biomorph snake robot

A B I O M O R P H S N A K E L I K E R O B O T A N T H O N Y H S I A O

A biomorph snake-like robotA biomorph snake-like robot

Research, Development and Behav iour Research, Development and Behav iour

by

Anthony Hsiao

Department of Electrical and Computer Engineering, National University of Singapore

Department of Electrical and Electronic Engineering, Imperial College London

Imperial College London, National University of Singapore, July - August 2006 1


AbstractAbstract

The field of robotics is as buzzing as never before, as new technologies allow

for more computing power to be packed inside ever more compact

autonomous systems. Much of research is concerned with developing ever

more capable humanoid or animal-like bio-inspired robots, which become

more alike their natural counterparts thanks to ever more capable digital

systems.

Analogue biomorph robots however underlie a different design paradigm, and

were introduced in the mid 1990s. They have particular appeal to scientists,

engineers and toy manufacturers due to their ability to survive in unstructured

environments and display emergent (unexpected, but 'desirable') behaviours,

while requiring a minimal number of electronic components.

This report describes the research and development carried out towards

creating a biomorph snake-like robot. Its analogue nervous network controller

makes use of an equivalent of only 40 transistors in total, in a spinal-chord-like

nervous system architecture, which is sufficient for the 9 degree of freedom

robot to display snake-like undulating motion. Furthermore, the robot displays

emergent behaviour when used in a slightly different configuration: Turned on

its side, the robot moves and crawls like a worm, and is able to climb out of an

enclosure, which is higher than the robot itself.

There is a lot of scope and room for future work and research to be carried out

on the developed robot, and indeed on the relatively new academic field of

biomorph robotics itself.



AcknowledgementsAcknowledgements

Thank you to all those people who have helped me get this far and further,

both academically and otherwise.

In particular, i would like to thank Dr. Christos Papavassiliou for enabling me

to participate at the pilot exchange program, and Adrian Hawksworth and

Susan Tan for organising the exchange so well, and helping me out and

looking after my welfare all along.

Many thanks to Dr. Vadakkepat for accepting me as a summer exchange

student, for his flexibility, support, direction giving and enthusiasm towards

my field of work. My appreciation also goes to Prof. Al Mamun, who was the

second supervisor of the project.

I would also like to thank Dr Tang Kok Zuea, Mr Tan Chee Siong, Li Yu and all

other members from the Mechatronics and Automation Laboratory for

welcoming me and for their kind support.

I would particularly like to thank Tan Shin Jiuh for all his help and his

undivided attention, whenever i needed it. Although he would be too humble

to admit it, he has guided and advised me on too many occasions, and without

him, my robot would probably not be crawling at all. Thank you for the

inspiring and motivating conversations over tea, with little sugar.

I would like to thank my parents, Wendy and Tien-Wen for their unconditional

support and for opening so many doors for me.

Finally, my thanks go out to all those friends i met during my stay in

Singapore, who have truly made the experience worthwhile. In particular, i

would like to thank Alvin F. Law, Lester t. M. Poon, Colin W. Enderoud and

Francesco C. Mazzocchi-Alemanni. Special thanks goes out to Gita M. Tanna,

for her continuous support and passion, and for choosing such an appropriate

name for the robot. May we all remember the times we spent together in

Singapore, transforming our lives, unleashing our minds!



Table of ContentsTable of ContentsINTRODUCTION................................................................................................................. 5

RATIONALE – SO WHY BUILD A BIOMORPH SNAKE-LIKE ROBOT?...................................................... 5AIMS AND OBJECTIVES........................................................................................................... 7

Aims........................................................................................................................................ 7Objectives................................................................................................................................8Key challenges........................................................................................................................8

BACKGROUND...................................................................................................................9SNAKES, VERTEBRATES AND THEIR NERVOUS SYSTEM......................................................................9

Lateral Undulation.................................................................................................................. 9Concertina ............................................................................................................................10Sidewinding ......................................................................................................................... 10On vertebrate locomotion and their Central Nervous System............................................ 10

NERVOUS NETWORKS...........................................................................................................12The Nv Neuron..................................................................................................................... 12The Bicore.............................................................................................................................13The Suspended Bicore..........................................................................................................13The Master-Slave dual Bicore.............................................................................................. 15Implex Feedback in Nervous Network Controllers............................................................. 15

THE ROBOT.......................................................................................................................18ABSTRACTION..................................................................................................................... 18ANATOMY..........................................................................................................................20MODELLING AND SIMULATION................................................................................................22

Lateral Undulation or general wave-like motion.................................................................22MECHANICAL DESIGN.......................................................................................................... 24ELECTRONIC DESIGN............................................................................................................ 27

General Circuit Design Guidelines.......................................................................................27The Pacemaker..................................................................................................................... 29The Motor Neuron Module ................................................................................................. 32

EVALUATION....................................................................................................................35NORMAL OPERATION............................................................................................................35

Discussion............................................................................................................................. 38WORM-LIKE OPERATION....................................................................................................... 41

CONCLUSION................................................................................................................... 46FUTURE WORK.................................................................................................................47REFERENCES.....................................................................................................................48APPENDIX I – LIST OF COMPONENTS.......................................................................... 50



Int ro duc t ionInt ro duc t ion

This report describes the work carried out during the Undergraduate Research OPportuntiy

(UROP) exchange placement between the faculties of engineering at Imperial College London

(IC) and the National University of Singapore (NUS), at the Centre for Intelligent Control (CIC)

at NUS, under the supervision of Assistant Professor Dr. Prahlat Vadakkepat.

From the three core competencies of the robotics research group, humanoid soccer robotics,

cooperative (soccer) robotics and biomorph robots, the latter was chosen as a field to pursue a

project in. The task to explore the marriage of biomorph robotics and nervous network control

with the design of a robotic snake emerged and is described in the following sections of this

report.

At first, a brief Introduction to the topic, starting with the Rationale for pursuing biomorph

robotics and the concrete Aims and Objectives as well as Key Challenges of this project, is

given. This is followed by an overview of relevant Background material, in which the two key

areas Snakes, vertebrates and their nervous system and some theory on Nervous Networks are

described. The following section on The Robot itself draws together key inspirations gained

from preceding sections, and the Abstraction performed as well as the robot's Anatomy,

Modelling and Simulation carried out to verify its operation and its Mechanical and Electrical

Design are described in detail. Finally, the work is assessed critically in the Evaluation, and

some Future Work is suggested, after the Conclusion rounded off this report.

Rationale – So why build a biomorph snake-like robot?

Research on robotics is more diverse than ever before, with reports of successful new and ever

more complex and capable robots making the headlines. The improvement in technology,

continuous miniaturisation and ever more powerful information processing capabilities have

revived the field and it has picked up momentum. While most robots employed in industry and

manufacturing are merely sophisticated purpose made machines, and relatively dumb, industry

as well as academia have been making good progress on intelligent and autonomous robots

and systems. Existing robots include toy robots such as SONY's robotic dog AIBO or the

famous Robosapien by Wow Wee Ltd., Honda's humanoid robot ASIMO (which is employed

at the Honda Headquarters as a receptionist!) or various football playing robots such as the



FIRA RoboWorld Cup winning Manus-1 humanoid, developed at the National University of

Singapore. The military have developed numerous autonomous Unmanned Aerial Vehicles

(UAV), which can be thought of as autonomous purpose built flying robots. While recent

trends and developments have been very promising, the scientists and engineers are still far

away from creating anything close to human-like intelligent machines, let alone robots that can

be integrated into our society; scenarios as portrayed in films such as 'AI' (Spielberg, Cubrick)

or 'I, Robot' (Proyas) where humanoid robots play an integral role in our future lives, remain

science fiction, for now.

The benefits to man of such robots are clear, and arguably, it is worthwhile pursuing research

in this area; at this stage of robot development and sophistication, dangers and potential

threats they could pose to humanity are best left to writers and philosophers, discussing these

here would go beyond the scope of this work.

However, there is a different school of thought, pioneered by Mark Tilden and his paper on

'Living Machines' [20], which explores the creation of robots as an attempt to create artificial

life, starting from simple creatures. Rather than creating complex humanoid (or otherwise)

purposefully build slave or helper robots, building living machines is about creating robots that

display the basic characteristics of simple living creatures, which do not necessarily have to

have useful (to the human) abilities (yet).

In particular, these so called biomorphs are autonomous, analogue, electro-mechanical devices

that are biologically inspired and built using minimalistic circuitry without microprocessors,

which will be explained in more detail in the section on Nervous Networks. These creatures

can display complex behaviours, resembling that of real (simple) animals through their highly

non-linear interaction with their environment, and their simple analogue nature allows for an

adaptivity and survivability that 'normal' digital robots can hardly match. These robots, it is

hoped, can also help to gain a better understanding of simple biological creatures and the

origin of their behaviour, or at least provoke a discussion on whether machines can be alive.

Therefore, studying biomorphs is very worthwhile indeed, both from a biological as well as

from a robotics point of view.

Snakes have fascinated people for millennia, with their smooth, seemingly effortless and

elegant motion which captivates observers, and their deadly poison and threatening

appearance, which still frightens many people. However, snakes share a long and relatively



well documented history with man, including appearances in the old testament, ancient

Egyptian mythology or Chinese medicine.

However, snakes still have a lot to offer, and studying serpentine (i.e. snake-like) locomotion

and the underlying neural mechanisms is believed to fill gaps in evolutionary knowledge. It is

established knowledge that terrestrial legged locomotory gaits evolved from aquatic swimming

gaits, however details of this transition are still subject to controversy. Decoding the underlying

mechanisms of serpentine locomotion in itself can be interesting, but is also hoped to shed

light on the changes to the neural mechanisms that were necessary to enable life to make the

transition from aquatic swimming to terrestrial walking.

To date, most commercial applications that have derived from snakes have been in the luxury

goods industry, through skinning them and making expensive shoes, bags or purses. However,

serpentine locomotion can potentially find commercial applications in different fields of

robotics, where 'conventional' robots fail. Successful snake-like robots have many advantages

in terms of size or shape over wheeled and legged robots, for example in environments with

tight spaces, long narrow interior traverses such as pipelines, nuclear power plants or loose

terrains such as earthquake sites.

Advancing the studies of snake-like robots is indeed worthwhile for various reasons, although

the work as this stage is foundational and far from to being concerned with different robotic

applications.

Aims and Objectives

The initial task of exploring biomorph robotics and the application of nervous network

controllers on a snake-like robot is manifested in the following three project aims, which are

hoped to be achieved through completing the following objectives.

Aims

To build a simple, autonomous snake-like robotic platform to study nervous network

control for snake-like motion

To investigate a nervous network configuration able to achieve snake-like motion on

this robotic platform

To provoke the reader's thoughts on life and artificial life manifested through robots



Objectives

1. Devise, and verify through simulation, a simple and realisable physical model for a

robot capable of displaying snake-like motion

2. Seek inspiration from biological systems and their nervous system to design a nervous

network controller to realise the physical model above

3. Manufacture hardware and assemble the robotic platform

4. Calibrate the controller to suit the underlying robotic platform

5. Assess the survivability of the artificial life form created

Key challenges

The existence of snakes and other snake-like creatures shows that it is possible to achieve this

kind of complex behaviour through neural control. The challenge for biomorph robotics is to

reveal or propose bio-inspired models of these underlying mechanisms, and to apply them to

real robots. As will be described in the section on Nervous Networks, a critical factor for

success is the harmonic interplay between robot body and controller, and unsuitable

mechanical design can deny an otherwise sound controller to be successful. For this heavily

time constrained project, the key challenges are believed to be as follows:

To find a simple enough biological model for an artificial nervous system (nervous

network) capable of achieving the desired behaviour

To design an appropriately simplified nervous architecture for the robot body

To complete the work, or achieve key milestones towards completion of the project, in

the short amount of time



BackgroundBackg round

There are two key areas of research that this work is sourcing from, the study of serpentine, i.e.

snake-like, locomotion and the field of biomorph robotics. The following section describes

relevant background material on both topics.

Snakes, vertebrates and their nervous system

The snake is a vertebrate, an animal with a backbone, and has the largest number of vertebrae

of any animal: between 100-400 vertebrae depending on the species. Unlike most other

vertebrae, their skeletal form and structure is quite simplified and only has three types of

bones: skull, vertebrae and ribs. This simplicity of a repeated structure and the relatively limited

motions between adjacent pieces can be compared to robot modularity, and are worthwhile to

keep in mind for later mechanical design.

Although snakes and other limbless animals have been studied for centuries, little research has

focused on the detailed mechanics of serpentine locomotion. However, qualitatively, there are

several broad classes of limbless locomotion, including concertina, lateral undulation or

sidewinding, amongst other less common forms, described below.

Lateral Undulation

The most frequently used form of snake locomotion is called

lateral undulation, which is also the most primitive, and is what

most people can relate to, when thinking of snake motion.

During lateral undulation, a wave propagates down the snakes

body, which pushes against irregularities of the ground it is

moving over to generate a forward force strong enough to

overcome sliding friction, as shown in Figure 1. All points

along the body of the snake follow a more or less identical

sinusoidal path [8]. It is unsuited for smooth, low-friction

surfaces and narrow corridors, and not suited for shorter and

stouter, or large heavy-bodied snakes.


Figure 1: During lateral undulation, the snake uses continuous sliding

contacts to propel the body


Concertina

Concertina locomotion is usually performed in confined spaces

or tunnels, or other places where large amplitude undulation

cannot be performed, like tree trunks. During it, a part of the

snake's body is stable, for example wrapped around a trunk or

spread inside a tube, while the rest of the body is moving

forwards. It is best summarised by Figure 2. This motion

derives its name from the accordion like instrument, for

obvious reasons.

Sidewinding

Sidewinding is a type of locomotion most often used by snakes that move across lose grounds,

such as desert sands or tidal mud flats. The method of movement is derived from lateral

undulation, and is very similar, in spite of appearances. It is like lateral undulation in 3D, where

there are only a few points of contact between

the snake and the ground, where the contact is

not sliding, but static, rather like rolling a wheel

or bent rod over the surface. Thereby, the head

can appear to leap forwards. Because the

snake's body is in static contact with the ground,

imprints of the snake body can be seen in the

tracks, and each track is almost exactly as long

as the snake, as shown in Figure 3. A through

review of snake motion can be found in [3].

On vertebrate locomotion and their Central Nervous System

Apart from studying snake's different locomotion gaits, it is necessary to have a closer look at

where these are generated: the nervous system.

The vertebrate central nervous systems (CNS) generally consist of two parts, the brain and the

spinal chord. Therein, the brain is generally attributed to, amongst others, higher level,

cognitive and decision making functions, for humans more so than for other vertebrates, while

the spinal chord performs routing and is responsible for our reflexes and motor functions. The


Figure 2: Concertina locomotion is usually used in confined spaces

Figure 3: Sidewinding is like lateral undulation in 3D, with rolling contacts between a few points of the body

and the ground


different animal locomotive gaits can be very complex and move many DOF through rhythmic

excitation of muscles.

This type of motion stands in stark contrast to most man made machines' forms of motion,

which tend to be continuous and have few DOF, like propellers, jet engines or wheels. Animal

locomotion needs to be well coordinated in order to work, and indeed it is, as we can see every

day in real life or on TV. In general, locomotion is controlled by the interaction of three

components: 1. spinal central pattern generators (CPGs), 2. sensory feedback, and 3.

descending supraspinal control [6].

CPGs are circuits which can generate rhythmic activity without rhythmic input, and can often

be initiated by simple electric or physiologic stimulation. These CPGs are located in the spinal

chord, distributed across different oscillatory centers. The lamprey, and eel like fish, for

example has a CPG chain of approximately 100 segmental oscillators distributed from head to

tail [6].

Sensory feedback is essential for shaping and coordinating the neural activity with the actual

mechanical movements. The main sensory feedback to the CPGs is provided by built-in sensory

receptors in joints and muscles. It is especially important in higher vertebrates with upright

posture such as mammals, because the limbs of those vertebrates play an important role in

posture control (i.e. supporting the body) in addition to locomotion. A whole set of reflexes

exist to coordinate neural activity with mechanical activity, which we observe literally with

every blink of an eye [6].

Supraspinal control, then, is the control that initiates and modulates locomotion through

descending pathways from higher order locomotor centers inside the brain or the spinal chord.

Put simply, when a certain locomotive gait is required, either dictated by a reflex or a higher

cognitive decision, a command sent by one or more of these locomotor centers propagates

down (e.g. from head to tail) a signal that addresses the right CPGs and thus initiates the

desired gait [6], [16].

The above paragraphs capture the relevant aspects of snake and vertebrate locomotion, and,

simplified, their underlying neural control mechanisms. By now, it should have become clear,

that the existence of reflexes, especially complex reflexes such as suddenly jerking back when

getting burned, demonstrate, that motion, or locomotion, does not necessarily require high

level cognitive processes, but can be a product of coordinated, but mechanistic, interaction of



CPGs within a nervous system.

Nervous Networks

Nervous network technology is based on a patent by Dr. Mark Tilden, which describes a self-

stabilizing control circuit that utilizes pulse delay circuits for controlling the limbs of a limbed

robot [21]. In the following sections, the reader will be introduced to the Nv Neuron, the

Bicore, the Suspended Bicore, the Master-Slave dual Bicore configuration and Implex

Feedback in nervous network controllers.

The Nv Neuron

The basic pulse delay circuit acts as an artificial neuron, also referred to as Nv Neuron (nervous

neuron), and is the basic building block of a nervous network controller, as shown in Figure 4.

It only consists of one resistor, one capacitor and an inverter (depicted with Schmitt trigger, but

that is not a necessity), and has a time constant =RC . Qualitatively, its operation is as

follows:

1. Assuming a stable initial condition, where the input has

been low for a long time, so that the capacitor is

uncharged, the input to the inverter is low, the output is

high, and there are no currents flowing.

2. The input voltage changes from GND to Vcc and stays high. It will instantaneously raise

the resistor voltage to high, which is also the input to the inverter, so the output is low.

3. Now, as there is a voltage across the resistor, current starts flowing, charging the

capacitor. The voltage across the resistor decreases exponentially as the voltage across

the capacitor increases (assuming no current is flowing into the inverter).

4. Once the input voltage to the inverter has decreased to its switching threshold after a

time tp, it is pulled down low, and the output is high.

5. Resetting the input to low does not have an effect on the output of the neuron after it

has finished 'firing', whereas resetting the input while the neuron is firing would

immediately reset the output.

Essentially, what happened is that a step input to the neuron has stimulated it to fire an active

low pulse at its output, with duration tp, which is directly proportional to the time constant of


Figure 4: a nervous neuron


the circuit, such that t p=RC ln V iV th

. A more detailed analysis of this circuit is omitted here,

as the actual circuit does not behave exactly as the equations predict, as will be outlined later,

and the qualitative understanding of it being a pulse delay element is sufficient to proceed. The

interested reader may consult [23], [9], [18] or [26] for more detailed information.

The Bicore

Of more interest here than the simple Nv neuron is the so called bicore, a self oscillating

arrangement of two Nv neurons connected together in a circular topology, as shown in Figure

5. Qualitatively, the analysis is similar to that of a single Nv neuron, but with the difference

that the input applied is not a step input, but a square wave. Let's consider a single Nv neuron

again, with a rectangular wave at its input:

1. Initially, the input is low, and the output is high.

2. The input goes high, the neuron fires and the output

is low for a time tp (proportional to the neuron's

time constant), and goes back high.

3. The input goes back low after a time greater than tp,

where nothing happens to the output, it stays high.

4. If the above procedure repeats, it can be appreciated, how the output of the Nv neuron

is a rectangular wave with a certain period and duty cycle, that depends on the

rectangular input wave.

5. Now, if the input to the Nv neuron comes from another Nv neuron, which in turn is

driven by the original neuron, i.e. they are arranged in a ring like topology as shown in

Figure 5, then the output at either of the two neurons is a square wave (in antiphase), if

the capacitors and the resistors each have the same size. (changing the ratios of the two

time constants of the two neurons can change the duty cycle and period of the bicore,

but this is not of interest here).

The Suspended Bicore

A modification to the bicore is the so called suspended bicore, as shown in Figure 6.

Essentially, the suspended bicore has the same circuit as the normal bicore, but with the two

resistors connected together, instead of being grounded, generating square wave at the inverter


Figure 5: The Bicore is an arrangement of two Nv neurons connected in a circular

topology


outputs. A brief but more thorough analysis of its operation here (a comprehensive description

of the circuit can be found in [2]):

1. Initially, both capacitors are completely discharged, Vi1 is

low, therefore Vo1 is high, and Vi2 is high, therefore Vo2

is low. C1 and C2 are equal.

2. There is a potential difference across the resistor, so a

current starts to flow from Vi2 to Vi1, charging C1 and

C2.

3. The voltages Vi1 and Vi2 increase and decrease

respectively exponentially, until one of them reaches the threshold voltage (Vcc/2) and

triggers its respective inverter to change the output. For arguments sake, let Inv1

change (maybe only a fraction of a second) first, and Vo1 goes low, pulling Vi2 down

with it, which triggers Inv2, so that Vo2 is now high.

4. Assuming that voltages only move within the range of Vcc and GND, Vi1 and Vi2 are

now high and low respectively, and the whole process repeats, but with current flowing

the other way through the resistor, discharging the capacitors.

It can be appreciated, how the suspended bicore is also generating square waves at its neurons'

outputs, which have a period depending on the RC values. However, there are a few interesting

properties of this suspended bicore that are worth pointing out:

1. According to the mathematical model to describe the operation of the suspended

bicore, it actually should not work if C1 and C2 are equal, because the voltages at Vi1

and Vi2 would symmetrically approach Vcc/2 asymptotically, so never actually reach it.

2. In reality, there is some noise on the wires, which can indeed be just large enough to

push one of the input voltages over the inverters threshold.

3. In practice, no two Capacitors are of identical capacitance, so inevitably, one of them

will reach Vcc/2 before the other one.

4. Even if the capacitors are not the same, the duty cycle of the circuit is still 50%.

5. When the inverters just change their output, Vo1 and Vo2 are actually -Vcc/2 and

1.5Vcc/2, which is why input protection diodes, which are common in commercially

available logic chips, are essential to protect the device from over- or undervoltage.


Figure 6: The suspended bicore


6. The exact period of this circuit's output signal is very sensitive, and depends on the

configuration within which the suspended bicore is used. It is of the order of

magnitude of RC.

The Master-Slave dual Bicore

A popular use of the suspended bicore is in the Master-Slave configuration, Figure 7, where the

suspended bicore can be thought of as a pacemaker to the rest of the circuit. This arrangement

can be thought of as a normal bicore, with it's normally grounded resistors connected to the

output of a suspended bicore. The master is the suspended bicore, oscillating away at a fixed

period (which is different to when the same

suspended bicore was running in isolation), while

the slave is the normal bicore, with its operation

dictated by the master via the coupling resistors.

Again, a detailed account of the operation of this

circuit can be found in [2], and will not be

reproduced here. A qualitative understanding of

its operation is sufficient.

Both bicores produce square waves at their respective outputs, where the outputs of the slave

have a delay ∆t on the master outputs, that is given by the coupling resistors and capacitors (or

can be measured). Their frequencies and duty cycles are otherwise the same. It can be

appreciated, that the period of this circuit, i.e. the period of the master bicore, is shorter than if

the master was operated on its own, because the coupling resistors introduce an extra path for

the charging and discharging currents, thereby accelerating the charge or discharge of the

capacitors, and hence resulting in a faster oscillation, as the threshold voltages are reached

earlier. The master-slave arrangement is commonly used to control quadruped walking robots,

i.e. robots that have four legs.

Implex Feedback in Nervous Network Controllers

Using the basic building block of nervous networks, the Nv neuron, many different

conflagrations and control networks can be achieved, and popular arrangements go beyond the

bicore, but include networks with four, six, eight or more Nv neurons. But what is it, that make

Nv nets so special?


Figure 7: Master-Slave configuration with a suspended bicore


First of all, they are simple and cheap. Using a few standard components, complex control

patterns can be generated using nervous network technology.

Furthermore, these networks are so sensitive on their topology and external connections, in a

way that allows them to display adaptivity. As illustrated on the example of the master-slave

configuration, where the presence of the slave bicore reduces the oscillation period of the

suspended bicore (compared to isolated operation), the nervous network controllers react to

the load connected to their outputs; the slave bicore can be thought of as a non-linear load on

the suspended bicore.

The (non-linear) load of a nervous network controller, which commonly comprises mechanical

actuators such as motors, has an effect on the operation of the controller itself, which in turn

controls the way the load behaves. Also, the environment that the actuators interact with has

an effect on the actuators itself, for example moving up a steep slope has a different loading

condition on a motor than moving over horizontal ground. Then, for example, a motor would

draw more current from the supply rails, which has an effect on the Nv neurons, which are also

fed through the same supply rails (supply rail feedback). All this together results in a complex,

non-linear interplay between the controller of the robot and its environment, as depicted in

Figure 8, and it is adaptive, because the controller can react to the environment, e.g. by

increasing the period of oscillation of a pattern generator.


Figure 8: Complex, adaptive interaction between the nervous network controller and its environment

Actuators

Controller

Actuators

Environ-ment

Drive actuators

Move robot within environment

Environment is loading the actuators

Actuators are a load to the controller


The nervous network controller can be thought of as trying to converge towards an optimal

state, wherein the optimality condition is determined by the mechanical body of the robot, the

topology of the nervous network and the environment encountered. It is important to ensure

that the mechanical body is such, that it has a solution for the nervous network to converge to.

A badly (for example asymmetrically) designed robot body would not have a solution gait that

the nervous network could converge to.

Under certain circumstances, where the controller is in harmony with the mechanical

configuration and the encountered environment, a robot with a nervous network controller can

display emergent behaviour, which is unexpected, but desirable behaviour. This could include

the ability for a robot to climb or avoid obstacles, or to display other characteristics usually

attributed to living creatures such as dynamic group behaviour.

Furthermore, in the words of the inventor,

“Autonomous legged creatures, to move and react effectively within their environment,

require precise synchronizing control circuitry and the ability to adapt to new conditions

as they arise. [...] attempts to create such a device have involved elaborate

arrangements of feedback systems utilizing complex sensor inputs and extensive

control and sequencing circuitry hard-wired to one or more central processors. Such a

robot is extremely complex and expensive to build, even to accomplish very simple

tasks. Moreover, due to the complexity of such a device and its heavy reliance on a

central processing system, power requirements are enormous, and a relatively minor

problem, such as injury to a limb, is likely to cause total system failure. Such walking

devices are accordingly impractical for other than experimental or educational

uses.”[21]

Nervous network technology can be very powerful, and has several unchallenged advantages

over 'conventional' microprocessor control used for robotics. Through its adaptivity, it gives a

robot the most basic but also most important ability, which is the ability to survive in an

unstructured environment.



T he Rob otT he Robot

The underlying design principle for the robot is simplicity. The reason for choosing nervous

network technology to control it is twofold, its ability to display complex and adaptive

behaviours on one hand, and its tremendous simplicity on the other. The following sections

first describe abstractions performed to simplify the problem, the robot's anatomy, i.e. its

conceptual architecture, performed simulations to verify this architecture, and finally the

robot's detailed electrical and mechanical design.

Abstraction

In order to address the problem of building a biologically inspired snake like robot, an

appropriate abstraction from a real snake has to be performed. In particular, one motion

scheme shall be determined for implementation, and the robot's complexity in degrees of

freedom and approximate dimensions be chosen.

The section above introduced three common forms of snake locomotion, namely lateral

undulation, sidewinding and concertina motion. Out of these, sidewinding is too complex for

simple implementation, as it would require the robot to be able to leap forwards, and careful

balancing would have to be applied for it to move in 3D. It would also place a lot of emphasis

on mechanical design, and therefore goes beyond the scope of this work. Concertina is similar

to lateral undulation in some ways, but also more complex to implement on a robot. It would

also restrict the robot's motion to confined spaces. Lateral undulation however is potentially

suitable to be implemented using a simple controller, as it is the most simple motion scheme

observed on real snakes. Thus, the robot shall imitate lateral undulation as target motion

scheme. Indeed, not only one particular curve, but any snake-like undulating motion of the

robot’s body shall be a satisfactory form of motion.

Intuitively, any snake-like robot will consist of a chain of links coupled or hinged together. The

degrees of freedom the robot has is thus the number of moving joints between these links;

clearly, the more degrees of freedom it has, the higher its complexity is in general, and in this

case of a snake-like robot, the more smoothly the robot will be able to move. The robot body

with its links can be thought of as sampling a smooth snake-like curve, and the more samples

there are, the more closely to the original (i.e. the snake) it can be, and surely enough, in spirit



of the sampling theorem, there is a certain minimum number of links, below which the robot is

guaranteed to fail. However, it is not attempted here to find an equivalent expression.

Here, as the length or size of each link is not yet specified, the number of links in the model

does not affect the total length of the robot, as shown in Figure 9. In choosing an appropriate

number of links, there is a trade off between robot (and circuit) complexity and motion

smoothness. (An impressive resource for robotic snakes is given by [22], and Dr. Miller's S2

snake robot has a sufficient total of ten degrees of freedom allowing the robot to move very

smoothly). An educated guess is made, and nine links and thus nine degrees of freedom is

chosen, which is believed to be more than sufficient to allow for smooth snake-like robot

motion.

Intuitively, snake-like undulating motion can be achieved by such a chain of links and joints by

propagating a wave down the chain. Nervous network controllers are simple controllers, where

commonly used control signals include 'on-off'

(i.e. Vcc or GND) or 'forwards-backwards' signals,

suitable to drive DC motors, and are 'tapped' off

the output nodes of the network. In this

application, motors could be used directly as

joints, and the nervous network be used to

control these motors, e.g. switching them on and

off, or driving them clockwise and anticlockwise.

Such a chain of linked motors could then be used

to drive the robotic snake body, where each of

the joints in Figure 9 could represent a motor.

The challenge then is to determine the correct set

of control signals the nervous network has to

produce in order to liven up the robot. Once this is achieved, further investigation into turning

mechanisms as well as successful forward locomotion are necessary to complete the first

objective.

Assuming a robot is able to survive in a given environment, i.e. it does not need permanent

human intervention for maintenance or otherwise (otherwise there is not much autonomy to be

had), it will only need a navigation strategy (as there shall be no remote human control) and


Figure 9: A snake-like robot can be thought of as sampling a snake-like curve. a) a snake like curve b) robot with high degree of freedom c) robot with small degree of freedom, both sampling the snake-like curve


sufficient power in order for it to be autonomous. As far as power requirements are concerned,

a suitable battery should be sufficient to enable the robot to survive, while the nervous network

controller is assumed to be able to handle and adapt to complex terrains, come what may

(within reason). This is coherent with [16], [17] and observable behaviour of most insects, fish,

worms or other 'simple' life forms; without clear (obvious) high level navigation strategy, these

animals appear to just exist; they roam around reacting to external events that trigger reflexes;

if there is an obstacle, they avoid or overcome it, if there is a threat, a reflex 'tells' them to flee

or confront. In a similar fashion, it is legitimate for the robot's navigation strategy to comprise

indeterminate roaming with robust terrain handling abilities, derived from the interaction of the

nervous network controller with the mechanical body and it's environment.

Anatomy

So what should the robot look like? The work done here tries to draw its inspirations from

nature, so the robot shall have an anatomy similar to a snake's anatomy. This is obvious, as it is

a snake-like robot, but what is meant here, is that the robot should, apart from looking like a

long flexible trunk, have a neural architecture that controls the behaviour of it. Disregarding

possible cognitive processes that happen within a snake's brain, and focusing purely on it's

locomotive mechanisms, the robot shall use a nervous network controller that serves as an

artificial CNS with central pattern generators dictating the locomotive reflexes, which,

combined, result in lateral undulating motion.

A variation of the master-slave bicore configuration, namely one master with nine slaves, one

slave CPG for every joint, can be used to achieve just that. Each slave bicore is a spinal CPG

itself and represents a local motor neuron dictating the motion of exactly one joint, while the

suspended bicore can be thought of as providing the supraspinal control necessary to initiate

and dictate the (motor-) neural activity; it functions as a pacemaker.

There are two obvious possible resulting neural architectures that could be adopted here, one is

a centralised, 'parallel' 'star' configuration, the other one a decentralised, 'serial' or 'line'

configuration, as shown in Figure 10. For each of the configurations, the pacemaker and the

CPGs interact with each other though certain relationships or coupling methods.

In the star configuration, the relationships all have to be different, so as to accommodate the

required phase that each CPG has to have with respect to its predecessor, while the line



configuration allows for the relationships to be identical. This allows the circuit, and the robot

itself, to be more modular and simple. Furthermore, the line configuration resembles the CNS

of a vertebrate more closely, with supraspinal control propagating down the links. Also, from a

circuit point of view, the decentralised line configuration may be more advantageous, as each

CPG is coupled to its two nearest neighbours and is the master of one slave, whereas in the star

configuration, there is no direct coupling between two consecutive CPGs, and only the

pacemaker is the superior master. It is intuitive, that an external factor which affects one link

should also affect the links closest to it; this feature can be advantageous.

Again, as there is no better knowledge about the exact topology of spinal CPGs in real animals,

an educated guess has to be made, and for the reasons and advantages mentioned above, the

serial line configuration is chosen as nervous architecture.

Following the nervous architecture, the output or control signals from the nervous network

controller are taken directly off each bicore (excluding the pacemaker), and are square waves

with a given phase, determined by the coupling resistances of the slaves. Conveniently, these

can be used to drive or control DC motors, and thereby switching them forwards and

backwards periodically. Is a chain of such switched DC motors, i.e. constant angular velocities


Figure 10: Two alternative nervous architectures: A. a parallel star configuration, B. a serial line configuration

Pacemaker

CPGCPG

CPG

CPG

CPG

CPGCPG

CPG

CPG

Rel 1

Rel 2

Rel 3

Rel 4Rel 5Rel 6

Rel 7

Rel 8

Rel 9

Pacemaker

CPG

CPG

CPG

CPGCPG CPG

CPG

CPG CPG

Rel 1

Rel 2 Rel 3

Rel 4

Rel 5Rel 6

Rel 7Rel 8 Rel 9

Alternative Nervous Architecture

A. Star Configuration B. Line Configuration


(in the ideal case) sufficient for undulating motion? Indeed it is, on a basic but sufficient level,

as will be verified in the section on modelling and simulation.

Another important consideration about the robot's anatomy is the power supply. Again, two

obvious architectures are possible, namely to have one central battery or power supply where

all modules, i.e. CPGs and motors, source from, versus a decentralised power supply, where

each module has its own power supply. Here, the consensus is, that one power source is the

better choice, so as to make sure that enough power rail feedback and interaction can happen.

In a distributed power system, this would not be possible, which, to some extent, defeats the

purpose of using a nervous network controller.

Modelling and Simulation

Before diving into practical adventures with a soldering iron, an appropriate simulation can

help to verify or reveal flaws about the above abstraction. In this case, it is worth verifying

whether the chain of phase shifted square waves controlling DC motors would indeed result in

undulating motion. Furthermore, certain parameters such as required period or delay could be

extracted from the simulation, saving possibly tedious practical experimentation. This section

describes relevant efforts undertaken to verify that undulating motion can be achieved, some

relationships extracted from the model with regards to the delay necessary in order to achieve a

certain wave.

Lateral Undulation or general wave-l ike motion

From the abstraction above, the robot can be modelled as a chain of links joined together. At

each joint, a DC motor provides an alternating torque or angular velocity (depending on how

you look at it), which is controlled by a square wave signal, dictating it to go clockwise or

anticlockwise. To achieve the desired wave like motion from this, only a few parameters have

to be set. But first of all, the desired wave has to be determined.

Real snakes are very long and, when wound or curled up, feature several loops. Also, when

moving, real snakes have far more flexibility than the 9 DOF robot proposed here, and usually

display a propagating wave with several peaks and troughs. When thinking back of the analogy

to the sampling theorem however, it is obvious, that with 9 DOF, or nine samples, the wave on

the robot's body cannot be much shorter than it's length. So how long should the wave be?



To address this, the robot of unit length can be considered

as displaying a standing wave of wavelength λ. The

shorter it is, the more peaks and troughs are there on the

robot's body. As shown in Figure 11, this has significant

implications on the 'snake-like-ness'. A balance between

the robot's physical length and the wavelength has to be

found. Arbitrarily, but intuitively, a wavelength of about

0.66 robot lengths, such that there are 1.5 wavelengths

propagating through the robot's body, is chosen.

The implications on the phase between the individual

square waves are as follows. If the first joint is used as reference, then phase shift between

successive joints' square waves should be such, that they add up to 150% on the 9th link, if 1.5

waves worth are to be standing on the robot. Therefore,

each shift is s = 150% / 8 = 18.75% between two

successive joints, as shown in Figure 12. The time delay

between two square waves is then sT seconds, where T is

the period of the square wave. As a matter of fact, the

exact matching of these phases does not matter, but is a

fair starting point. If the robot had 1.4 or 1.6 wavelengths

on its body, it would still be acceptable, especially as

1.5λ was chosen arbitrarily.

The symmetric and continuous wave motion displayed by the robot relies on the square wave

signal to be of 50% duty cycle. Every flat top or bottom of the signal represents the motor

going in either clockwise or anticlockwise direction for the given time (i.e. with 50% duty

cycle, the motors are turning in either direction for half the period of the signal). Clearly, the

period of the signal and the (assumed constant in an ideal case) speed of the motor have to be

balanced such, that the robot does not curl up at once (if the motors are too fast, or the period

too long, or both). On the other hand, the period and motor speed should not be too short or

slow either, otherwise, the robot body would hardly move, but only vibrate. Assuming that a

driven DC motor under no load generates a constant angular velocity, the whole system was

modelled using Nastran, a physical modelling software. Therein, a chain of blocks resembling


Figure 12: Phase relationship between joints

Joint Phase Shift1 0.00%2 18.75%3 37.50%4 56.25%5 75.00%6 93.75%7 112.50%8 131.25%9 150.00%

Figure 11: What wave should propagate the robot's body?


motors coupled together by joints with applied angular velocities would represent the robot.

The nervous network controller generates a square wave (ideal case), which drives the motors,

so each joint motor in the Nastran simulation receives an angular velocity square wave, as in

Figure 13, with given phase relationship as defined above, from an input file generated

manually.

Without going into unnecessary detail, inspection of the video generated by the simulation did

indeed display a propagating wave that enables the chain of blocks to move in a wavelike

form, with about 1.5 wavelengths displayed. The simulation was run using several different

periods, all yielding the same motion, but at different speeds, as expected.

Figure 13: Simulated phase shifted signals to four joints/motors (not to scale)

Mechanical Design

The robot as abstracted should work; the simulation confirmed this. However, this was done

for a general case, and verified the concept (that a sequence of phase shifted square wave

signals driving DC motors should result in undulating motion) rather than any particular robotic

design. As mentioned earlier, the nervous network controller can be thought of as interacting

with the robot's environment through its body. For a moving robot, this means that a desired


Simulated Phase Shifted Signals


and stable gait can only be achieved by the controller, if the mechanical body allows for one. If

that is the case, then the network will converge to a stable solution.

Some tolerances can be handled through its adaptivity. If, for example, the network and the

body are not exactly well matched, as it is likely to be the case (and it is very difficult to pre-

determine analytically what parameter settings are required to achieve a harmonic interplay

between controller, robot body and environment), then the network may still be able to

produce a useful gait, as the environment feeds back to the network through external loading

of the actuators, thereby affecting the state of the network. It should be noted, that to date,

there is no know methodology to design or engineer nervous network controllers to

mechanical bodies, which makes 'tuning', or rather 'manually evolving' the robot, as well as a

healthy amount of engineering gut feeling, rather useful.

Following the design philosophy of simplicity, it would be desirable to manufacture the robot

from as few mechanical parts as possible. Using 'hacked' servo motors, a modular and simple

but appropriate mechanical design was achieved, as follows.

Servomotors are widely used in robotics, due to their strength, accuracy, agility and

compactness. However, they also offer another advantage that shall be exploited here: their

casing. For the application here, a simple DC motor would do the job, as all that is required is

simple alternating rotational motion. However, ordinary DC motors are far too fast, and

possibly too weak for this application, while ordinary available geared motors are usually very

bulky. A servomotor on the other hand is basically a highly geared, compact DC motor with a

control unit. By hacking the servo and removing the control electronics, a simple highly geared,

slow, strong and, most importantly, conveniently packaged DC motor is obtained, which is

exactly what is required for this application.

The Futaba S3003 servo motor, Figure 14, was chosen, as it was the

slowest motor in its class, with a rated 0.23 sec/60° at 4.8V. The box-

like casing (41mm x 20mm x 36mm) of the motor hardly needs any

modifications, and nine of these can be used as the robot body

directly, when linked together. The flat side of the body is suitable for

attaching small circuit board to, and overall, a very modular design

can be achieved. In fact, as was determined experimentally, the motor

is still too fast at the rated 4.8V, which is why the supply voltage to


Figure 14: Futaba S3003 servo motor, from

www.futaba-rc.com


the robot will have to be reduced, slowing down the motor and reducing its torque by tolerable

amounts.

The mechanical links between the motors are made from

simple (scrap) pieces aluminium, cut to shape, and were

manufactured manually. Here, it is important to keep the links,

as short as possible, in order to minimise the distance between

two neighbouring servos, thereby reducing the torque coupling

of each motor to the rest of the body.

As it is, the robot body is very simple, consisting of nine

hacked servo motors linked together by short and light

aluminium links. It is desirable to attach wheels to the servo

cases, so as to provide differential friction, i.e. low, rolling

friction along the long side of the servo, and a lot of resistance

to motion (against the wheel) sideways to the servo. Without wheels, the robot just 'slides on

its belly', which is not very ideal. First of all, the servo casings are not suitable for sliding, and

secondly, and more importantly, there are no constraints for the motion. Without wheels, the

robot's lateral interaction with the environment is not constrained, and the solution space for

the nervous network controller is (essentially) infinitely large. It would be very unlikely that the

controller managed to converge towards a stable gait without any constraints on the

mechanical motion of the body. Adding wheels however would discourage motion in the

sideways direction, and encourage motion in the longitudinal direction, narrowing down the

solution space and thereby assisting the nervous network controller to converge. Unfortunately

however, there was not enough time to add wheels to the robot body, which is the first item

for future work. The consequences of the lack of wheels, and alternative solutions, will be

discussed in another section. A picture of the assembled robot body can be seen in Figure 16.

Figure 16: Nine servos linked together to form the basic robot body


Figure 15: Mechanical aluminium links connecting the servos together


Electronic Design

The previous section on abstraction has developed the plan for the robot, the implementation

of the easy part was described in the section on mechanical design above. Here, the crucial

part, the nervous network controller, will be developed and described in detail.

The nervous network controller, also referred to as the Artificial Central Nervous System

(ACNS) of the robot, follows the line configuration as explained above, and consists, thanks to

its modularity, of only two different types of circuits: a pacemaker and a motor neuron module

(MNM). These are realised using a variation on the master-slave dual bicore arrangement,

where the pacemaker is a suspended bicore, and the MNMs are ordinary bicores, coupled to

the pacemaker or the predecessor MNM respectively. Each of the nine motors will have one

MNM, while the pacemaker is independent.

General Circuit Design Guidelines

There are some circuit design concepts, that are common to both pacemaker and MNM, and

are as follows:

Use LEDs for visual inspection

In total, the ACNS will consist of ten different circuit modules, and using a standard

measurement device such as an oscilloscope to verify its operation is impractical.

Instead, circuit modules shall feature LEDs that flash according to the generated motor

square wave, for visual inspection.

Keep circuits variable

As the controller will have to be fine tuned to suit the body, both the pacemaker and

each MNM should have some degree of flexibility. In particular, this means, that the

pacemaker should allow for its period, and the MNMs for the delay between them, to

be variable or reconfigurable. In other words, the individual circuits' time constants,

given by their resistors and capacitors, should be easy to modify.

Keep the ACNS modular

The robot body is very simple and modular, and every module (i.e. every servo with a

metal link) of the body is interchangeable. The circuit should have the same property, as

each MNM should be attached to one motor, comprising a functional robot module.

This is achieved by designing nine separate circuit boards, one for each body segment,



and using a bus structure made from ribbon wires to connect the modules together.

Fix capacitors, change resistors

In order to enable the above required variability, only the resistors should be made

variable. Capacitors to date still suffer from large manufacturing tolerances, and

variable trimmer capacitors even more so. Therefore, for the sake of accuracy and

simplicity, all capacitors in the circuits should be fixed. Resistors on the other hand can

can be obtained at 1% accuracy, and variable resistors or potentiometers of all different

sorts are readily available. To enable the circuits to be variable enough, time constants

should be changed by changing resistor values only.

Nv neurons are dedicated to the ACNS only

Due to the sensitivity of the ACNS, it is important to minimise the effect that peripheral,

non controlling, devices have on it. These include the LEDs and motor drivers, which

can be connected to the ACNS using other inverters (buffers) on the same IC chip. As

an inverter can be approximated as an ideal inverting amplifier, it will draw no current

from the output of a MNM, and therefore not (or only little) affect the coupling

between two successive MNMs.

Circuits should be compact

To keep the robot compact, and snake-like, efforts should be spent on trying to keep

the circuits as compact as possible. In particular, each MNM should be attached to the

side of a servomotor, i.e. the circuit should be smaller than 41mm x 36mm.

IC Logic series used

The ACNS requires 20 inverters for its operation, two per MNM and two for the

pacemaker. Additional inverters are used as buffers. The IC logic family used shall be

74HC series logic.

Power rails

As discussed earlier, the entire ACNS should source from the same power rails.

Therefore, as the line configuration is to implemented, a common Vcc and GND

connection should run throughout the entire circuit, from module to module and head

to tail (or tail to head?). This way, only one battery or power supply needs to be

connected at one connector to the robot. For experimental purposes, a DC power



supply instead of a battery is used. For reasons explained below, relating to the speed of

the motor, the potential difference across the power rails is 3.5V.

The Pacemaker

The pacemaker circuit is very simple. It is a suspended bicore, and has no inputs, and two

neuron outputs that link to the rest of the circuit through the first MNM's coupling resistors, in

addition to the power rail connectors.

The IC employed here is the 74HC04 (a generic datasheet can be found online) hex inverter

chip, featuring six independently usable inverters, although only four are used here. It uses two

0.3µF ceramic capacitors, and a 560kΩ bicore resistor in series with a 1MΩ potentiometer,

acting as a variable resistor to tune the circuit. It should be noted, that although the pot (or a

large enough pot) could have been used on its own, it allows for a 'zero' resistance setting, and

therefore a short circuit inside the bicore, which is undesirable. In order to avoid that, the

second fixed bicore resistor is used. Therefore, the circuit has a nominal (unloaded) time

constant ∈[0.168,0 .468] s . Once the entire ACNS is connected up, loading effects will

change this range, and so the achievable periods of oscillation of the pacemaker will change,

too.

The LEDs are driven through a separate pair of

inverters, so as to minimise the effect that switching

them has on the ACNS, as explained in the guidelines.

They are rated at about 20mA to 25mA diode current

Id, and as the output voltage of the inverter is 3.5V, a

150Ω driving resistor was chosen. The entire

pacemaker circuit is shown in Figure 18 below. Figure

19 Illustrates the compact circuit layout on the

veriboard, where horizontal red lines represent the

metal strips on the board, interruptions of the red line

represent cuts made to the strips, and green lines

represent connecting wires on the board. Figure 20 shows a close-up photo of the actual

pacemaker circuit board, clearly showing the components and the connectors used.

With the entire ACNS connected together, the required oscillation period of the circuit was


Figure 17: The pacemaker in a nutshell

Pacemaker

IC Chip: 74HC04 hex inverter

Resistors:

Capacitors:

LEDs: 2 HE red LEDs

Inputs: None

Outputs:

Power:

1 bicore resistor 560kΩ, 1 bicore trimmer pot 1MΩ, 2 LED resistors 150Ω

2 bicore capacitors, 0.3µF

2 neuron outputs to first MNM, 2 identical neuron output measurement pins

Vcc and GND rails


determined experimentally. With a higher supply rail voltage than 3.5V, the motors would be

too fast, therefore any snake-like motion would have been similarly fast, resulting in an

unrealistic motion of the robot (imagine a fast forwarded video of a snake, clearly not

desirable). Then, at 3.5V, a suitable period of around 0.5 seconds (540ms) was chosen, so that

the motors turn less than 30 degrees each way within one period.



Figure 18: Circuit diagram of the pacemaker

Figure 19: Veriboard layout of the Pacemaker circuit. Red horizontal lines represent the metal

strips on the board, and green vertical lines represent connecting wires.

Figure 20: Photo of the bicore circuit board



The Motor Neuron Module

The core of each MNM circuit is a bicore, with its two coupling resistors acting as the only

inputs (apart from power supply rails). There are four output pins, two pairs of two, namely the

main two ACNS outputs pins from the bicore to the next MNM, and two output pins to drive

the motors.

The IC employed here is the 74HC240 (a generic

datasheet can be found online) octal inverter,

featuring eight separately usable inverters

arranged in two banks of four, which can be

separate enable or disabled by the corresponding

one of the two enable inputs. This feature can be

used to enable/disable outputs to the motor, if

turning abilities are to be implemented, but this is

not done here; only six inverters are used. The

MNM features two ceramic capacitors of 0.1µF,

together with two coupling resistors of 1MΩ. In order to allow reconfigurations, single pin

female connectors are used to connect the resistors to the circuit, so that they are easily

exchangeable (see actual circuit). The circuit has a nominal time constant of =0.1s , which

corresponds to approximately 18.75% of the period (500ms - 540ms), as required for a 1.5

wavelengths propagating wave.

As in the case of the pacemaker, the LEDs are driven through a separate pair of inverters, so as

to minimise the effect that switching them has on the ACNS, and also have the same circuit.

The motors can require anything up to 600mA under high loading condition, although during

normal operation, less than 150mA are typical current requirements. The 74HC240 is not able

to deliver such an output current, which is why a separate motor driver, controlled by the

bicore, is required. Fir this is, a H-bridge IC, a common motor driver for this application, from

the servo control board was de-soldered, and conveniently used. It is powered by the supply

rails, takes its two inputs from an inverter pair, buffered from the ACNS's neuron outputs, and

connects to the motor connector output. The inverters driving the respective LEDs and H-

Bridge input are connected in parallel. The complete circuit is shown in Figure 22, while Figure

24 and Figure 23 show the compact veriboard layout and a close up photo of the MNM.


Figure 21: The MNM in a nutshell

MNM

IC Chips:

Resistors:

Capacitors:

LEDs: 2 HE red LEDs

Inputs:

Outputs:

Power:

74HC240 octal inverter, BAL 6686 H-Bridge

2 coupling resistors on sockets (exchangable), 2 LED resistors 150Ω

2 bicore capacitors, 0.1µF

2 neuron inputs from preceding MNM

2 neuron outputs to next NMN, 2 DC motor outputs

Vcc and GND rails


Figure 22: Circuit diagram of a Motor Neuron Module

Figure 23: Veriboard layout of a Motor Neuron Module. Red horizontal lines represent the metal strips on the board, and green vertical lines represent connecting wires.

Figure 24: Photo of a Motor Neuron Module



The entire ACNS was manufactured manually in the workshop. Due to the compactness of the

circuits, the H-Bridge had to be arranged in an unideal way. The circuit boards are stuck to the

side of each motor case using double sided adhesive pads. 4 bit wide bus connectors made

from ribbon cable were made to connect the modules together.

In total, the robot is about 50cm-60cm long, and a complete picture of it lying on its side, with

the ACNS clearly visible is shown in Figure 25.


Figure 25: The complete robot on its side, with the ACNS clearly visible


Eva luat ionEva luat ion

The previous sections have described the underlying principle, and the mechanical and

electronic design of the biomorph snake-like robot. Whether it actually works, is described

here.

Normal Operation

Normal operation refers to the robot operated in the way for which it was designed, as a snake,

which is in contrast to operating it as a worm, where the robot displays emergent behaviour, as

described in the section after this.

In order to evaluate the robot, a rigid testing strategy to observe and describe the behaviour of

it under different conditions was developed. For each of the different set-ups, an evaluation

sheet with relevant criteria is filled out during the testing process. These different testing

conditions are the following:

1. Power up of head only, no body connected

2. Power up of entire controller/spinal chord, no motors connected

3. Power up with only individual motors connected one by one

4. Power up fully connected robot

The results are as follows.

1. Power up head only

1. Behaviour at fixed oscillation period (533ms @ 3.5V)

On power up, the circuit behaves about right. The duty cycle is ~50% [49.4%, 50.7%] and Vout is at 3.44V. The period can be fixed at approximately 540ms.

2. Behaviour for varying oscillation

The range of frequencies is [300ms,1160ms]

Time to convergence It takes less than 3s for the Bicore to display the stable correct signal at the right frequency.

Desired behaviour? Yes – Power up & Run

Other observations After having run stable for some time, the period suddenly drops to 280ms.

When touching one of the output pins, the period decreases, probably due to faster discharge of the capacitor through the alternative path created. This is in contrast to loading, which increases the period.



2. Power up entire spinal chord, no motors


With the whole circuit connected, the period drops to a saturated mode with 189ms period, probably for a similar reason as above, as the whole circuit is connected to the head’s output pins, draining the capacitors faster.

Delay is still 85-90ms, which is within the expected (100ms).


The circuit appears to have discrete levels of ‘allowed’ periods, i.e. changing the Bicore resistance does not alter the period of oscillation in a linear way, but rather in a sudden step like way. Observed levels of periods of oscillation are [672ms, 503ms, 346ms, 190ms].

Time to convergence Initially, after power up, neurons are in a random state, and it takes some time (9 delays) for all the neurons to be driven by the master’s wave.

Desired behaviour? The neurons all show the desired behaviour of stable oscillation with very close to 50% duty cycle. Phase delay is also about 60deg as calculated. LEDs show a wavelike pattern.

Other observations none

3. Power up with only M1


The circuit with only the first motor connected is capable of the right behaviour, oscillating at about the right frequency and duty cycle, but a lot of disturbances and feedback make it difficult to remain in the stable state (50% duty cycle) in a controlled fashion.

The oscillation period changes with the loading on the motor, and tends to be proportional (more load longer period).


As expected, the motor turns in each direction for longer/shorter amounts of time.

Time to convergence n/a

Desired behaviour? More or less

Other observations The effect of the motor feedback and loading on the oscillation period is propagated down the spine, i.e. a loading feedback can be seen on a MNM further down the spine.

In ‘unstable’ state, the duty cycle tends to be around 60% (or 40% depending on where it is measured from), regardless of the orientation of the motor (as it is a DC motor, it could be connected either way). Thus, the head motor M1 will always tend to curl in left (of the snake).



4. Power up with only M2


Also capable of displaying the right behaviour with about 50% duty cycle at the right frequency, but it also tends to become unstable as M1, with 60% (40%) duty cycles, curling in. However, it is definitely more stable than M1 on its own, and remains stable for most of the time.

Desired behaviour? More or less

Other observations The duty cycle is also independent of the direction of the motor connection.

5. Power up with only M3, M4, M5, M6, M7, M8, M9


In general, the closer to the center of the snake the motor is (i.e. M5 is the center of the snake), the better its performance, and the closer to the desired result it performs, and the less often it leaves the stable state. This is probably because in the center, the load as seen by the motor, i.e. the weight or inertia of the robot, is more balanced.

Time to convergence Fast

Desired behaviour? Mostly

Other observations When turned on the side, the loading of the motor increases, which feeds back into the circuit, resulting in a longer period of oscillation.

6. Power up fully connected robot


The first motor M1 always tends to curl leftwards, as mentioned earlier, which is attributed to the imbalance of load as seen by the first motor (i.e. there is no weight or inertia in front of it, and a lot after it). Ditto the tail motor. Adding weight to the head and tail may improve the problem. In fact, connecting the pacemaker to the second motor and disconnecting the first one (so it is just a weight at the end of the second motor) improves the performance, and the ‘new head’ (i.e. the second motor) is more stable than when the first motor is used as the head.

Other than that, the rest of the body displays a wavelike motion, sliding more or less freely over the floor. However, it is not absolutely regular, and is jerky from time to time. This is attributed to the lack or mechanical constraints on the robot. If the nervous network was to converge to a stable motion, there must be a more constrained ‘problem space’ for it to search for a solution. However, the fact that the body of the snake is just sliding freely over the ground implies that there is not one optimal



solution, but any solution is as good as another. Adding differential friction elements such as wheels is believed to impose constructive constraints onto the solution space, and there should be clear benefits of continuous wavelike motion over other more erratic or irregular motion, a the network should converge.


Wave like motion changes accordingly over the short range of achievable periods of oscillation. At its shortest, the robot more or less vibrates, as a very fast oscillation is propagating the robot.

Time to convergence fast

Desired behaviour? In general, there is wavelike motion, but it is not smooth enough at all times, and the first and last link tend to curl up.

Other observations When put on its side, the robot displays worm like backwards motion, and varying the oscillation period changes the wave that is travelling down the body of the robot.

7. Power up fully connected robot, restrained at both ends


Using wires to loosely restrain the robot at its head and tail, without stretching it outwards too much, the motion is much more smooth and closer to the desired motion. The curling of the first and last link is reduced.


As the period changes, so does the wave on the robot body, but the general behaviour is conserved.

Time to convergence Fast

Desired behaviour? Restraining both ends of the robot improves the motion of the body compared to the robot let lose, which further underlines the assumption that the uneven mechanical loading conditions of the first and last motors compared to the other motors might be the reason for the curling up behaviour. Adding a dedicated ‘head’ or ‘tail’ with appropriate weight could improve the performance.

Other observations

Discussion

The robot works partly as desired, but seeing it jerking to live and holding it's erratically

moving body certainly feels special, not like a machine. The ACNS works as it is supposed to,

and the oscillating LEDs clearly display a propagating wave, like a row of lights that go on and

off as on an airport's runway at night. Depending on its initial orientation, the fully connected

robot may curl up after power up. This is because it has not been 'initialised', for operation. If

the robot is arranged in a straight line initially, then it should, in theory, open and close, going

from an 'I' shape to a 'U' shape. To avoid this, the robot has to be pulled and stretched after



power up, 'overwriting' the commands of the ACNS and forcing the motors into position.

Then, if the robot is unrestrained, it does not stay in a wavelike shape, but tends to curl up.

This is because the duty cycle of the square waves are not balanced at about 50% on average

(it would be no problem, if it deviated from 50%, if the average was about 50%), but biased to

one side (either <50% on average, or >50% on average). It is difficult to determine the exact

reason for this, but there could be several, including mechanical irregularities, an uneven

surface or an insufficient match between the controller and its mechanical body. The first and

the last module tend to curl up more than others. This is attributed to the uneven load

distribution that these two motors see, and can be overcome. Indeed, the tail has one 'dead'

(i.e. unconnected and unused) servo motor attached to its end to act as a counterbalance,

which reduces the tendency to curl up. The robot is reduced to a jerking chain of motors,

which are just sliding over the surface with no observable snake-like pattern.

A sequence of rapidly taken photographs is shown in Figure 26, where the camera took 16

pictures in about one second. It can be observed, how the robot is curling in and how it fails to

display any remotely snake like motion. If this creature was to face the unforgiving nature and

evolution, it would not make it very far.


Figure 26: Unrestrained snake-like motion: the robot tends to curl up. Pictures taken over approximately 1 second


Restraining the robot at its two ends loosely using wires or strings improves the performance a

lot. The first and last module, as well as the robot as a whole, are stopped from curling up, and

a smooth wave motion is achieved. This improvement is attributed to the fact that fixing the

body applies constraints to the way the robot is able to interact with its environment, thereby

changing and narrowing down the solution space that the nervous network is searching in.

Now, a more smooth and continuous wave like motion is more favourable solution than other

types of behaviour, for whatever reason (again, this is not yet understood, but the answer could

lie in the realm of non-linear dynamics). Figure 27 shows another sequence of photographs

taken within about one second, showing the snake-like motion displayed by the robot (note

how the sequence of photos starts at the bottom left, and works upwards to the top right).


Figure 27: Good snake motion with head and tail restrained loosely. Pictures taken over approximately 1 second


Despite the smoother and more desirable motion of the robot, it still does not experience

locomotion, but only slides over the ground, as there are no wheels or other differential friction

elements employed.

Test 2 described how the period of oscillation of the ACNS without motors connected

appeared to be in discrete levels., i.e. linearly changing the resistance in the suspended bicore

of the pacemaker did not linearly change the period of oscillation. This is a prime example of

how the network would adapt to the external environment. Even though the network was

physically configured to oscillate at one frequency by the tuning resistor, it oscillated at a

different frequency, as that was the 'preferred' solution. This effect reappears in several areas

of physics, be it the 'electron-in-a-1D-box model' from quantum physics, the physics of an

organ pipe or the quantisation of current in carbon nanotubes. All these examples have to do

with a wave 'interacting' and interfering with its environment (and itself), where only certain

configurations (e.g. wavelengths or physical dimensions) allow for constructive interference. In

this case, the physical configuration, i.e. the circuit topology and the devices connected to it,

are such, that only certain periods of oscillation are stable, thereby forming what was observed

as discrete levels. Thus, the notion of the ACNS finding a 'preferred' state is slightly simplified,

it should be referred to as finding a constructive state, a stable state or even simply an existing

state.

In summary, the robot with its ACNS as described above can indeed display snake-like motion,

however, it is incapable of snake-like (forward) locomotion. Surprisingly though, the snake

robot is also a very capable worm robot, as described below.

Worm-like Operation

Throughout this report, the phenomenon of emergent behaviour is often mentioned, and

described as 'unexpected, but desirable behaviour'. In fact, emergent behaviour is usually used

in the context of swarm or multi-agent behaviour (e.g. a swarm of bees or fish, the stock

market, traffic, ...), where complex or advantageous collective behaviour is derived from simple

individual, often selfish, behaviour of an agent inside the system (e.g. grouping together in a

swarm of fish reduces the dangers of encountering predators for each fish).

In this context, emergent behaviour is referred to as the unexpected but advantageous

behaviour of the single robot. In particular, robust interaction with a changing environment



despite lack of preprogrammed sequences or predetermined behaviour.

Here, a snake-like robot was designed to exist in a simple planar environment. Yet, When

turned onto its side, the robot is displaying worm like forwards locomotion. The undulating

propagating wave lifts up parts of the body at a time, and slowly push the robot forwards.

Figure 28 shows a snapshot of worm-like motion, taken from a video, where the robot is

crawling towards the camera.

What is even more surprising than its

locomotion is that in this configuration, the

robot is able to avoid certain obstacles, and

climb over barriers taller than itself. Thereby,

the non-linear feedback capabilities and

adaptivity of the robot are well exploited. As

the robot encounters a barrier, as in Figure 29,

it first fails to move further, and is pushed to

one side, as if 'trying' to avoid it. At the same

time, the increased load on the motors cause the oscillation period of the circuit ton increase,

resulting in a longer motor driving cycle; the amplitude of the undulation is increased, allowing

the robot to get it's head up on the barrier. Once this has been achieved, the rest of the body

continues to push the robot forwards, while the body slowly but surely moves up the barrier,

link by link. Thereby, the robot body is bending its back in parts more than during 'normal'


Figure 28: Screenshot of worm like motion

Figure 29: Sequence of worm climbing over an obstacle out of an enclosure in less than 5 minutes


operation; as the robot is very adaptive, this does not affect the locomotion. Eventually, after

about four minutes, the robot managed to successfully overcome the obstacle, and continued

to make its way forwards as if nothing happened. Its original shape is restored as it adapts to

the even terrain of the floor again.

With these locomotive and terrain handling capabilities, autonomy is simply a question of

adding the right power supply. As the robot 'happily' creeps away, it gives off the impression

that it could do that forever, marking a simple but noteworthy existence of an artificial 'form of

life'. This simple creature has not had the luxury of millions of years of evolutionary

incremental improvement, blending it smoothly into the (laboratory) environment, yet it is

conceivable how it would survive in an unstructured environment. There are many parallels

between it's existence to simple natural forms of life. Many animals, insects for example, can

be observed to display seemingly erratic and random motion, trying to climb over or out of

something (like an enclosing), such as the fly that is trying to leave a room through its closed

window or the ant that is just roaming around until it finds something 'interesting', like food. In

a similar way, the robot just roams around, as if looking for something, which, of course, it

won't find (because there is nothing it is looking for). It is remarkable however, how the simple

set of reflexes (in this case locomotory reflexes) are sufficient for the robot to display behaviour

resembling real living creatures.

Then, a question that arises is, what distinguishes a sufficiently complex biomorph robot (one

that may have more capabilities than the worm or snake described here) from a real, simple

animal or insect? Is the robot alive? Is an ant in fact a robot? Can artificial life be real life?

Without trying to answer these questions, it should be noted that biomorph robotics, or an

even more sophisticated field that may arise from it, could challenge the way we think about

'life'.

Some thoughts the reader is welcome to dwell upon:

Life reproduces, whereas machines do not reproduce. It would be more correct to say,

that living systems reproduce, as many flowers only reproduce with the aid of bees or

the wind, or worker ants, which do not reproduce themselves at all, but only the

collection of the ant colony as a whole reproduces. Similarly, robots could be said to

reproduce using the aid of humans. Furthermore, reproducing machines, i.e. machines

manufacturing other machines, are not an inconceivable scenario for the future.



Living creatures have emotions. It is difficult to imagine that a robot has emotions, but

similarly, it may be difficult to imagine that a cockroach, which is said to be living, has

any more emotions. Conversely, maybe cockroaches do have emotions (that we have

not yet discovered), but then robots may also have internal processes (voltages,

currents, electromagnetic fields, ...) that, if resulting in certain actions, could be

interpreted as emotions.

Generally, living creatures cannot be taken apart and reassembled like machines or

robots can, although that may change in the future as medical technology advances.

Living creatures fulfil a purpose within their natural environment. Nature as a whole is a

self sufficient system where the circle of life provides every creature with a 'purpose' (or

at least with a place in nature), may it be as 'food' (prey) for other animals, or natural

garbage collectors. So far, no real value other than an academic interest or enjoyment

can be derived from simple 'living' machines, but a sufficiently advanced machine could

fulfil several useful purposes both for humans and other living creatures.

Living creatures are carbon based. To date, all living creatures known to man are indeed

carbon based. Would a machine that is manufactured on a carbon base be alive?

Authors and scientist do not rule out, that non-carbon based living creatures may exist

elsewhere in the universe.

Living creatures have a metabolism. The process of generating energy through chemical

reactions, thereby also creating some waste, is in some ways similar to what happens in

car engines or fuel cells. There is no reason why a living machine could not have an

equivalent metabolism.

Living creatures are breathing or experience photosynthesis. The purpose of breathing is

generally to acquire a fuel, oxygen, to power the metabolism. Would it be fair to say

that an artificial form of life would 'breathe' some other substance than oxygen, i.e. fuel

off something else?

Living creatures can grow. As such, growth is the accumulation of the 'modules of life',

i.e. cells, into a meaningful and self-assembled way. These kind of cellular or modular

self assembling machines are already the scrutiny of researchers, and could yield robotic

systems that are also able to grow.



Living creatures can adapt to changing environments and conditions through evolution.

It is more precise to say that adaptivity is a feature of surviving species, rather than of

life itself. Creatures that cannot adapt to changing environments become extinct, but

are still living creatures before they die. 'Living machines' that cannot adapt could be

regarded as species doomed to extinction, but they would still have been alive at one

point. Alternatively, the human could close the loop of machine evolution.

This list of arguments and counter examples could be continued endlessly, and the reader is

advised to decide for him- or herself what it is that constitutes life.



ConclusionConclusion

This report set out to describe the research, development and the behaviour of a snake-like

robot. The robot was described and developed step by step, with the underlying design

principle of simplicity.

The mechanical body of the robot was kept very simple and modular, and allows for a nervous

network or other controller to be employed. However, due to time constraints, it does not

feature the required wheels and battery that would enable it to move forwards and become

autonomous. Nevertheless, an emergent behaviour solution allows it to crawl forwards like a

worm despite its lack of wheels.

Using simple biologically inspired nervous network circuits, an artificial central nervous system

was devised through abstraction and simulation, and manufactured manually to fit the

mechanical body. The resulting robot is modular and very simple, using a total of 40 equivalent

transistors (20 inverters) only to control it, and it does not make use of microchips and

therefore does not require any programming. The resulting motion is indeed snake-like, if the

robot body is subjected to some mechanical constraints, i.e. if it is suspended at its head and

tail using strings or wires.

In a different mode of operation, the robot displayed emergent behaviour; lying on its side, the

snake-like robot crawls forwards like a worm, and is able to climb or avoid obstacles. While the

snake-like robot cannot be classified as very survivable, due to its inability to move forwards,

the worm-like robot behaves in a way that could enable it to 'live a very simple existence', and

can be classified as able to 'survive'.

As far as the project within the framework of an academic research exchange is concerned, it

has been highly successful and fruitful, not only as most aims and objectives of the project

were achieved, but also because the process of the work and everything around the exchange

experience have been a great lesson and very insightful.

It is hoped, that the reader could appreciate some aspects of artificial life and robotics, which

can be a very exciting discipline both from a technical, but also from a philosophical point of

view.



Fut ure WorkFut ure Work

This project is far from finished. The seven weeks spent on devising the robot were very

precious, and extending the project by weeks or months would not allow for enough time to

finish off everything that could be done. The work that this project could imply can be

described broadly as 1. work that has not been accomplished in this project, 2. further work to

improve or update the robot and 3. other work to advance the field of biomorph robotics.

For the first part, there are several aspects that were not picked up upon in the work carried

out, which are worth spending time on. This includes the addition of wheels or other

differential frictional elements that would constrain the motion of the snake-like robot

sufficiently for it to freely and stably converge to an undulating form of locomotion.

Furthermore, different parameters for the ACNS, such as period of oscillation and intermodule

delay (which translates to different propagating wave frequency and amplitude), are worth

experimenting with, to investigate advantageous configurations, as the set-up described here

was derived largely through engineering approximations and estimates.

Next, many different improvements to the robot of different scales are conceivable. An

initialisation circuit that enables the robot to display the right wave motion as it is powered up

would avoid having to stretch and shape the robot manually. Turning capabilities could be

added and, together with the right battery arrangement, would enable the robot to be more

autonomous. Also, different neural architectures other than the line connection could be

experimented with or investigated. An up-scaling of the neural complexity would also be

worthwhile, to investigate more complex, and possibly more realistic behaviours, such as heat

seeking or feeding (recharging) instincts. In connection with the battery, an optimisation of the

power consumption would be beneficial.

Lastly, there is a lot of work that can be done in this relatively new (academic) field of

biomorph robotics. A lot of things are not yet fully understood, so deriving appropriate robot or

controller models, together with the right tools and methodologies to analyse and characterise

these would pose a great advance. Also, in order for biomorph robots to be more than fancy

toys, new circuits and network topologies have to be engineered, which allow for more

complex phenomena, such as learning, to surface.



ReferencesReferences

[1] Arslan, A., Brauer, E., Cohen, A., DeWeerth, S., Nicholson, D., Schraudolph, N.,

Simoni, M., Stanford, T., Tilden, M., Williams, T., (1998). 'Locomotion of segmented

lamprey-like robots'. Report to the National Science Foundation: Workshop on

neuromorphic engineering 1998, Telluride, CO.

[2] Conradt, J., Varshavskaya, P. ‘Distributed Central Pattern Generator Control for a

Serpentine Robot’.

[3] Dowling, K. J. (1997). 'Limbless locomotion: Learning to crawl with a Snake Robot'.

Dissertation submitted in partial fulfillment ot the requirements of the degree of Doctor

of Philosophy in Robotics, Carnegie Mellon University.

[4] Gavin, M. e-mail correspondence.

[5] Hirose, S (1993). ‘Biologically Inspired Robots’. Oxford University Press.

[6] Ijspeert, A. J. ‘Locomotion, Vertebrate’. The Handbook of Brain Theory and Neural

Networks, Second Edition, M.Arbib (Ed.), MIT Press.

[7] Inoue, K., Ma, S., Jin, C. (2004). 'Neural Oscillator Network-Based Controller for

Meandering Locomotion of Snake-Like Robots'.Proceedings of the 2004 IEEE

International conference on Robotics & Automation.

[8] Jayne, B. C. (1986). Kinematics of Terrestrial Snake Locomotion. COPEIA No. 4, 1986

pp 915-927, American Society of Ichthyologists and Herpetologists.

[9] Jiuh, T. S. (2003). Final Year Project Report on Biomorph Robots.

[10] Jiuh, T. S., Personal Communication.

[11] Jiuh, T. S., Vadakkepat, P., Vasudev, A. R. ‘Biomorphic Architecture: Implications and

Possibilities in Robotic Engineering’.

[12] Lewis, M. A. (1996). 'Self-oganisation of locomotory controllers in robots and

aninmals'. Dissertation submitted in partial fulfillment of the requirements for the

degree of Doctor of Philosophy in Electrical Engineering, Univesrity of Southern

California.

[13] Lu, Z., Ma, S., Li, B., Wang, Y. ‘Serpentine Locomotion of a Snake-like Robot



Controlled by Cyclic Inhibitory CPG Model’.

[14] Ma, S. (1999). ‘Analysis of Snake Movement Forms for Realisation of Snake-like

Robots’. Proceedings of the 1999 IEEE International Conference on Robotics &

Automation.

[15] Mazzocchi Alemanni, F. Personal Communication and Mechanical Modelling.

[16] Nieuwenhuys, R, ten Donkelaar, H. J., C. Nicholson (1998). ‘The Central Nervous

System of Vertebrates Volume 1’. Springer Verlag

[17] Paap, K. L., Kirchner, F., Klaassen, B. (1999). ‘Motion Control Scheme for a Snake-Like

Robot’.

[18] Still, S., Tilden, M. W. ‘Controller for a four legged walking machine’.

[19] Tilden, M. W. (1997). ‘The Design of “Living” Biomech Machines: How low can one

go?’.

[20] Tilden, M. W., Hasslacher, B. (1994). ‘Living Machines’.

[21] Tilden, M. W. (1992). Adaptive robotic nervous systems and control circuits therefore.

US Patent 5,325,031.

Web References

[22] AIBO, http://www.sony.net/Products/aibo, SONY Corporation

[23] Brok, W. ‘Suspended Bicore’. http://library.solarbotics.net/pdflib/pdf/suspbicr.pdf

[24] ASIMO, http://www.honda.co.jp/ASIMO, Honda Motor Co., Ltd.

[25] Manus-1 humanoid football robot,

http://www.ece.nus.edu.sg/stfpage/elepv/humanoid.htm, National University of

Singapore

[26] Material on Nv neurons, and Figures, www.solarbotics.net, solarbotics.net

[27] Robosapien, http://www.robosapienonline.com, Wow Wee Ltd.

[28] SnakeRobots.com, www.snakerobots.com, Dr. Gavin Miller


http://www.sony.net/Products/aibo

http://www.snakerobots.com/

http://www.robosapienonline.com/

http://www.robosapienonline.com/

http://www.solarbotics.net/

http://www.ece.nus.edu.sg/stfpage/elepv/humanoid.htm

http://www.honda.co.jp/ASIMO

http://library.solarbotics.net/pdflib/pdf/suspbicr.pdf


App endix I – L is t of comp onent sApp endix I – L is t of component s

List of components bought. Datasheets of all components can be found online.

Shopping List

Part Description Quantity Farnell Number

74HC240 IC for CNS Nv Neurons and Motor Drivers

18 (order 25, as only marginally more expensive)

380-0696

74HC04 IC for Bicore 1 380-362

H-Bridge To drive motors 9 From servo

Resistors & Capacitors Several In workshop

Potentiometer 1M From toolbox

LED To observe the outputs of Pacemaker and every motor neuron

25 (just in case) 515-693

Screws Several From Workshop

1x4 Crimp Terminal Housing Bus Connector ~20 511-729

Crimp Terminal Bus Connector ~80 182-3218

Pins (crimp socket) Bus Connector ~20 865-620

Stripboard 1 451-058

Servos 9 From Shop


biomorph snake robot

Technology

biomorph snake

developed robot

analogue biomorph robots

friends i

special thanks

electronic engineering

continuous support

natural counterparts