A learning market based layered multi-robot architecture

Abstract

This work presents a novel learning market based layered architecture for distributed multi-robot systems. For the market system, a reward function is defined and adapted by some kind of learning algorithm in the dynamical system. A layered architecture combining the market based system with the typical layered robot architecture is also proposed to make the robot team execute smoothly in the dynamic environment. Results illustrate the relationships between task time, task amount and robot amount. The task/robot rate is defined to deeply study their relationships.