Synchronization of coupled pulse-type hardware neuron models for CPG model

Saito,; Saeki,; Sekine,

doi:10.1109/ijcnn.2009.5179069

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…In the case of excitatory mutual coupling of the excitatory neuron by the excitatory synapse, the neural networks systems will in-phase synchronization. On the contrary, for inhibitory mutual coupling of the inhibitory neuron by the inhibitory synapse, the neural networks systems will anti-phase synchronization (for example, see [24]). The output port of the HNN was connected directly to the helical artificial muscle wires by copper wires.…”

Section: Active Hardware Neural Network Systemmentioning

confidence: 99%

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Biomimetics Micro Robot with Active Hardware Neural Networks Locomotion Control and Insect-Like Switching Behaviour

Saito

Takato

Sekine

et al. 2012

International Journal of Advanced Robotic Systems

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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 insects. 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 behaviour. In addition, we constructed the active hardware neural networks (HNN) by analogue CMOS circuits as a locomotion controlling system. The HNN utilized the pulse‐type hardware neuron model (P‐HNM) as a basic component. The HNN 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 HNN 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 HNN. 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: Active Hardware Neural Network Systemmentioning

confidence: 99%

“…It is well-known that locomotion rhythms of living organisms are generated by central pattern generators (CPG). Previously, we proposed the CPG model using P-HNMs [23,24]. The CPG model was a board level circuit using surface-mounted components.…”

Section: Locomotion Mechanismsmentioning

confidence: 99%

Biomimetics Micro Robot with Active Hardware Neural Networks Locomotion Control and Insect-Like Switching Behaviour

Saito

Takato

Sekine

et al. 2012

International Journal of Advanced Robotic Systems

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“…This is a nonlinear differential equation model of a bio-inspired system with a continuous time and continuous states (CTCS). A class CTCS bio-inspired model can be generally implemented in an analog nonlinear circuit, e.g., [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…(b) Gait diagram of wave gait (slow). (c) Target synchronization pattern for tripod gait defined by Eq (13). .…”

mentioning

confidence: 99%

Smooth gait transition in hardware-efficient CPG model based on asynchronous coupling of cellular automaton phase oscillators

Takeda

Torikai

2021

NOLTA

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In this paper, a central pattern generator (CPG) model based on asynchronous coupling of cellular automaton (CA) phase oscillators for a hexapod robot is presented. The presented CPG model is composed of the CA phase oscillators whose discrete state transitions are triggered by multiple asynchronous clocks. Then, evaluation functions to quantify synchronization states for target gait patterns in the presented CPG model are introduced. Analyzing the synchronizations using the evaluation functions, this paper clarifies that the presented CPG model is suitable to perform smooth gait transitions for the hexapod robot than a CPG model whose discrete state transitions are triggered by a single clock (i.e., synchronous coupling). The presented CPG model is implemented in a field programmable gate array (FPGA); experiments verify that the hexapod robot mounted with the FPGA, in which the presented CPG model is implemented, can perform smooth gait transitions.

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“…Previously, we constructed the 4.0, 2.7, 2.5 mm, width, length, and height size microrobot fabricating the silicon wafer by micro fabrication technology, reported the rotary-type actuator composed of heat stimulated artificial muscle wires in the robot, and also driving waveform of the robot was generated by using packaged neural networks integrated circuit (NNIC) which was externally connected [18].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

et al. 2014

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In this paper, we will propose the neural networks integrated circuit (NNIC) which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS) microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generates the driving waveform using synchronization phenomena such as biological neural networks. The driving waveform can operate the actuators of the MEMS microrobot directly. Therefore, the NNIC bare chip realizes the robot control without using any software programs or A/D converters. The microrobot performed forward and backward locomotion, and also changes direction by inputting an external single trigger pulse. The locomotion speed of the microrobot was 26.4 mm/min when the step width was 0.88 mm. The power consumption of the system was 250 mWh when the room temperature was 298 K.

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Synchronization of coupled pulse-type hardware neuron models for CPG model

Cited by 11 publications

References 8 publications

Biomimetics Micro Robot with Active Hardware Neural Networks Locomotion Control and Insect-Like Switching Behaviour

Biomimetics Micro Robot with Active Hardware Neural Networks Locomotion Control and Insect-Like Switching Behaviour

Smooth gait transition in hardware-efficient CPG model based on asynchronous coupling of cellular automaton phase oscillators

Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

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