Journal of Robotics, Networking and Artificial Life
Vol. 5(4); March (2019), pp. 253–256
DOI: https://doi.org/10.2991/jrnal.k.190220.010; ISSN 2405-9021 print; 2352-6386 online
https://www.atlantis-press.com/journals/jrnal

Research Article

Snake Robot Controlled by Biomimetic CPGs
Damien Blanchard1, Kazuyuki Aihara2, Timothée Levi2,*GEII Deparment, IUT Bordeaux, University of Bordeaux, Talence 33400 FranceInstitute of Industrial Science (IIS), The University of Tokyo, Meguro-ku, Tokyo 153-8505, Japan12

ARTICLE INFO
Article History
Received 14 October 2018
Accepted 18 December 2018Keywords
Snake robot
CPG
biomimetic
spiking neural network
locomotion

ABSTRACTThe design of biomimetic robot is one popular research. To achieve this goal, the reproduction of animal locomotion ismandatory. Animal locomotion is created by the activities of Central Pattern Generator (CPG). CPGs are neural networks
capable of producing rhythmic patterned outputs without rhythmic sensory or central input. We propose a network ofseveral biomimetic CPGs using biomimetic neuron model and synaptic plasticity. This network is implemented on a field
programmable gate array. We designed one unsupervised snake robot using this network of CPG. It is composed of one headwagon followed by seven slave wagons. Infrared sensors are also embedded in the head wagon. This robot can reproduce thelocomotion of one snake.
© 2019 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
The design of biomimetic robot is one popular research. To achieve
this goal, the reproduction of animal locomotion is mandatory.
Animal locomotion is created by the activities of Central PatternGenerator (CPG) that produces alternating bursts [1]. Most rhythmic movements are programmed by CPG networks [2]. CPGs areneural networks capable of producing rhythmic patterned outputs
without sensory input. CPGs in animal locomotion have been studied such as swimming in salamander [3] or lamprey [4], and heartbeat system in leech [5,6].
Usually in robotic field, CPGs are designed using simple neuronmodels [7] or simple oscillators [8], which are not biologicaltime scales and which are considered as bio-inspired systems
rather than biomimetic systems. These models provide sinusoidal oscillations and cannot reproduce biomimetic CPGs whichhave variability in performance. As our aim is to replicate biological behavior, we developed biomimetic CPGs using digitalneuromorphic system.

candidate for our applications. This neural network model has
been designed by Hill et al. [6] and described in Figure 1.

2.2. Simplification of the Model
To design this CPG (Figure 1), Hill’s neural network diagram [6]was used.
The network designed by Hill uses the Hodgkin–Huxley (HH) model[9] and complex equations for synapses. Our first work was to simplify
this model by applying Izhikevich neurons [10] instead of HH model.
Using the short-term synaptic plasticity [11] and the Izhikevich
Regular Spiking neurons, the activity of Hill’s model was reproduced.
Even though the model was simplified, the same behavior andcharacteristics as the biological CPGs was successfully reproduced.
The important point is to allow a variability in the characteristics

2. BIOMIMETIC CPGS IN DIGITAL
NEUROMORPHIC SYSTEM
2.1. CPG Neural Network
The biomimetic CPG is based on the neural network system thatcontrols the heartbeat of a leech [6]. This simple network uses eight
excitatory neurons with 12 inhibitory synapses, making it an ideal
Corresponding author. Email: levi@sat.t.u-tokyo.ac.jp*

Figure 1 | Diagram of the Segmental Oscillator of the Heartbeat of the Leech.
The modelling is from Hill et al. [6]. N1–N2 and N3–N4 are two elemental
oscillators. N1, N3, N5, and N6 are controlling the left part (blue), N2, N4,N7, and N8, the right part (orange). The 12 black dots represents inhibitory
synapses with short-term plasticity. All neurons are excitatory ones.

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254

Table 1 | Utilization of the resources for the implementation ofbiomimetic CPGs in CMOD A7-A35 board

driver. Motors are DG01D with a 0.8 kg.cm couple and a no load
speed of 90 rpm. Figure 3 shows the 3D structure of the wagons.

Kintex-7

Utilization

Available

Percentage

LUTFF
BRAM
DSP

7786
3299
5040

20,80041,600509037.43
7.93
100
44.44

All the distance between the different wagons and the possible
movement angle can be tuned with mechanical structure. Figure 4
shows how the tuning is possible with the orange screws.
The design of electrical connection and components for the locomotive and for the wagon is described in Figure 5.
The locomotive system has three IR sensors for detecting thedifferent obstacles and then send stimulus to CPGs for modifying duty cycle and period. The tuning of the duty cycle allows
the movement and the tuning of period allows the speed or thestop of the robot.

4. RESULTS
Figure 2 | Two different CPGs with different period (1.8 and 7.8 s). The
output are from the scope.

of the CPGs. Percentages of variability can be controlled by adjusting
the parameters of short-term synaptic plasticity.

Height CPGs were implemented (one for the locomotive and sevenfor the wagons). Enable input are connected to each wagon tochoose which ones are working for simulating a lesion of the spinalcord. The CPGs are connected for allowing forward locomotion

By changing the parameters of the inhibitory synapses, it is possibleto obtain different periods and cyclic ratios. This is very important especially for closed-loop experiments and to reproduce locomotion activities for robotics. A single parameter pair manages
the modification of the period and of the duty cycle. The possible
period interval in seconds is quite wide [0.3 and 35 s].

2.3. Implementation in Digital
Neuromorphic System
Biomimetic CPGs [12–15] are implemented in a digital platform: CMOD A7-A35 Xilinx Artix-7 Field Programmable Gate
Array (FPGA). The neural network implementation architecture operates on a single computation core. This real-time digital system requires few resources and low power consumption[16–19]. Table 1 summarizes the stated resources and Figure 2
describes two CPGs with different periods.
As we implemented the biomimetic CPGs in the FPGA board,
we design the structure of the snake robot which embedded theFPGA board.

3. SNAKE ROBOT
3.1. Design
The snake robots is composed of seven wagons (like spinal cord)and one locomotive (mimics the brain).
Biomimetic CPGs will control the different motors of the wagons.
As the output of the FPGA board is 3.3 V with low current, apower driver for the motors and a VHDL module for converting
the CPGs to Pulse Width Modulation (PWM) to control motor
speed needs to be added. The PWM frequency should be between
100 Hz and 200 kHz. The duty cycle of the PWM controls thespeed of the motor. Pololu_DRV8835 was used for the power

Figure 3 | 3D view of the wagon design.

Figure 4 | The orange screw allows the tuning of distance of the different
wagons but also the angle for the movement.

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like we can find in animal locomotion [20]. Figure 6 describes this
CPG network and the forward locomotion [19].
The final snake robot is 59 cm long and works with real-time biomimetic CPGs and mimics the snake locomotion. Figure 7 shows
the final snake robot picture.

255

5. CONCLUSION
This article describes the design of a snake robot. This robot is controlled by real-time network of biomimetic CPGs. This system canbe used in robotic applications [21] for a closer behavior to biological animals. It can also be used in bio-hybrid robotics (biomimetic CPGs can be replaced by in vitro cell culture). We previously
showed that our system can be connected to biological neurons[22,23]. Another advantage is that the real-time systems can simulate multiple CPG topologies and simulate different hypothesis andprotocol treatments for biomedical applications such as spinal cord
injury [24] and neuroprosthetics [25,26].

ACKNOWLEDGMENT
Authors thank Social Cooperation Program for Brain-Morphic AI
to Resolve Social Issues, IIS, University of Tokyo.

REFERENCES
Figure 5 | On the left, the electrical circuit of the wagon. On the right, theelectrical circuit of the locomotive with the FPGA board.

Figure 6 | Forward locomotion [19] with two examples of speed using achain of 7 CPGs for the seven wagons. Time axis is 1 s by division. Signal
are the left neuron (N0, N2, N4, N6, N8, N10 and N12) of each CPG.

Figure 7 | Picture of the snake robot with the locomotive and the sevenwagons.

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Authors Introduction
Mr. Damien Blanchard

Prof. Kazuyuki Aihara
He obtained a 2-year technical degree
in electronical engineering and computer sciences at Bordeaux Instituteof Technology in 2007. From 2008 totoday, he is working as electronicaldesigner at IUT Bordeaux at Univ. ofBordeaux, France.

Dr. Timothée Levi
He received the PhD degree inElectronics at Univ. of Bordeaux in2007. From October 2009 to today, heis an Associate Professor at IMS lab.,
Univ. Of Bordeaux, France. During
this period, from September 2013
to August 2015, he was a Visiting
Research Fellow at University ofTokyo, Japan. From April 2017, Heis Project Associate Professor at The
University of Tokyo. His researchinterest deals with neuromorphic engineering which aims at
designing and using integrated circuits which components andarchitecture are neuro-mimetic for bio-hybrid experiments.

He received a B.E. degree in electrical engineering and PhD degree
in electronic engineering from theUniversity of Tokyo, Japan, in 1977and 1982, respectively. He led theERATO (Exploratory Researchfor Advanced Technology) Aihara
Complexity Modelling project byJST (Japan Science and Technology
Agency) from 2003 to 2008 and the FIRST Innovative
Mathematical Modelling project by JSPS (Japan Society for thePromotion of Science) through the FIRST (Funding Programfor World-Leading Innovative R&D Science and Technology)
program from 2010 to 2014 by CSTP (Council for Science andTechnology Policy). He is studying mathematical theory formodelling complex systems and its wide-ranging transdisciplinary applications in science and technology from the viewpoint of mathematical engineering and chaos engineering. On
the applications side, he is working to bridge neuroscientific andclinical studies and next-generation AI by mathematical modeling and analyses of brain dynamics to realize neurointelligence.