Continuous-spaced action selection for single- and multi-robot tasks using cooperative extended kohonen maps

Abstract

Action selection is a central issue in the design of behavior-based control architectures for autonomous mobile robots. This paper presents an action selection framework based on an assemblage of self-organizing neural networks called Cooperative Extended Kohonen Maps. This framework encapsulates two features that significantly enhance a robot's action selection capability: self-organization in the continuous state and action spaces to provide smooth, efficient and fine motion control; action selection via the cooperation and competition of Extended Kohonen Maps so that more complex motion tasks can be achieved. Qualitative and quantitative comparisons for both single- and multi-robot motion tasks show that our framework can provide better action selection than do action superposition methods.