Gait Generation of Multilegged Robots by using Hardware Artificial Neural Networks

Saito, Ken; Ohara, Masaya; Abe, Matanobu; Kaneko, Makoto; FumioUchikoba,

doi:10.5772/intechopen.70693

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…The authors speculate that spike firing has significant roles in information processing in the nervous system. We are studying robot control using pulse-type hardware neuron models (P-HNMs) that can output the spike firing (action potential) the same as a biological neuron [43]. Our research aims to develop a simple and efficient control method for robots using the artificial motor nervous system and central nervous systems.…”

Section: Introductionmentioning

confidence: 99%

Active Gaits Generation of Quadruped Robot Using Pulse-Type Hardware Neuron Models

Takei¹,

Morishita²,

Tazawa³

et al. 2021

Biomimetics

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In this chapter, the authors will propose the active gait generation of a quadruped robot. We developed the quadruped robot system using self-inhibited pulse-type hardware neuron models (P-HNMs) as a solution to elucidate the gait generation method. We feedbacked pressures at the robot system’s each foot to P-HNM and varied the joints’ angular velocity individually. We experimented with making the robot walk from an upright position on a flat floor. As a result of the experiment, we confirmed that the robot system spontaneously generates walk gait and trot gait according to the moving speed. Also, we clarified the process by which the robot actively generates gaits from the upright state. These results suggest that animals may generate gait using a similarly simple method because P-HNM mimics biological neurons’ function. Furthermore, it shows that our robot system can generate gaits adaptively and quite easily.

show abstract

Section: Introductionmentioning

confidence: 99%

Active Gaits Generation of Quadruped Robot Using Pulse-Type Hardware Neuron Models

Takei¹,

Morishita²,

Tazawa³

et al. 2021

Biomimetics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors are studying robot control using pulse-type hardware neuron models (P-HNMs). The P-HNMs can output the spike firing (action potential) similar to a biological neuron [13]. Our research aims to develop a simple and efficient control method for robots using the artificial motor nervous system and central nervous systems.…”

Section: Introductionmentioning

confidence: 99%

Non-programmed gait generation of quadruped robot using pulse-type hardware neuron models

Takei

Morishita

Tazawa

et al. 2020

Artif Life Robotics

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In this paper, the authors will propose the active gait generation of a quadruped robot. The theory that quadruped animals unconsciously generate gaits by some system based on neural networks in the spinal cord is widely accepted. However, how biological neurons or neural networks can generate gaits is not clear. To clarify the gait generation method, one of the solutions is using the neuron model similar to the biological neuron. We developed the quadruped robot system using self-inhibited pulse-type hardware neuron models (P-HNMs), which can output the electrical activity similar to those of biological neurons. The P-HNMs consist of the cell body model and the inhibitory synaptic model. The cell body model periodically outputs pulsed voltages; the inhibitory synaptic model inhibits the pulsed voltages. The pulse period can change by varying the synaptic weight control voltage applied to the P-HNMs. We varied the synaptic weight control voltage according to the pressure on the robot’s toes. Also, we changed the angle of the robot’s joints by a constant angle each time the P-HNMs output a pulse. As a result of the walking experiment, we confirmed that the robot generates walk gait and trot gait according to the moving speed. Also, we clarified the process by which the robot actively generates gaits from the upright state. These results show that animals may not use many biological neurons to generate gaits. Furthermore, the results suggest the possibility of realizing simple and bio-inspired robot control.

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Development of receptor cell model with oscillation frequency‐dependent on sensor input intensity

Morishita

Kato

Takei

et al. 2022

Elect Comm in Japan

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This paper developed a receptor cell model that mimics the receptor cells’ function of living organisms. The receptor cell model converts the signal input of the sensor into an oscillating pulse waveform. The oscillation frequency of the receptor cell model varies according to the sensory input intensity. First, the authors construct the receptor cell model by discrete circuit. The circuit was described using circuit simulation and compared with the experimental result. Also, we measure the frequency characteristics utilizing a pressure sensor and an optical sensor. The result shows that the receptor cell model could vary from 300 to 600 Hz according to the sensory input intensity. Second, the authors propose the receptor cell model by the integrated circuit. Experimental results showed that the receptor cell model's oscillation frequency changed from 370 to 890 Hz. As a result, the proposal receptor cell model could output the oscillating pulse waveform dependent on sensor input intensity.

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Gait Generation of Multilegged Robots by using Hardware Artificial Neural Networks

Cited by 9 publications

References 28 publications

Active Gaits Generation of Quadruped Robot Using Pulse-Type Hardware Neuron Models

Active Gaits Generation of Quadruped Robot Using Pulse-Type Hardware Neuron Models

Non-programmed gait generation of quadruped robot using pulse-type hardware neuron models

Development of receptor cell model with oscillation frequency‐dependent on sensor input intensity

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