Synchronization of Coupled Pulse-Type Hardware Neuron Models for CPG Model

Saito, Ken; Saeki, Katsutoshi; Sekine, Yoshifumi

doi:10.1007/978-1-4614-0724-9_7

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…In-phase synchronization of P-HNMs. [16].). Therefore, we use the inhibitory mutual coupling to generate the driving waveform which can operate the actuators of hexapod walking biomimetics micro robot.…”

Section: A Pulse-type Hardware Neuron Modelmentioning

confidence: 90%

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Biomimetics micro robot with CMOS IC neural networks locomotion control

Takahama

Tanida

Takato

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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In this paper, we presented the 4.0, 2.7, 2.5 (mm), width, length, height size biomimetics micro robot system which was inspired by ant. The micro robot system was made from silicon wafer fabricated by micro electro mechanical systems (MEMS) technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the insect-like switching behavior. In addition, we constructed the complementary metal oxide semiconductor integrated circuit neural networks (CMOS IC NN) as a locomotion controlling system. The CMOS IC NN utilized the pulse-type hardware neuron model (P-HNM) as a basic component. The CMOS IC NN outputs the driving pulses using synchronization phenomena such as biological neural networks. The driving pulses can operate the actuators of the biomimetics micro robot directly. Therefore, the CMOS IC NN realized the robot control without using any software programs or A/D converters. The micro robot emulated the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms and CMOS IC NN. The micro robot performed forward and backward locomotion, and also changed direction by inputting an external trigger pulse. The locomotion speed was 26.4 (mm/min) when the step width was 0.88 (mm).

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Section: A Pulse-type Hardware Neuron Modelmentioning

confidence: 90%

“…Previously, we proposed the central pattern generator (CPG) model using pulse-type hardware neuron model (P-HNM) [15,16]. CPG model was board level circuit using surface-mounted components.…”

Section: Complementary Metal Oxide Semiconductor Integrated Circmentioning

confidence: 99%

Biomimetics micro robot with CMOS IC neural networks locomotion control

Takahama

Tanida

Takato

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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“…The hardware ANNs are based on a pulse-type hardware neuron model of class II, specifically, a Hodgkin-Huxley model and a Bonhoeffer-van der Pol model [28]. This section describes the circuit diagrams and the basic characteristics of the pulse-type hardware neuron model.…”

Section: Hardware Artificial Neural Networkmentioning

confidence: 99%

“…Meanwhile, the inhibitory synaptic model sums the inhibitory inputs (output voltage of the cell body model v MI and the external input voltage v extinI ). Both cell body models are assigned the same circuit parameters (The synchronization phenomena and oscillatory patterns of the mutually coupled excitatory-inhibitory neuron pair model are provided in [28]). …”

Section: Excitatory-inhibitory Neuron Pair Modelmentioning

confidence: 99%

“…In our multilegged robots, the number of actuators is reduced by a link mechanism, and the gait is controlled by a hardware ANN. The hardware ANN consists of 4 excitatory synaptic models, 16 inhibitory synaptic models, and 8 cell body models for the quadruped robot [26], and 12 inhibitory synaptic models and 4 cell body models for the hexapod robot [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Gait Generation of Multilegged Robots by using Hardware Artificial Neural Networks

Saito¹,

Ohara²,

Abe³

et al. 2018

Advanced Applications for Artificial Neural Networks

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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.

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