EXPERIENCED TASK-BASED MULTI ROBOT TASK ALLOCATION

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.