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  • 1. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO A biomor ph snake-like robot Re s e a rch, D evelopment a nd Behav iour by Anthony Hsiao Department of Electrical and Computer Engineering, National University of Singapore Department of Electrical and Electronic Engineering, Imperial College LondonImperial College London, National University of Singapore, July - August 2006 1
  • 2. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOAbstract 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.Imperial College London, National University of Singapore, July - August 2006 2
  • 3. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOAcknowledgements 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!Imperial College London, National University of Singapore, July - August 2006 3
  • 4. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOTable of ContentsINTRODUCTION................................................................................................................. 5 RATIONALE – SO WHY BUILD A BIOMORPH SNAKE-LIKE ROBOT?...................................................... 5 AIMS AND OBJECTIVES........................................................................................................... 7 Aims........................................................................................................................................ 7 Objectives................................................................................................................................8 Key challenges........................................................................................................................ 8BACKGROUND...................................................................................................................9 SNAKES, VERTEBRATES AND THEIR NERVOUS SYSTEM......................................................................9 Lateral Undulation.................................................................................................................. 9 Concertina ............................................................................................................................10 Sidewinding ......................................................................................................................... 10 On vertebrate locomotion and their Central Nervous System............................................ 10 NERVOUS NETWORKS........................................................................................................... 12 The Nv Neuron..................................................................................................................... 12 The Bicore.............................................................................................................................13 The Suspended Bicore..........................................................................................................13 The Master-Slave dual Bicore.............................................................................................. 15 Implex Feedback in Nervous Network Controllers............................................................. 15THE ROBOT.......................................................................................................................18 ABSTRACTION..................................................................................................................... 18 ANATOMY..........................................................................................................................20 MODELLING AND SIMULATION................................................................................................22 Lateral Undulation or general wave-like motion.................................................................22 MECHANICAL DESIGN.......................................................................................................... 24 ELECTRONIC DESIGN............................................................................................................ 27 General Circuit Design Guidelines.......................................................................................27 The Pacemaker..................................................................................................................... 29 The Motor Neuron Module ................................................................................................. 32EVALUATION....................................................................................................................35 NORMAL OPERATION............................................................................................................35 Discussion............................................................................................................................. 38 WORM-LIKE OPERATION....................................................................................................... 41CONCLUSION................................................................................................................... 46FUTURE WORK.................................................................................................................47REFERENCES.....................................................................................................................48APPENDIX I – LIST OF COMPONENTS.......................................................................... 50Imperial College London, National University of Singapore, July - August 2006 4
  • 5. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOI nt ro duc t ionThis 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 aproject in. The task to explore the marriage of biomorph robotics and nervous network controlwith the design of a robotic snake emerged and is described in the following sections of thisreport.At first, a brief Introduction to the topic, starting with the Rationale for pursuing biomorphrobotics and the concrete Aims and Objectives as well as Key Challenges of this project, isgiven. This is followed by an overview of relevant Background material, in which the two keyareas Snakes, vertebrates and their nervous system and some theory on Nervous Networks aredescribed. The following section on The Robot itself draws together key inspirations gainedfrom preceding sections, and the Abstraction performed as well as the robots Anatomy,Modelling and Simulation carried out to verify its operation and its Mechanical and ElectricalDesign are described in detail. Finally, the work is assessed critically in the Evaluation, andsome 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 evermore complex and capable robots making the headlines. The improvement in technology,continuous miniaturisation and ever more powerful information processing capabilities haverevived the field and it has picked up momentum. While most robots employed in industry andmanufacturing are merely sophisticated purpose made machines, and relatively dumb, industryas well as academia have been making good progress on intelligent and autonomous robotsand systems. Existing robots include toy robots such as SONYs robotic dog AIBO or thefamous Robosapien by Wow Wee Ltd., Hondas humanoid robot ASIMO (which is employedat the Honda Headquarters as a receptionist!) or various football playing robots such as theImperial College London, National University of Singapore, July - August 2006 5
  • 6. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOFIRA RoboWorld Cup winning Manus-1 humanoid, developed at the National University ofSingapore. The military have developed numerous autonomous Unmanned Aerial Vehicles(UAV), which can be thought of as autonomous purpose built flying robots. While recenttrends and developments have been very promising, the scientists and engineers are still faraway from creating anything close to human-like intelligent machines, let alone robots that canbe 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, remainscience fiction, for now.The benefits to man of such robots are clear, and arguably, it is worthwhile pursuing researchin this area; at this stage of robot development and sophistication, dangers and potentialthreats they could pose to humanity are best left to writers and philosophers, discussing thesehere would go beyond the scope of this work.However, there is a different school of thought, pioneered by Mark Tilden and his paper onLiving Machines [20], which explores the creation of robots as an attempt to create artificiallife, starting from simple creatures. Rather than creating complex humanoid (or otherwise)purposefully build slave or helper robots, building living machines is about creating robots thatdisplay the basic characteristics of simple living creatures, which do not necessarily have tohave useful (to the human) abilities (yet).In particular, these so called biomorphs are autonomous, analogue, electro-mechanical devicesthat are biologically inspired and built using minimalistic circuitry without microprocessors,which will be explained in more detail in the section on Nervous Networks. These creaturescan display complex behaviours, resembling that of real (simple) animals through their highlynon-linear interaction with their environment, and their simple analogue nature allows for anadaptivity and survivability that normal digital robots can hardly match. These robots, it ishoped, can also help to gain a better understanding of simple biological creatures and theorigin 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 asfrom a robotics point of view.Snakes have fascinated people for millennia, with their smooth, seemingly effortless andelegant motion which captivates observers, and their deadly poison and threateningappearance, which still frightens many people. However, snakes share a long and relativelyImperial College London, National University of Singapore, July - August 2006 6
  • 7. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOwell documented history with man, including appearances in the old testament, ancientEgyptian mythology or Chinese medicine.However, snakes still have a lot to offer, and studying serpentine (i.e. snake-like) locomotionand the underlying neural mechanisms is believed to fill gaps in evolutionary knowledge. It isestablished knowledge that terrestrial legged locomotory gaits evolved from aquatic swimminggaits, however details of this transition are still subject to controversy. Decoding the underlyingmechanisms of serpentine locomotion in itself can be interesting, but is also hoped to shedlight on the changes to the neural mechanisms that were necessary to enable life to make thetransition from aquatic swimming to terrestrial walking.To date, most commercial applications that have derived from snakes have been in the luxurygoods industry, through skinning them and making expensive shoes, bags or purses. However,serpentine locomotion can potentially find commercial applications in different fields ofrobotics, where conventional robots fail. Successful snake-like robots have many advantagesin terms of size or shape over wheeled and legged robots, for example in environments withtight spaces, long narrow interior traverses such as pipelines, nuclear power plants or looseterrains such as earthquake sites.Advancing the studies of snake-like robots is indeed worthwhile for various reasons, althoughthe work as this stage is foundational and far from to being concerned with different roboticapplications.Aims and ObjectivesThe initial task of exploring biomorph robotics and the application of nervous networkcontrollers on a snake-like robot is manifested in the following three project aims, which arehoped 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 readers thoughts on life and artificial life manifested through robotsImperial College London, National University of Singapore, July - August 2006 7
  • 8. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOObjectives 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 createdKey challengesThe existence of snakes and other snake-like creatures shows that it is possible to achieve thiskind of complex behaviour through neural control. The challenge for biomorph robotics is toreveal or propose bio-inspired models of these underlying mechanisms, and to apply them toreal robots. As will be described in the section on Nervous Networks, a critical factor forsuccess is the harmonic interplay between robot body and controller, and unsuitablemechanical design can deny an otherwise sound controller to be successful. For this heavilytime 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 timeImperial College London, National University of Singapore, July - August 2006 8
  • 9. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOBackg roundThere 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 describesrelevant background material on both topics.Snakes, vertebrates and their nervous systemThe snake is a vertebrate, an animal with a backbone, and has the largest number of vertebraeof any animal: between 100-400 vertebrae depending on the species. Unlike most othervertebrae, their skeletal form and structure is quite simplified and only has three types ofbones: skull, vertebrae and ribs. This simplicity of a repeated structure and the relatively limitedmotions between adjacent pieces can be compared to robot modularity, and are worthwhile tokeep in mind for later mechanical design.Although snakes and other limbless animals have been studied for centuries, little research hasfocused on the detailed mechanics of serpentine locomotion. However, qualitatively, there areseveral broad classes of limbless locomotion, including concertina, lateral undulation orsidewinding, amongst other less common forms, described below.Lateral UndulationThe most frequently used form of snake locomotion is calledlateral undulation, which is also the most primitive, and is whatmost people can relate to, when thinking of snake motion.During lateral undulation, a wave propagates down the snakesbody, which pushes against irregularities of the ground it ismoving over to generate a forward force strong enough toovercome sliding friction, as shown in Figure 1. All pointsalong the body of the snake follow a more or less identicalsinusoidal path [8]. It is unsuited for smooth, low-friction Figure 1: During lateral undulation,surfaces and narrow corridors, and not suited for shorter and the snake uses continuous sliding contacts to propel the bodystouter, or large heavy-bodied snakes.Imperial College London, National University of Singapore, July - August 2006 9
  • 10. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOConcertinaConcertina locomotion is usually performed in confined spacesor tunnels, or other places where large amplitude undulationcannot be performed, like tree trunks. During it, a part of thesnakes body is stable, for example wrapped around a trunk orspread inside a tube, while the rest of the body is movingforwards. It is best summarised by Figure 2. This motionderives its name from the accordion like instrument, forobvious reasons. Figure 2: Concertina locomotion is usually used in confined spacesSidewindingSidewinding 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 lateralundulation, and is very similar, in spite of appearances. It is like lateral undulation in 3D, wherethere are only a few points of contact betweenthe snake and the ground, where the contact isnot sliding, but static, rather like rolling a wheelor bent rod over the surface. Thereby, the headcan appear to leap forwards. Because thesnakes body is in static contact with the ground,imprints of the snake body can be seen in thetracks, and each track is almost exactly as long Figure 3: Sidewinding is like lateral undulation in 3D,as the snake, as shown in Figure 3. A through with rolling contacts between a few points of the body and the groundreview of snake motion can be found in [3].On vertebrate locomotion and their Central Nervous SystemApart from studying snakes different locomotion gaits, it is necessary to have a closer look atwhere these are generated: the nervous system.The vertebrate central nervous systems (CNS) generally consist of two parts, the brain and thespinal 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, whilethe spinal chord performs routing and is responsible for our reflexes and motor functions. TheImperial College London, National University of Singapore, July - August 2006 10
  • 11. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOdifferent animal locomotive gaits can be very complex and move many DOF through rhythmicexcitation 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. Animallocomotion needs to be well coordinated in order to work, and indeed it is, as we can see everyday in real life or on TV. In general, locomotion is controlled by the interaction of threecomponents: 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 oftenbe initiated by simple electric or physiologic stimulation. These CPGs are located in the spinalchord, distributed across different oscillatory centers. The lamprey, and eel like fish, forexample has a CPG chain of approximately 100 segmental oscillators distributed from head totail [6].Sensory feedback is essential for shaping and coordinating the neural activity with the actualmechanical movements. The main sensory feedback to the CPGs is provided by built-in sensoryreceptors in joints and muscles. It is especially important in higher vertebrates with uprightposture such as mammals, because the limbs of those vertebrates play an important role inposture control (i.e. supporting the body) in addition to locomotion. A whole set of reflexesexist to coordinate neural activity with mechanical activity, which we observe literally withevery blink of an eye [6].Supraspinal control, then, is the control that initiates and modulates locomotion throughdescending 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 highercognitive decision, a command sent by one or more of these locomotor centers propagatesdown (e.g. from head to tail) a signal that addresses the right CPGs and thus initiates thedesired 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 whengetting burned, demonstrate, that motion, or locomotion, does not necessarily require highlevel cognitive processes, but can be a product of coordinated, but mechanistic, interaction ofImperial College London, National University of Singapore, July - August 2006 11
  • 12. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOCPGs within a nervous system.Nervous NetworksNervous 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 limbedrobot [21]. In the following sections, the reader will be introduced to the Nv Neuron, theBicore, the Suspended Bicore, the Master-Slave dual Bicore configuration and ImplexFeedback in nervous network controllers.The Nv NeuronThe basic pulse delay circuit acts as an artificial neuron, also referred to as Nv Neuron (nervousneuron), 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, butthat is not a necessity), and has a time constant = RC . Qualitatively, its operation is asfollows: 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. Figure 4: a nervous neuron 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 activelow pulse at its output, with duration tp, which is directly proportional to the time constant ofImperial College London, National University of Singapore, July - August 2006 12
  • 13. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO V i the circuit, such that t p= RC ln   . A more detailed analysis of this circuit is omitted here, V th 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. Theinterested reader may consult [23], [9], [18] or [26] for more detailed information.The BicoreOf more interest here than the simple Nv neuron is the so called bicore, a self oscillatingarrangement of two Nv neurons connected together in a circular topology, as shown in Figure5. Qualitatively, the analysis is similar to that of a single Nv neuron, but with the differencethat the input applied is not a step input, but a square wave. Lets consider a single Nv neuronagain, 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 neurons time constant), and goes back high. Figure 5: The Bicore is an arrangement 3. The input goes back low after a time greater than tp, of two Nv neurons connected in a circular topology 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 BicoreA 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 tworesistors connected together, instead of being grounded, generating square wave at the inverterImperial College London, National University of Singapore, July - August 2006 13
  • 14. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOoutputs. A brief but more thorough analysis of its operation here (a comprehensive descriptionof 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. Figure 6: The suspended bicore 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 neuronsoutputs, which have a period depending on the RC values. However, there are a few interestingproperties 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.Imperial College London, National University of Singapore, July - August 2006 14
  • 15. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 6. The exact period of this circuits 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 BicoreA popular use of the suspended bicore is in the Master-Slave configuration, Figure 7, where thesuspended bicore can be thought of as a pacemaker to the rest of the circuit. This arrangementcan be thought of as a normal bicore, with its normally grounded resistors connected to theoutput of a suspended bicore. The master is the suspended bicore, oscillating away at a fixedperiod (which is different to when the samesuspended bicore was running in isolation), whilethe slave is the normal bicore, with its operationdictated by the master via the coupling resistors.Again, a detailed account of the operation of thiscircuit can be found in [2], and will not be Figure 7: Master-Slave configuration with areproduced here. A qualitative understanding of suspended bicoreits operation is sufficient.Both bicores produce square waves at their respective outputs, where the outputs of the slavehave a delay ∆t on the master outputs, that is given by the coupling resistors and capacitors (orcan be measured). Their frequencies and duty cycles are otherwise the same. It can beappreciated, that the period of this circuit, i.e. the period of the master bicore, is shorter than ifthe master was operated on its own, because the coupling resistors introduce an extra path forthe charging and discharging currents, thereby accelerating the charge or discharge of thecapacitors, and hence resulting in a faster oscillation, as the threshold voltages are reachedearlier. The master-slave arrangement is commonly used to control quadruped walking robots,i.e. robots that have four legs.Implex Feedback in Nervous Network ControllersUsing the basic building block of nervous networks, the Nv neuron, many differentconflagrations and control networks can be achieved, and popular arrangements go beyond thebicore, but include networks with four, six, eight or more Nv neurons. But what is it, that makeNv nets so special?Imperial College London, National University of Singapore, July - August 2006 15
  • 16. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOFirst of all, they are simple and cheap. Using a few standard components, complex controlpatterns can be generated using nervous network technology.Furthermore, these networks are so sensitive on their topology and external connections, in away that allows them to display adaptivity. As illustrated on the example of the master-slaveconfiguration, where the presence of the slave bicore reduces the oscillation period of thesuspended bicore (compared to isolated operation), the nervous network controllers react tothe load connected to their outputs; the slave bicore can be thought of as a non-linear load onthe suspended bicore.The (non-linear) load of a nervous network controller, which commonly comprises mechanicalactuators such as motors, has an effect on the operation of the controller itself, which in turncontrols the way the load behaves. Also, the environment that the actuators interact with hasan effect on the actuators itself, for example moving up a steep slope has a different loadingcondition on a motor than moving over horizontal ground. Then, for example, a motor woulddraw more current from the supply rails, which has an effect on the Nv neurons, which are alsofed 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 inFigure 8, and it is adaptive, because the controller can react to the environment, e.g. byincreasing the period of oscillation of a pattern generator. Actuators are a load Controller Drive to the controller actuators Actuators Actuators Environment is Environ- Move robot within loading the actuators ment environment Figure 8: Complex, adaptive interaction between the nervous network controller and its environmentImperial College London, National University of Singapore, July - August 2006 16
  • 17. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOThe nervous network controller can be thought of as trying to converge towards an optimalstate, wherein the optimality condition is determined by the mechanical body of the robot, thetopology of the nervous network and the environment encountered. It is important to ensurethat 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 thatthe nervous network could converge to.Under certain circumstances, where the controller is in harmony with the mechanicalconfiguration and the encountered environment, a robot with a nervous network controller candisplay emergent behaviour, which is unexpected, but desirable behaviour. This could includethe ability for a robot to climb or avoid obstacles, or to display other characteristics usuallyattributed 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 advantagesover conventional microprocessor control used for robotics. Through its adaptivity, it gives arobot the most basic but also most important ability, which is the ability to survive in anunstructured environment.Imperial College London, National University of Singapore, July - August 2006 17
  • 18. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOT he Rob otThe underlying design principle for the robot is simplicity. The reason for choosing nervousnetwork technology to control it is twofold, its ability to display complex and adaptivebehaviours on one hand, and its tremendous simplicity on the other. The following sectionsfirst describe abstractions performed to simplify the problem, the robots anatomy, i.e. itsconceptual architecture, performed simulations to verify this architecture, and finally therobots detailed electrical and mechanical design.AbstractionIn order to address the problem of building a biologically inspired snake like robot, anappropriate abstraction from a real snake has to be performed. In particular, one motionscheme shall be determined for implementation, and the robots complexity in degrees offreedom and approximate dimensions be chosen.The section above introduced three common forms of snake locomotion, namely lateralundulation, sidewinding and concertina motion. Out of these, sidewinding is too complex forsimple implementation, as it would require the robot to be able to leap forwards, and carefulbalancing would have to be applied for it to move in 3D. It would also place a lot of emphasison mechanical design, and therefore goes beyond the scope of this work. Concertina is similarto lateral undulation in some ways, but also more complex to implement on a robot. It wouldalso restrict the robots motion to confined spaces. Lateral undulation however is potentiallysuitable to be implemented using a simple controller, as it is the most simple motion schemeobserved on real snakes. Thus, the robot shall imitate lateral undulation as target motionscheme. Indeed, not only one particular curve, but any snake-like undulating motion of therobot’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. Thedegrees 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 thiscase of a snake-like robot, the more smoothly the robot will be able to move. The robot bodywith its links can be thought of as sampling a smooth snake-like curve, and the more samplesthere are, the more closely to the original (i.e. the snake) it can be, and surely enough, in spiritImperial College London, National University of Singapore, July - August 2006 18
  • 19. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOof the sampling theorem, there is a certain minimum number of links, below which the robot isguaranteed 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 modeldoes not affect the total length of the robot, as shown in Figure 9. In choosing an appropriatenumber of links, there is a trade off between robot (and circuit) complexity and motionsmoothness. (An impressive resource for robotic snakes is given by [22], and Dr. Millers S2snake robot has a sufficient total of ten degrees of freedom allowing the robot to move verysmoothly). An educated guess is made, and nine links and thus nine degrees of freedom ischosen, which is believed to be more than sufficient to allow for smooth snake-like robotmotion.Intuitively, snake-like undulating motion can be achieved by such a chain of links and joints bypropagating a wave down the chain. Nervous network controllers are simple controllers, wherecommonly used control signals include on-off(i.e. Vcc or GND) or forwards-backwards signals,suitable to drive DC motors, and are tapped offthe output nodes of the network. In thisapplication, motors could be used directly asjoints, and the nervous network be used tocontrol these motors, e.g. switching them on andoff, or driving them clockwise and anticlockwise.Such a chain of linked motors could then be usedto drive the robotic snake body, where each ofthe joints in Figure 9 could represent a motor. Figure 9: A snake-like robot can be thought of as sampling a snake-like curve. a) a snake like curve b)The challenge then is to determine the correct set robot with high degree of freedom c) robot with small degree of freedom, both sampling the snake-like curveof control signals the nervous network has toproduce in order to liven up the robot. Once this is achieved, further investigation into turningmechanisms as well as successful forward locomotion are necessary to complete the firstobjective.Assuming a robot is able to survive in a given environment, i.e. it does not need permanenthuman intervention for maintenance or otherwise (otherwise there is not much autonomy to behad), it will only need a navigation strategy (as there shall be no remote human control) andImperial College London, National University of Singapore, July - August 2006 19
  • 20. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOsufficient 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 networkcontroller 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, theseanimals 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 fleeor confront. In a similar fashion, it is legitimate for the robots navigation strategy to compriseindeterminate roaming with robust terrain handling abilities, derived from the interaction of thenervous network controller with the mechanical body and its environment.AnatomySo what should the robot look like? The work done here tries to draw its inspirations fromnature, so the robot shall have an anatomy similar to a snakes anatomy. This is obvious, as it isa snake-like robot, but what is meant here, is that the robot should, apart from looking like along flexible trunk, have a neural architecture that controls the behaviour of it. Disregardingpossible cognitive processes that happen within a snakes brain, and focusing purely on itslocomotive mechanisms, the robot shall use a nervous network controller that serves as anartificial 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, oneslave CPG for every joint, can be used to achieve just that. Each slave bicore is a spinal CPGitself and represents a local motor neuron dictating the motion of exactly one joint, while thesuspended bicore can be thought of as providing the supraspinal control necessary to initiateand dictate the (motor-) neural activity; it functions as a pacemaker.There are two obvious possible resulting neural architectures that could be adopted here, one isa centralised, parallel star configuration, the other one a decentralised, serial or lineconfiguration, as shown in Figure 10. For each of the configurations, the pacemaker and theCPGs 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 therequired phase that each CPG has to have with respect to its predecessor, while the lineImperial College London, National University of Singapore, July - August 2006 20
  • 21. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOconfiguration allows for the relationships to be identical. This allows the circuit, and the robotitself, to be more modular and simple. Furthermore, the line configuration resembles the CNSof a vertebrate more closely, with supraspinal control propagating down the links. Also, from acircuit point of view, the decentralised line configuration may be more advantageous, as eachCPG is coupled to its two nearest neighbours and is the master of one slave, whereas in the starconfiguration, there is no direct coupling between two consecutive CPGs, and only thepacemaker is the superior master. It is intuitive, that an external factor which affects one linkshould 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, theserial line configuration is chosen as nervous architecture.Following the nervous architecture, the output or control signals from the nervous networkcontroller are taken directly off each bicore (excluding the pacemaker), and are square waveswith a given phase, determined by the coupling resistances of the slaves. Conveniently, thesecan be used to drive or control DC motors, and thereby switching them forwards andbackwards periodically. Is a chain of such switched DC motors, i.e. constant angular velocities Alternative Nervous Architecture A. Star Configuration B. Line Configuration CPG Pacemaker CPG Rel 1 Rel 9 Rel 1 CPG Rel 2 Rel 3 CPG Rel 2 CPG CPG CPG Rel 8 Rel 4 Pacemaker CPG Rel 7 Rel 3 Rel 6 Rel 5 CPG CPG CPG CPG Rel 6 Rel 5 Rel 4 Rel 7 CPG Rel 8 Rel 9 CPG CPG CPG CPG CPG Figure 10: Two alternative nervous architectures: A. a parallel star configuration, B. a serial line configurationImperial College London, National University of Singapore, July - August 2006 21
  • 22. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO(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 robots anatomy is the power supply. Again, twoobvious architectures are possible, namely to have one central battery or power supply whereall modules, i.e. CPGs and motors, source from, versus a decentralised power supply, whereeach module has its own power supply. Here, the consensus is, that one power source is thebetter 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 thepurpose of using a nervous network controller.Modelling and SimulationBefore diving into practical adventures with a soldering iron, an appropriate simulation canhelp to verify or reveal flaws about the above abstraction. In this case, it is worth verifyingwhether the chain of phase shifted square waves controlling DC motors would indeed result inundulating motion. Furthermore, certain parameters such as required period or delay could beextracted from the simulation, saving possibly tedious practical experimentation. This sectiondescribes relevant efforts undertaken to verify that undulating motion can be achieved, somerelationships extracted from the model with regards to the delay necessary in order to achieve acertain wave.Lateral Undulation or general wave-like motionFrom the abstraction above, the robot can be modelled as a chain of links joined together. Ateach joint, a DC motor provides an alternating torque or angular velocity (depending on howyou look at it), which is controlled by a square wave signal, dictating it to go clockwise oranticlockwise. To achieve the desired wave like motion from this, only a few parameters haveto 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, whenmoving, real snakes have far more flexibility than the 9 DOF robot proposed here, and usuallydisplay a propagating wave with several peaks and troughs. When thinking back of the analogyto the sampling theorem however, it is obvious, that with 9 DOF, or nine samples, the wave onthe robots body cannot be much shorter than its length. So how long should the wave be?Imperial College London, National University of Singapore, July - August 2006 22
  • 23. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOTo address this, the robot of unit length can be consideredas displaying a standing wave of wavelength λ. Theshorter it is, the more peaks and troughs are there on therobots body. As shown in Figure 11, this has significantimplications on the snake-like-ness. A balance betweenthe robots physical length and the wavelength has to befound. Arbitrarily, but intuitively, a wavelength of about0.66 robot lengths, such that there are 1.5 wavelengthspropagating through the robots body, is chosen. Figure 11: What wave should propagate theThe implications on the phase between the individual robots body?square waves are as follows. If the first joint is used as reference, then phase shift betweensuccessive joints square waves should be such, that they add up to 150% on the 9th link, if 1.5waves worth are to be standing on the robot. Therefore, Joint Phase Shifteach shift is s = 150% / 8 = 18.75% between two 1 0.00%successive joints, as shown in Figure 12. The time delay 2 18.75% 3 37.50%between two square waves is then sT seconds, where T is 4 56.25%the period of the square wave. As a matter of fact, the 5 75.00%exact matching of these phases does not matter, but is a 6 93.75%fair starting point. If the robot had 1.4 or 1.6 wavelengths 7 112.50% 8 131.25%on its body, it would still be acceptable, especially as 9 150.00%1.5λ was chosen arbitrarily. Figure 12: Phase relationship between jointsThe symmetric and continuous wave motion displayed by the robot relies on the square wavesignal to be of 50% duty cycle. Every flat top or bottom of the signal represents the motorgoing in either clockwise or anticlockwise direction for the given time (i.e. with 50% dutycycle, the motors are turning in either direction for half the period of the signal). Clearly, theperiod of the signal and the (assumed constant in an ideal case) speed of the motor have to bebalanced such, that the robot does not curl up at once (if the motors are too fast, or the periodtoo long, or both). On the other hand, the period and motor speed should not be too short orslow either, otherwise, the robot body would hardly move, but only vibrate. Assuming that adriven DC motor under no load generates a constant angular velocity, the whole system wasmodelled using Nastran, a physical modelling software. Therein, a chain of blocks resemblingImperial College London, National University of Singapore, July - August 2006 23
  • 24. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOmotors 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 inFigure 13, with given phase relationship as defined above, from an input file generatedmanually.Without going into unnecessary detail, inspection of the video generated by the simulation didindeed display a propagating wave that enables the chain of blocks to move in a wavelikeform, with about 1.5 wavelengths displayed. The simulation was run using several differentperiods, all yielding the same motion, but at different speeds, as expected. Simulated Phase Shifted Signals Figure 13: Simulated phase shifted signals to four joints/motors (not to scale)Mechanical DesignThe robot as abstracted should work; the simulation confirmed this. However, this was donefor a general case, and verified the concept (that a sequence of phase shifted square wavesignals driving DC motors should result in undulating motion) rather than any particular roboticdesign. As mentioned earlier, the nervous network controller can be thought of as interactingwith the robots environment through its body. For a moving robot, this means that a desiredImperial College London, National University of Singapore, July - August 2006 24
  • 25. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOand stable gait can only be achieved by the controller, if the mechanical body allows for one. Ifthat 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 thebody 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 interplaybetween controller, robot body and environment), then the network may still be able toproduce a useful gait, as the environment feeds back to the network through external loadingof 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 tomechanical bodies, which makes tuning, or rather manually evolving the robot, as well as ahealthy amount of engineering gut feeling, rather useful.Following the design philosophy of simplicity, it would be desirable to manufacture the robotfrom as few mechanical parts as possible. Using hacked servo motors, a modular and simplebut appropriate mechanical design was achieved, as follows.Servomotors are widely used in robotics, due to their strength, accuracy, agility andcompactness. However, they also offer another advantage that shall be exploited here: theircasing. For the application here, a simple DC motor would do the job, as all that is required issimple alternating rotational motion. However, ordinary DC motors are far too fast, andpossibly too weak for this application, while ordinary available geared motors are usually verybulky. A servomotor on the other hand is basically a highly geared, compact DC motor with acontrol 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 isexactly what is required for this application.The Futaba S3003 servo motor, Figure 14, was chosen, as it was theslowest 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 anymodifications, and nine of these can be used as the robot bodydirectly, when linked together. The flat side of the body is suitable forattaching small circuit board to, and overall, a very modular designcan be achieved. In fact, as was determined experimentally, the motor Figure 14: Futaba S3003 servo motor, fromis still too fast at the rated 4.8V, which is why the supply voltage to www.futaba-rc.comImperial College London, National University of Singapore, July - August 2006 25
  • 26. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOthe robot will have to be reduced, slowing down the motor and reducing its torque by tolerableamounts.The mechanical links between the motors are made fromsimple (scrap) pieces aluminium, cut to shape, and weremanufactured manually. Here, it is important to keep the links,as short as possible, in order to minimise the distance betweentwo neighbouring servos, thereby reducing the torque couplingof each motor to the rest of the body.As it is, the robot body is very simple, consisting of ninehacked servo motors linked together by short and lightaluminium links. It is desirable to attach wheels to the servocases, so as to provide differential friction, i.e. low, rolling Figure 15: Mechanical aluminiumfriction along the long side of the servo, and a lot of resistance links connecting the servos togetherto motion (against the wheel) sideways to the servo. Without wheels, the robot just slides onits belly, which is not very ideal. First of all, the servo casings are not suitable for sliding, andsecondly, and more importantly, there are no constraints for the motion. Without wheels, therobots lateral interaction with the environment is not constrained, and the solution space forthe nervous network controller is (essentially) infinitely large. It would be very unlikely that thecontroller managed to converge towards a stable gait without any constraints on themechanical motion of the body. Adding wheels however would discourage motion in thesideways direction, and encourage motion in the longitudinal direction, narrowing down thesolution space and thereby assisting the nervous network controller to converge. Unfortunatelyhowever, there was not enough time to add wheels to the robot body, which is the first itemfor future work. The consequences of the lack of wheels, and alternative solutions, will bediscussed 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 bodyImperial College London, National University of Singapore, July - August 2006 26
  • 27. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOElectronic DesignThe previous section on abstraction has developed the plan for the robot, the implementationof the easy part was described in the section on mechanical design above. Here, the crucialpart, 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 toits 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 tothe pacemaker or the predecessor MNM respectively. Each of the nine motors will have oneMNM, while the pacemaker is independent.General Circuit Design GuidelinesThere are some circuit design concepts, that are common to both pacemaker and MNM, andare 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,Imperial College London, National University of Singapore, July - August 2006 27
  • 28. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 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 powerImperial College London, National University of Singapore, July - August 2006 28
  • 29. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 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 PacemakerThe pacemaker circuit is very simple. It is a suspended bicore, and has no inputs, and twoneuron outputs that link to the rest of the circuit through the first MNMs coupling resistors, inaddition to the power rail connectors.The IC employed here is the 74HC04 (a generic datasheet can be found online) hex inverterchip, featuring six independently usable inverters, although only four are used here. It uses two0.3µF 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 alarge enough pot) could have been used on its own, it allows for a zero resistance setting, andtherefore a short circuit inside the bicore, which is undesirable. In order to avoid that, thesecond fixed bicore resistor is used. Therefore, the circuit has a nominal (unloaded) timeconstant ∈[0.168,0 .468] s . Once the entire ACNS is connected up, loading effects willchange this range, and so the achievable periods of oscillation of the pacemaker will change,too.The LEDs are driven through a separate pair of Pacemakerinverters, so as to minimise the effect that switching IC Chip: 74HC04 hex inverterthem has on the ACNS, as explained in the guidelines. Resistors: 1 bicore resistor 560kΩ, 1They are rated at about 20mA to 25mA diode current bicore trimmer pot 1MΩ, 2 LED resistors 150ΩId, and as the output voltage of the inverter is 3.5V, a Capacitors: 2 bicore capacitors, 0.3µF150Ω driving resistor was chosen. The entire LEDs: 2 HE red LEDspacemaker circuit is shown in Figure 18 below. Figure Inputs: None19 Illustrates the compact circuit layout on the Outputs: 2 neuron outputs to first MNM, 2 identical neuronveriboard, where horizontal red lines represent the output measurement pinsmetal strips on the board, interruptions of the red line Power: Vcc and GND railsrepresent cuts made to the strips, and green lines Figure 17: The pacemaker in a nutshellrepresent connecting wires on the board. Figure 20 shows a close-up photo of the actualpacemaker circuit board, clearly showing the components and the connectors used.With the entire ACNS connected together, the required oscillation period of the circuit wasImperial College London, National University of Singapore, July - August 2006 29
  • 30. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOdetermined experimentally. With a higher supply rail voltage than 3.5V, the motors would betoo fast, therefore any snake-like motion would have been similarly fast, resulting in anunrealistic motion of the robot (imagine a fast forwarded video of a snake, clearly notdesirable). Then, at 3.5V, a suitable period of around 0.5 seconds (540ms) was chosen, so thatthe motors turn less than 30 degrees each way within one period.Imperial College London, National University of Singapore, July - August 2006 30
  • 31. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO Figure 18: Circuit diagram of the pacemaker Figure 19: Veriboard layout of the Pacemaker Figure 20: Photo of the bicore circuit board circuit. Red horizontal lines represent the metal strips on the board, and green vertical lines represent connecting wires.Imperial College London, National University of Singapore, July - August 2006 31
  • 32. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOThe Motor Neuron ModuleThe core of each MNM circuit is a bicore, with its two coupling resistors acting as the onlyinputs (apart from power supply rails). There are four output pins, two pairs of two, namely themain two ACNS outputs pins from the bicore to the next MNM, and two output pins to drivethe motors. MNMThe IC employed here is the 74HC240 (a generic IC Chips: 74HC240 octal inverter,datasheet can be found online) octal inverter, BAL 6686 H-Bridgefeaturing eight separately usable inverters Resistors: 2 coupling resistors on sockets (exchangable), 2 LEDarranged in two banks of four, which can be resistors 150Ωseparate enable or disabled by the corresponding Capacitors: 2 bicore capacitors, 0.1µFone of the two enable inputs. This feature can be LEDs: 2 HE red LEDs Inputs: 2 neuron inputs from precedingused to enable/disable outputs to the motor, if MNMturning abilities are to be implemented, but this is Outputs: 2 neuron outputs to next NMN, 2 DC motor outputsnot done here; only six inverters are used. The Power: Vcc and GND railsMNM features two ceramic capacitors of 0.1µF, Figure 21: The MNM in a nutshelltogether with two coupling resistors of 1MΩ. In order to allow reconfigurations, single pinfemale connectors are used to connect the resistors to the circuit, so that they are easilyexchangeable (see actual circuit). The circuit has a nominal time constant of =0.1s , whichcorresponds to approximately 18.75% of the period (500ms - 540ms), as required for a 1.5wavelengths propagating wave.As in the case of the pacemaker, the LEDs are driven through a separate pair of inverters, so asto 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 duringnormal operation, less than 150mA are typical current requirements. The 74HC240 is not ableto deliver such an output current, which is why a separate motor driver, controlled by thebicore, is required. Fir this is, a H-bridge IC, a common motor driver for this application, fromthe servo control board was de-soldered, and conveniently used. It is powered by the supplyrails, takes its two inputs from an inverter pair, buffered from the ACNSs neuron outputs, andconnects 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 Figure24 and Figure 23 show the compact veriboard layout and a close up photo of the MNM.Imperial College London, National University of Singapore, July - August 2006 32
  • 33. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 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 ModuleImperial College London, National University of Singapore, July - August 2006 33
  • 34. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOThe entire ACNS was manufactured manually in the workshop. Due to the compactness of thecircuits, the H-Bridge had to be arranged in an unideal way. The circuit boards are stuck to theside of each motor case using double sided adhesive pads. 4 bit wide bus connectors madefrom 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, withthe ACNS clearly visible is shown in Figure 25. Figure 25: The complete robot on its side, with the ACNS clearly visibleImperial College London, National University of Singapore, July - August 2006 34
  • 35. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOEva luat ionThe previous sections have described the underlying principle, and the mechanical andelectronic design of the biomorph snake-like robot. Whether it actually works, is describedhere.Normal OperationNormal 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, asdescribed in the section after this.In order to evaluate the robot, a rigid testing strategy to observe and describe the behaviour ofit under different conditions was developed. For each of the different set-ups, an evaluationsheet with relevant criteria is filled out during the testing process. These different testingconditions 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 robotThe results are as follows. 1. Power up head only 1. Behaviour at fixed On power up, the circuit behaves about right. The duty cycle is oscillation period ~50% [49.4%, 50.7%] and Vout is at 3.44V. The period can be (533ms @ 3.5V) fixed at approximately 540ms. 2. Behaviour for The range of frequencies is [300ms,1160ms] varying oscillation 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.Imperial College London, National University of Singapore, July - August 2006 35
  • 36. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 2. Power up entire spinal chord, no motors 1. Behaviour at fixed With the whole circuit connected, the period drops to a saturated oscillation period mode with 189ms period, probably for a similar reason as above, (533ms @ 3.5V) 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). 2. Behaviour for The circuit appears to have discrete levels of ‘allowed’ periods, i.e. varying oscillation 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 1. Behaviour at fixed The circuit with only the first motor connected is capable of the oscillation period right behaviour, oscillating at about the right frequency and duty (533ms @ 3.5V) 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). 2. Behaviour for As expected, the motor turns in each direction for longer/shorter varying oscillation 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).Imperial College London, National University of Singapore, July - August 2006 36
  • 37. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 4. Power up with only M2 1. Behaviour at fixed Also capable of displaying the right behaviour with about 50% oscillation period duty cycle at the right frequency, but it also tends to become (533ms @ 3.5V) 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 1. Behaviour at fixed In general, the closer to the center of the snake the motor is (i.e. oscillation period M5 is the center of the snake), the better its performance, and the (533ms @ 3.5V) 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 1. Behaviour at fixed The first motor M1 always tends to curl leftwards, as mentioned oscillation period earlier, which is attributed to the imbalance of load as seen by the (533ms @ 3.5V) 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 optimalImperial College London, National University of Singapore, July - August 2006 37
  • 38. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 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. 2. Behaviour for Wave like motion changes accordingly over the short range of varying oscillation 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 1. Behaviour at fixed Using wires to loosely restrain the robot at its head and tail, oscillation period without stretching it outwards too much, the motion is much more (533ms @ 3.5V) smooth and closer to the desired motion. The curling of the first and last link is reduced. 2. Behaviour for As the period changes, so does the wave on the robot body, but varying oscillation 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 observationsDiscussionThe robot works partly as desired, but seeing it jerking to live and holding its erraticallymoving 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 andoff as on an airports runway at night. Depending on its initial orientation, the fully connectedrobot may curl up after power up. This is because it has not been initialised, for operation. Ifthe robot is arranged in a straight line initially, then it should, in theory, open and close, goingfrom an I shape to a U shape. To avoid this, the robot has to be pulled and stretched afterImperial College London, National University of Singapore, July - August 2006 38
  • 39. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOpower 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 toone side (either <50% on average, or >50% on average). It is difficult to determine the exactreason for this, but there could be several, including mechanical irregularities, an unevensurface or an insufficient match between the controller and its mechanical body. The first andthe last module tend to curl up more than others. This is attributed to the uneven loaddistribution 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 16pictures in about one second. It can be observed, how the robot is curling in and how it fails todisplay any remotely snake like motion. If this creature was to face the unforgiving nature andevolution, it would not make it very far. Figure 26: Unrestrained snake-like motion: the robot tends to curl up. Pictures taken over approximately 1 secondImperial College London, National University of Singapore, July - August 2006 39
  • 40. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAORestraining the robot at its two ends loosely using wires or strings improves the performance alot. The first and last module, as well as the robot as a whole, are stopped from curling up, anda smooth wave motion is achieved. This improvement is attributed to the fact that fixing thebody applies constraints to the way the robot is able to interact with its environment, therebychanging 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 othertypes of behaviour, for whatever reason (again, this is not yet understood, but the answer couldlie in the realm of non-linear dynamics). Figure 27 shows another sequence of photographstaken within about one second, showing the snake-like motion displayed by the robot (notehow 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 secondImperial College London, National University of Singapore, July - August 2006 40
  • 41. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAODespite the smoother and more desirable motion of the robot, it still does not experiencelocomotion, but only slides over the ground, as there are no wheels or other differential frictionelements employed.Test 2 described how the period of oscillation of the ACNS without motors connectedappeared to be in discrete levels., i.e. linearly changing the resistance in the suspended bicoreof the pacemaker did not linearly change the period of oscillation. This is a prime example ofhow the network would adapt to the external environment. Even though the network wasphysically configured to oscillate at one frequency by the tuning resistor, it oscillated at adifferent frequency, as that was the preferred solution. This effect reappears in several areasof physics, be it the electron-in-a-1D-box model from quantum physics, the physics of anorgan pipe or the quantisation of current in carbon nanotubes. All these examples have to dowith a wave interacting and interfering with its environment (and itself), where only certainconfigurations (e.g. wavelengths or physical dimensions) allow for constructive interference. Inthis 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 observedas 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 existingstate.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 snakerobot is also a very capable worm robot, as described below.Worm-like OperationThroughout this report, the phenomenon of emergent behaviour is often mentioned, anddescribed as unexpected, but desirable behaviour. In fact, emergent behaviour is usually usedin the context of swarm or multi-agent behaviour (e.g. a swarm of bees or fish, the stockmarket, traffic, ...), where complex or advantageous collective behaviour is derived from simpleindividual, often selfish, behaviour of an agent inside the system (e.g. grouping together in aswarm of fish reduces the dangers of encountering predators for each fish).In this context, emergent behaviour is referred to as the unexpected but advantageousbehaviour of the single robot. In particular, robust interaction with a changing environmentImperial College London, National University of Singapore, July - August 2006 41
  • 42. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOdespite lack of preprogrammed sequences or predetermined behaviour.Here, a snake-like robot was designed to exist in a simple planar environment. Yet, Whenturned onto its side, the robot is displaying worm like forwards locomotion. The undulatingpropagating 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 iscrawling towards the camera.What is even more surprising than itslocomotion is that in this configuration, therobot is able to avoid certain obstacles, andclimb over barriers taller than itself. Thereby,the non-linear feedback capabilities andadaptivity of the robot are well exploited. Asthe robot encounters a barrier, as in Figure 29,it first fails to move further, and is pushed toone side, as if trying to avoid it. At the same Figure 28: Screenshot of worm like motiontime, 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, allowingthe robot to get its head up on the barrier. Once this has been achieved, the rest of the bodycontinues 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 29: Sequence of worm climbing over an obstacle out of an enclosure in less than 5 minutesImperial College London, National University of Singapore, July - August 2006 42
  • 43. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOoperation; as the robot is very adaptive, this does not affect the locomotion. Eventually, afterabout four minutes, the robot managed to successfully overcome the obstacle, and continuedto make its way forwards as if nothing happened. Its original shape is restored as it adapts tothe even terrain of the floor again.With these locomotive and terrain handling capabilities, autonomy is simply a question ofadding the right power supply. As the robot happily creeps away, it gives off the impressionthat it could do that forever, marking a simple but noteworthy existence of an artificial form oflife. This simple creature has not had the luxury of millions of years of evolutionaryincremental improvement, blending it smoothly into the (laboratory) environment, yet it isconceivable how it would survive in an unstructured environment. There are many parallelsbetween its existence to simple natural forms of life. Many animals, insects for example, canbe observed to display seemingly erratic and random motion, trying to climb over or out ofsomething (like an enclosing), such as the fly that is trying to leave a room through its closedwindow or the ant that is just roaming around until it finds something interesting, like food. Ina similar way, the robot just roams around, as if looking for something, which, of course, itwont find (because there is nothing it is looking for). It is remarkable however, how the simpleset of reflexes (in this case locomotory reflexes) are sufficient for the robot to display behaviourresembling real living creatures.Then, a question that arises is, what distinguishes a sufficiently complex biomorph robot (onethat may have more capabilities than the worm or snake described here) from a real, simpleanimal 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 aneven more sophisticated field that may arise from it, could challenge the way we think aboutlife.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.Imperial College London, National University of Singapore, July - August 2006 43
  • 44. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO  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.Imperial College London, National University of Singapore, July - August 2006 44
  • 45. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO  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 isadvised to decide for him- or herself what it is that constitutes life.Imperial College London, National University of Singapore, July - August 2006 45
  • 46. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOConclusionThis report set out to describe the research, development and the behaviour of a snake-likerobot. The robot was described and developed step by step, with the underlying designprinciple of simplicity.The mechanical body of the robot was kept very simple and modular, and allows for a nervousnetwork or other controller to be employed. However, due to time constraints, it does notfeature the required wheels and battery that would enable it to move forwards and becomeautonomous. Nevertheless, an emergent behaviour solution allows it to crawl forwards like aworm despite its lack of wheels.Using simple biologically inspired nervous network circuits, an artificial central nervous systemwas devised through abstraction and simulation, and manufactured manually to fit themechanical body. The resulting robot is modular and very simple, using a total of 40 equivalenttransistors (20 inverters) only to control it, and it does not make use of microchips andtherefore does not require any programming. The resulting motion is indeed snake-like, if therobot body is subjected to some mechanical constraints, i.e. if it is suspended at its head andtail using strings or wires.In a different mode of operation, the robot displayed emergent behaviour; lying on its side, thesnake-like robot crawls forwards like a worm, and is able to climb or avoid obstacles. While thesnake-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, andcan be classified as able to survive.As far as the project within the framework of an academic research exchange is concerned, ithas been highly successful and fruitful, not only as most aims and objectives of the projectwere achieved, but also because the process of the work and everything around the exchangeexperience have been a great lesson and very insightful.It is hoped, that the reader could appreciate some aspects of artificial life and robotics, whichcan be a very exciting discipline both from a technical, but also from a philosophical point ofview.Imperial College London, National University of Singapore, July - August 2006 46
  • 47. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOFut ure Wor kThis project is far from finished. The seven weeks spent on devising the robot were veryprecious, and extending the project by weeks or months would not allow for enough time tofinish off everything that could be done. The work that this project could imply can bedescribed broadly as 1. work that has not been accomplished in this project, 2. further work toimprove 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 carriedout, which are worth spending time on. This includes the addition of wheels or otherdifferential frictional elements that would constrain the motion of the snake-like robotsufficiently 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 intermoduledelay (which translates to different propagating wave frequency and amplitude), are worthexperimenting with, to investigate advantageous configurations, as the set-up described herewas derived largely through engineering approximations and estimates.Next, many different improvements to the robot of different scales are conceivable. Aninitialisation circuit that enables the robot to display the right wave motion as it is powered upwould avoid having to stretch and shape the robot manually. Turning capabilities could beadded and, together with the right battery arrangement, would enable the robot to be moreautonomous. Also, different neural architectures other than the line connection could beexperimented with or investigated. An up-scaling of the neural complexity would also beworthwhile, to investigate more complex, and possibly more realistic behaviours, such as heatseeking or feeding (recharging) instincts. In connection with the battery, an optimisation of thepower consumption would be beneficial.Lastly, there is a lot of work that can be done in this relatively new (academic) field ofbiomorph robotics. A lot of things are not yet fully understood, so deriving appropriate robot orcontroller models, together with the right tools and methodologies to analyse and characterisethese would pose a great advance. Also, in order for biomorph robots to be more than fancytoys, new circuits and network topologies have to be engineered, which allow for morecomplex phenomena, such as learning, to surface.Imperial College London, National University of Singapore, July - August 2006 47
  • 48. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOReferenc e s[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 RobotImperial College London, National University of Singapore, July - August 2006 48
  • 49. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAO 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 MillerImperial College London, National University of Singapore, July - August 2006 49
  • 50. A BIOMORPH SNAKE LIKE ROBOT ANTHONY HSIAOApp endi x I – L is t of comp onent sList of components bought. Datasheets of all components can be found online. Shopping List Part Description Quantity Farnell Number 74HC240 IC for CNS Nv Neurons 18 (order 25, as 380-0696 and Motor Drivers only marginally more expensive) 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 25 (just in case) 515-693 of Pacemaker and every motor neuron 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 ShopImperial College London, National University of Singapore, July - August 2006 50