Development of Hardware Neural Networks IC with Switchable Gait Pattern for Insect-Type Microrobot

Abstract

The authors are studying Hardware Neural Networks (HNN) for generating a driving waveform of a millimeter size insect-type microrobot. HNN can generate the pulse waveform such as the neural networks of living organisms. In the previous research, the HNN constructed by an Integrated Circuit (IC) could generate only a tripod gait pattern which is necessary to perform the locomotion of the microrobot. The microrobot can move six legs independently; thus, the mechanical structure of the microrobot allows the several gaits by changing the driving waveform. The HNN with switchable gait pattern could perform the different locomotion of the microrobot. In this paper, the authors discuss HNN which can switch the gait pattern of the microrobot. HNN can generate two gait pattern such as the tripod gait pattern and the ripple gait pattern which is typical gaits of insects. The tripod is for fast walking and the ripple is for slow walking, respectively. Usually, six cell body models mutually connected by 18 inhibitory synaptic models were required to generate the tripod gait pattern. Also, six cell body models mutually connected by 30 inhibitory synaptic models were required to generate the ripple gait pattern. The authors simplified the network with proposal excitatory-inhibitory switchable synaptic model. The HNN can simplify as six cell body models connected by two excitatory-inhibitory switchable synaptic model and ten inhibitory synaptic models. In addition, two types of gait patterns can be switched using a single external voltage source. As a result, The HNN generates tripod gait pattern with an external voltage of 3.0 V and ripple gait pattern with an external voltage of -3.0 V.