Abstract

Living organisms can act autonomously because biological neural networks process the environmental information in continuous time. Therefore, living organisms have inspired many applications of autonomous control to small-sized robots. In this chapter, a small-sized robot is controlled by a hardware artificial neural network (ANN) without software programs. Previously, the authors constructed a multilegged walking robot. The link mechanism of the limbs was designed to reduce the number of actuators. The current paper describes the basic characteristics of hardware ANNs that generate the gait for multilegged robots. The pulses emitted by the hardware ANN generate oscillating patterns of electrical activity. The pulse-type hardware ANN model has the basic features of a class II neuron model, which behaves like a resonator. Thus, gait generation by the hardware ANNs mimics the synchronization phenomena in biological neural networks. Consequently, our constructed hardware ANNs can generate multilegged robot gaits without requiring software programs.

Highlights

  • Many types of multilegged robots have been developed for various applications [1–3]

  • The gait rhythms generated by the hardware artificial neural network (ANN) are tested in a multilegged robot. 4.1

  • The width and length of the mounted hardware ANNs are 100 and 80 mm, respectively; the hardware ANNs are sufficiently small to install on the quadruped robot

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Summary

Introduction

Many types of multilegged robots have been developed for various applications [1–3]. Most of these robots were bioinspired by the structures, features, and excellent functionalities of living organisms [4, 5]. Hardware rings of coupled oscillators, which can generate various oscillatory patterns by using the synchronization phenomena [19, 20], have been employed as the structural elements of ANNs. given that most of the hardware neuron models contain inductors in their circuit architectures [19–22], they are difficult to implement in an integrated circuit (IC); the use of such models is disadvantageous on the circuit scale [23]. The authors are studying hardware ANNs based on a pulse-type hardware neuron model [24– 27] with the same basic features as biological neurons. This model possesses spatiotemporal summation characteristics, a threshold period, and a refractory period and generates oscillating patterns of electrical activity. The hardware ANNs are validated in locomotion tests of the multilegged robot

Multilegged robots
Quadruped robot
Hexapod robot The fabricated hexapod robot is displayed in Figure 3
Hardware artificial neural networks
Pulse-type hardware neuron model
A þ B þ VGp
Basic characteristics of the cell body model
Excitatory-inhibitory neuron pair model
Results and discussion
Conclusions
Full Text
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