A CNN-Based Chip for Robot Locomotion Control

In this paper, the paradigm of emergent computation is applied to locomotion control in legged robots: the locomotion gait is the result of self-organization of a network of locally coupled nonlinear oscillators. This means to adopt the biological paradigm of central pattern generator (CPG), implemented by using cellular neural networks (CNNs). The whole control strategy is hybrid in the sense that the gait generation is accomplished by a fully analog CNN, while a simple logic unit modulates the behavior of the CNN-based CPG, so that the strategy is suitable to eventually include sensory feedback. The design of a VLSI chip implementing the CNN-based CPG and some experimental results on the chip are presented. The chip is designed using a switched-capacitor technique, fundamental to obtain in a simple and direct way some key features of the hybrid control discussed. The experimental results confirm the suitability of the approach.