A Novel Hardware-Efficient Central Pattern Generator Model Based on Asynchronous Cellular Automaton Dynamics for Controlling Hexapod Robot

Kentaro Takeda,Hiroyuki Torikai

doi:10.1109/access.2020.3012706

Abstract

This paper presents a novel hardware-efficient central pattern generator (CPG) model to realize a bio-inspired gait of a hexapod robot. The CPG model consists of a network of cellular automaton (CA) oscillators; thus, it can be implemented as a network of sequential logic circuits. Detailed analyses of nonlinear oscillation dynamics show that the oscillator that is driven by multiple asynchronous clocks is more suitable to realize the gait of the robot than an oscillator that is driven by a single clock or multiple synchronous clocks. Moreover, detailed analyses of nonlinear network dynamics show that the clocks among the CA oscillators should be asynchronous to appropriately realize the gait. Using the analyses, systematic procedures to design the CPG model are proposed. The proposed CPG model is implemented in a field programmable gate array (FPGA); our experiments validate that the CPG model implemented in an FPGA can realize the bio-inspired gait of a hardware robot. Further, we show that the proposed CPG model utilizes fewer circuit elements and lower power than a conventional CPG model.

Highlights

Various species of animals, such as ants, spiders, snakes, and fish perform locomotion using flexor and extensor muscles that are driven by multiple signals with rhythmic patterns
Our experiments indicate that the central pattern generator (CPG) model implemented in an field programmable gate array (FPGA) can realize a tripod gait of the hardware robot, as shown in Fig. 1, where the tripod gait is a typical gait observed in six-legged insects [37]
This paper presented a novel CPG model consisting of a network of cellular automaton (CA) oscillators

Summary

Introduction

Various species of animals, such as ants, spiders, snakes, and fish perform locomotion using flexor and extensor muscles that are driven by multiple signals with rhythmic patterns. Studies in the field of biological motor control system show that such rhythmic patterns are produced by central pattern generators (CPGs) in the central nervous systems [1]. Inspired by this biological principle, many mathematical and electronic circuit models of CPGs have been designed to control artificial robots that are capable of performing useful tasks [2], [3]. A network of integrate-and-fire oscillators were used to

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Methods

Conclusion