Silicon Micro-Robot With Neural Networks

2019 ◽  
pp. 979-990
Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba

Insect type 4.0, 2.7, 2.5 mm. width, length, height size silicon micro-robot system with active hardware neural networks locomotion controlling system is presented in this chapter. The micro-robot system was made from a silicon wafer fabricated by Micro-Electro Mechanical Systems (MEMS) technology. The mechanical system of the robot equipped with millimeter-size rotary type actuators, link mechanisms, and six legs to realize the insect-like switching behavior. In addition, the authors constructed the active hardware neural networks by analog CMOS circuits as a locomotion controlling system. Hardware neural networks consisted of pulse-type hardware neuron models as basic components. Pulse-type hardware neuron model has same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics, and enables the generation of continuous action potentials. The hardware neural networks output the driving pulses using synchronization phenomena such as biological neural networks. Four output signal ports are extracted from hardware neural networks, and they are connected to the actuators. The driving pulses can operate the actuators of silicon micro-robot directly. Therefore, the hardware neural networks realize the robot control without using any software programs or A/D converters. The micro-robot emulates the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms, and hardware neural networks. The micro-robot performs forward and backward locomotion, and also changes direction by inputting an external trigger pulse. The locomotion speed was 26.4 mm/min when the step width was 0.88 mm.

Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba

Insect type 4.0, 2.7, 2.5 mm. width, length, height size silicon micro-robot system with active hardware neural networks locomotion controlling system is presented in this chapter. The micro-robot system was made from a silicon wafer fabricated by Micro-Electro Mechanical Systems (MEMS) technology. The mechanical system of the robot equipped with millimeter-size rotary type actuators, link mechanisms, and six legs to realize the insect-like switching behavior. In addition, the authors constructed the active hardware neural networks by analog CMOS circuits as a locomotion controlling system. Hardware neural networks consisted of pulse-type hardware neuron models as basic components. Pulse-type hardware neuron model has same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics, and enables the generation of continuous action potentials. The hardware neural networks output the driving pulses using synchronization phenomena such as biological neural networks. Four output signal ports are extracted from hardware neural networks, and they are connected to the actuators. The driving pulses can operate the actuators of silicon micro-robot directly. Therefore, the hardware neural networks realize the robot control without using any software programs or A/D converters. The micro-robot emulates the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms, and hardware neural networks. The micro-robot performs forward and backward locomotion, and also changes direction by inputting an external trigger pulse. The locomotion speed was 26.4 mm/min when the step width was 0.88 mm.


Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba

Hexapod locomotive Micro-Electro Mechanical Systems (MEMS) microrobot with Pulse-type Hardware Neural Networks (P-HNN) locomotion controlling system is presented in this chapter. MEMS microrobot is less than 5 mm width, length, and height in size. MEMS microrobot is made from a silicon wafer fabricated by micro fabrication technology to realize the small size mechanical components. The mechanical components of MEMS microrobot consists of body frames, legs, link mechanisms, and small size actuators. In addition, MEMS microrobot has a biologically inspired locomotion controlling system, which is the small size electrical components realized by P-HNN. P-HNN generates the driving pulses for actuators of the MEMS microrobot using pulse waveform such as biological neural networks. The MEMS microrobot emulates the locomotion method and the neural networks of an insect with small size actuator, link mechanisms, and P-HNN. As a result, MEMS microrobot performs hexapod locomotion using the driving pulses generated by P-HNN.


2016 ◽  
pp. 630-647
Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba

Hexapod locomotive Micro-Electro Mechanical Systems (MEMS) microrobot with Pulse-type Hardware Neural Networks (P-HNN) locomotion controlling system is presented in this chapter. MEMS microrobot is less than 5 mm width, length, and height in size. MEMS microrobot is made from a silicon wafer fabricated by micro fabrication technology to realize the small size mechanical components. The mechanical components of MEMS microrobot consists of body frames, legs, link mechanisms, and small size actuators. In addition, MEMS microrobot has a biologically inspired locomotion controlling system, which is the small size electrical components realized by P-HNN. P-HNN generates the driving pulses for actuators of the MEMS microrobot using pulse waveform such as biological neural networks. The MEMS microrobot emulates the locomotion method and the neural networks of an insect with small size actuator, link mechanisms, and P-HNN. As a result, MEMS microrobot performs hexapod locomotion using the driving pulses generated by P-HNN.


2012 ◽  
Vol 132 (7) ◽  
pp. 1094-1100
Author(s):  
Ken Saito ◽  
Kazuto Okazaki ◽  
Tatsuya Ogiwara ◽  
Minami Takato ◽  
Katsutoshi Saeki ◽  
...  

Author(s):  
Ken Saito ◽  
Shiho Takahama ◽  
Shinpei Yamasaki ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
...  

Author(s):  
Ken Saito ◽  
Kazuto Okazaki ◽  
Kentaro Sakata ◽  
Tatsuya Ogiwara ◽  
Yoshifumi Sekine ◽  
...  

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