Volume: 18 Issue: 4, 864 - 875, 31.10.2017

H. Hilal Ezercan Kayır

https://doi.org/10.18038/aubtda.340101

Abstract

In multi robot system applications, it
is possible that the robots transform their past experiences into useful
information which will be used for next task allocation processes by using
learning-based task allocation mechanisms. The major disadvantages of multi-robot
Q-learning algorithm are huge learning space and computational cost due to
generalized state and joint action spaces of robots. In this study, experienced
task-based multi robot task allocation approach is proposed. According to this
approach, robots believe to be experienced about the tasks most frequently
done. Robots prefer to do these tasks rather than the inexperienced ones. Then,
robots refuse to execute inexperienced tasks over time. This means that the
system has reduced learning space. The proposed approach plays a crucial role
to achieve required system performance and provides effective solutions to
learning space dimensions.  The
effectiveness of the proposed algorithm is demonstrated by simulations on multi-robot
task allocation problem.

Keywords

Multi robot task allocation, Multi-agent Q-learning, Adaptive system architecture

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