MORPHOGENETIC ROBOTICS: AN EMERGING
NEW FIELD
IN DEVELOPMENTAL ROBOTICS
A SEMINAR REPORT
Submitted
In
Partial fulfillment ...
Department of Computer Science & Engineering
MEA ENGINEERING COLLEGE
Perinthalmanna - 679 325
C E R T I F I C A T E
This i...
ACKNOWLEDGMENTS
I take this opportunity to express my deepest gratitude and appreciation to all those
people who made this...
ABSTRACT
Developmental robotics is also known as epigenetic robotics. This report proposes
that there is one substantial d...
TABLE OF CONTENTS
List of Figures i
1. INTRODUCTION 1
2. MULTICELLULAR MORPHOGENESIS AND ITS
COMPUTATIONAL MODELING
4
2.1 ...
LIST OF FIGURES
Figure No Title Page No.
1.1 Relationships between morphogenetic, epigenetic,and developmental
robotics
3
...
Introduction 1
Dept. of Computer Science & Engineering M.E.A Engineering College
CHAPTER 1
INTRODUCTION
DEVELOPMENTAL robo...
Introduction 2
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understanding biological processes such a...
Introduction 3
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robotics,which belongs to developmental r...
Multicellular morphogenesis and its computational modeling 4
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Multicellular morphogenesis and its computational modeling 5
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Multicellular morphogenesis and its computational modeling 6
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Multicellular morphogenesis and its computational modeling 7
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Morphogenetic Swarm Robotic Systems 8
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CHAPTER 3
MORPHOGE...
Morphogenetic Swarm Robotic Systems 9
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concentration of a...
Morphogenetic Swarm Robotic Systems 10
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Fig. 3.1. Swarm r...
Morphogenetic Swarm Robotic Systems 11
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defined as
(3.3)
T...
Morphogenetic Swarm Robotic Systems 12
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where h defines th...
Morphogenetic Swarm Robotic Systems 13
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shapes can be for...
Morphogenetic Swarm Robotic Systems 14
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assumed to be kno...
Morphogenetic Modular Robots for self-organized Reconfiguration 15
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Morphogenetic Modular Robots for self-organized Reconfiguration 16
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Morphogenetic Modular Robots for self-organized Reconfiguration 17
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Morphogenetic Modular Robots for self-organized Reconfiguration 18
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Morphogenetic Brain and Body Design for Intelligent Robots 19
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Morphogenetic Brain and Body Design for Intelligent Robots 20
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Morphogenetic Brain and Body Design for Intelligent Robots 21
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Morphogenetic Brain and Body Design for Intelligent Robots 22
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Morphogenetic Brain and Body Design for Intelligent Robots 23
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Morphogenetic Brain and Body Design for Intelligent Robots 24
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Morphogenetic Brain and Body Design for Intelligent Robots 25
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Morphogenetic Brain and Body Design for Intelligent Robots 26
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Morphogenetic Brain and Body Design for Intelligent Robots 27
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Conclusion 28
Dept. of Computer Science & Engineering M.E.A Engineering College
CONCLUSION
This report suggests a new fiel...
References 29
Dept. of Computer Science & Engineering M.E.A Engineering College
REFERENCES
[1] U. Alon, “Network motifs: T...
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  • 1. MORPHOGENETIC ROBOTICS: AN EMERGING NEW FIELD IN DEVELOPMENTAL ROBOTICS A SEMINAR REPORT Submitted In Partial fulfillment for the award of the Degree of Bachelor of Technology In Computer Science & Engineering From UNIVERSITY OF CALICUT Submitted by MUHAMMAD NADEEM K.K. (CEAIECS050) DEPARTMENT OF COMPUTER SCIENCE &ENGINEERING MEA ENGINEERING COLLEGE, VENGOOR (P.O) PERINTHALMANNA– 679325 DECEMBER 2011
  • 2. Department of Computer Science & Engineering MEA ENGINEERING COLLEGE Perinthalmanna - 679 325 C E R T I F I C A T E This is to certify that the seminar report entitled “MORPHOGENETIC ROBOTICS: AN EMERGING NEW FIELD IN DEVELOPMENTAL ROBOTICS” submitted byMUHAMMADNADEEM K.K.(CEAIECS050), as the record of the seminar presented by him, is accepted as the Seminar Report submission in in partial fulfillment of the requirements for the award of Degree of Bachelor of Technology in Computer Science & Engineering from the University of Calicut for the year 2011. Seminar Co-ordinator HOD
  • 3. ACKNOWLEDGMENTS I take this opportunity to express my deepest gratitude and appreciation to all those people who made this seminar presentation with words of encouragement, motivation, discipline, and faith by offering different places to look to expand my ideas and helped me towards the successful completion of this seminar report. I grab this opportunity to express my sincere thanks to DR. ABDUL RAUF. H, our respected principal who gave the best facilities and atmosphere for the project work completion. I am especially thankful to MR. SREERAM. S (Head of the Computer Science and Technology Department) for his immense support and guidance. I am also thankful to my seminar coordinators MR.SREERAM and MS. ANU.K.P for their invaluable support and also for the stress and strain they had undertaken for my seminar. Last but not the least; I also thankfully acknowledge the help and encouragement from all other faculties of Computer Science department and my friends. Date: MUHAMMAD NADEEM K.K.
  • 4. ABSTRACT Developmental robotics is also known as epigenetic robotics. This report proposes that there is one substantial difference between developmental robotics and epigenetic robotics, since epigenetic robotics concentrates primarily on modeling the development of cognitive elements of living systems in robotic systems, such as language, emotion, and social skills, while developmental robotics should also cover the modeling of neural and morphological development in single- and multirobot systems. With the recent rapid advances in evolutionary developmental biology and systems biology, increasing genetic and cellular principles underlying biological morphogenesis have been revealed. These principles are helpful not only in understanding biological development, but also in designing self- organizing, self-reconfigurable, and self-repairable engineered systems. The report propose morphogenetic robotics, an emerging new field in developmental robotics, is an important part of developmental robotics in addition to epigenetic robotics. By morphogenetic robotics, it means a class of methodologies in robotics for designing self-organizing, self- reconfigurable, and self-repairable single- or multirobot systems, using genetic and cellular mechanisms governing biological morphogenesis. Report categorizes these methodologies into three areas, namely, morphogenetic swarmrobotic systems, morphogenetic modular robots, and morphogenetic body and brain design for robots. Examples are provided for each of the three areas to illustrate the main ideas underlying the morphogenetic approaches to robotics.
  • 5. TABLE OF CONTENTS List of Figures i 1. INTRODUCTION 1 2. MULTICELLULAR MORPHOGENESIS AND ITS COMPUTATIONAL MODELING 4 2.1 Biological Morphogenesis and Metamorphosis 4 2.2 Computational Modeling of Developmental Gene Networks 5 3. MORPHOGENETIC SWARM ROBOTIC SYSTEM 8 3.1 Swarm Robotic Systems 8 3.2 Metaphor Between Swarm Robotic Systems and Multicellular Systems 8 3.3 Illustrative Results on GRN-Based Swarm Robot Self Organization 13 4. MORPHOGENETICMODULAR ROBOTS FOR SELF ORGANIZED RECONFIGURATION 15 4.1 Reconfigurable Modular Robots 15 4.2. CrossCube—A Simulated Modular Robot 16 4.3. Illustrative Examples of GRN-Based Self-Reconfiguration 17 5. MORPHOGENETIC BRAIN AND BODY DESIGN FOR INTELLIGENT ROBOTS 19 5.1 Computational Models for Neural and Morphological Development 19 5.2 GRN Model for Neural and Morphological Development 21 5.3 Conceptual Framework for Coevolving the development of Robot Hand Morphology and Controller 25 CONCLUSION 28 REFERENCES 29
  • 6. LIST OF FIGURES Figure No Title Page No. 1.1 Relationships between morphogenetic, epigenetic,and developmental robotics 3 2.1 Illustration of the gene expression process 5 3.1 Swarm robotic system represented by a multicellular system 10 3.2 Snapshots showing the emergence of a pattern from 17 robots similar to bird flocking 13 3.3 Snapshots of 8 E-Puck robots forming the letter ‘R’ 14 4.1 Examples of physical and simulated modular robots 16 4.2 Mechanical demonstration of CrossCube 17 4.3 Set of snapshots demonstrating a series of reconfigurable processes during locomotion and climbing 18 5.1 Influence of development on selection directions 19 5.2 Main processes in neural development driven by genetic and environmental control 21 5.3 Example of chromosome for neural development 21 5.4 Self-stabilized cellular growth under the control of a GRN model 24 5.5 Development of a nervous system using the GRN model 26 5.6 Evolved GRN resulting in the neural development 26 5.7 Conceptual diagram for coevolving the development of hand/arm and control 27 i
  • 7. Introduction 1 Dept. of Computer Science & Engineering M.E.A Engineering College CHAPTER 1 INTRODUCTION DEVELOPMENTAL robotics is an interdisciplinary field of robotics that employs simulated or physical robots to understand natural intelligence on the one hand, and to design better robotic systems using principles in biological development, on the other hand. The term developmental robotics is often used interchangeably with other two terms, namely, epigenetic robotics and ontogenetic robotics, which focuses on modeling the postnatal development of cognitive behaviors in living systems, such as language, emotion, anticipation, and social skills. Note, however, that the meaning of epigenetic is not unambiguous in biology. It can either be derived from epigenesist that describes morphogenesis and postnatal development of organisms, or from epigenetics referring to the changes in gene expression that are not caused by genetic changes. Although the difference between developmental robotics and epigenetic robotics has been noticed and topics that are far beyond cognitive development have been discussed, researchers have so far refrained from stressing the difference between developmental robotics and epigenetic robotics. Nevertheless, other terminologies such as developmental cognitive robotics and autonomous mental development have also been suggested to refer to the research efforts on modelling cognitive development, which is believed to be seen as attempts to clarify the confusion caused by treating epigenetic robotics equivalent to developmental robotics. The physical development of animals includes the processes that cause the creation of both the body plan and nervous system, including cleavage, gastrulation, neurulation, organogenesis. Some living organisms, such as amphibians, also undergo a biological process known as metamorphosis, during which both the shape and size of the organisms change. The past decade has witnessed rapid technical and theoretical advances in evolutionary developmental biology (often known as evo- devo) and systems biology in understanding molecular and cellular mechanisms that control the biological morphogenesis. These advances have not only helped us in
  • 8. Introduction 2 Dept. of Computer Science & Engineering M.E.A Engineering College understanding biological processes such as human deceases, but also provided us new powerful tools for designing engineered systems. For example, increasing evidence has been revealed that biological morphogenesis can be regarded as a self-organizing and self-assembling process through cellular and molecular interactions under the genetic and environmental control. In addition, biological morphogenesis has also shown a surprising degree of robustness. Due to the attractive properties that biological morphogenesis exhibits, much attention has been paid to employing genetic and cellular mechanisms for designing robotic systems, in particular for self- organizing swarm robotic systems and self-reconfigurable modular robots. In addition, a large body of research has been performed in artificial life and robotics to design the body plan and neural controller of robots, using an evolutionary developmental approach. which both the shape and size of the organisms change. The past decade has witnessed rapid technical and theoretical advances in evolutionary developmental biology (often known as evo-devo) and systems biology in understanding molecular and cellular mechanisms that control the biological morphogenesis. These advances have not only helped us in understanding biological processes such as human deceases, but also provided us new powerful tools for designing engineered systems. For example, increasing evidence has been revealed that biological morphogenesis can be regarded as a self-organizing and self- assembling process through cellular and molecular interactions under the genetic and environmental control. In addition, biological morphogenesis has also shown a surprising degree of robustness. Due to the attractive properties that biological morphogenesis exhibits, much attention has been paid to employing genetic and cellular mechanisms for designing robotic systems, in particular for self-organizing swarm robotic systems and self-reconfigurable modular robots. In addition, a large body of research has been performed in artificial life and robotics to design the body plan and neural controller of robots, using an evolutionary developmental approach, In this report, it propose that it is high time that the difference between developmental robotics and epigenetic robotics be stressed. To this end, it is suggested that a new term, namely, morphogenetic robotics, be used to denote research efforts dedicated to the application of morphogenetic mechanisms to
  • 9. Introduction 3 Dept. of Computer Science & Engineering M.E.A Engineering College robotics,which belongs to developmental robotics. From our perspective, morphogenetic robotics may include, but is not limited to the following three main topics: Fig. 1.1 Relationships between morphogenetic robotics, epigenetic robotics, and developmental robotics. 1) Morphogenetic swarm robotic systems that deal with the self-organization of swarm robots using genetic and cellular mechanisms underlying the biological early morphogenesis 2) Morphogenetic modular robots where modular robots adapt their configurations autonomously based on the current environmental conditions using morphogenetic principles 3) Developmental approaches to the design of the body or body parts, including sensors and actuators, and/or, design of the neural network-based controller of robots. Note that in epigenetic robotics, autonomous mental development can be seen as an incremental, on-line, and open-ended autonomous learning process situated in physical and social environments. The first neural structure, as well as a basic intrinsic motivation system, which enables robots to learn autonomously, is genetically hard wired as a result of neural morphogenesis (neurogenesis).
  • 10. Multicellular morphogenesis and its computational modeling 4 Dept. of Computer Science & Engineering M.E.A Engineering College CHAPTER 2 MULTICELLULAR MORPHOGENESIS AND ITS COMPUTATIONAL MODELING 2.1 Biological Morphogenesis andMetamorphosis Morphogenesis of animals can be divided into early embryonic development and later embryonic development. Early embryonic development typically involves cleavage, gastrulation, and axis formation, while later embryonic development is mainly responsible for the development of the nervous systems, starting with the segregation of neural and glial cells from the ectoderm germ layer.. Metamorphosis is another interesting stage of biological development. There are two types of metamorphosis, namely, incomplete and complete metamorphosis. For organisms underlying incomplete metamorphosis, there are three developmental stages, in which nymphs look similar to adults. In contrast, organisms that undergo complete metamorphosis have four developmental stages, in which the shape of the organisms changes drastically. Both multicellular morphogenesis and metamorphosis are under the control of gene regulatory networks. When the DNA is expressed, information stored in the genome is transcribed into mRNA and then translated into proteins. Some of these proteins are transcription factors (TFs) that can regulate the expression of their own or other genes, thus resulting in a complex network of interacting genes termed as a gene regulatory network (GRN). New findings in the recent years suggest that some previously so called noncoding genes are transcribed into small RNAs,
  • 11. Multicellular morphogenesis and its computational modeling 5 Dept. of Computer Science & Engineering M.E.A Engineering College which can downregulate the expression of genes through e.g., RNA interference, as sketched in Fig. 2.1. However, most computational GRN models have not yet taken such effects Fig. 2.1 Illustration of the gene expression process. explicitly into account with few exceptions. In the following, the discussion is on computational modeling of GRNs and show how these models can be used for understanding biology and solving engineering problems. 2.2 Computational Modeling of Developmental Gene Networks To understand the emergent morphology resulting from the interactions of genes in a GRN, reconstruction of gene regulatory pathways using a computational model has become popular in systems biology. A large number of computational models for GRNs have been suggested, which can largelybe divided into discrete models, such as random Boolean networks and Markovian models, and continuous models, such As ordinary differential equations and partial differential equations. Sometimes, GRN
  • 12. Multicellular morphogenesis and its computational modeling 6 Dept. of Computer Science & Engineering M.E.A Engineering College models also distinguish themselves as deterministic models and stochastic models according to their ability to describe stochasticity in gene expression. Note that in artificial life, a few high-level abstraction models have also been used for modeling development, such as the L-systems and grammar trees. Generally speaking, the regulatory dynamics in a unicellular cell can be described by a set of ordinary differential equations. For example, the mathematical model of gene expression with autoregulation can be described by (2.1) (2.2) where [R] and [P] are the concentration of mRNA and protein, respectively, γR and γP are the decay rate of the mRNA and protein, αR and αP are the synthesis rate of the mRNA and protein, and H(X) is the Hill function. If the autoregulation is a repression, also known as negative autoregulation, the Hill function can be described by (2.3) and if the autoregulation is activation, the Hill function can be written as (2.4) (2.4)
  • 13. Multicellular morphogenesis and its computational modeling 7 Dept. of Computer Science & Engineering M.E.A Engineering College where β is the activation coefficient, θ is the threshold, and n isthe Hill coefficient. For describing themorphogenesis of multicellular organisms, the interaction between the cells and its influence on gene expression dynamics must be taken into account. Mjolsnesset al. [66]has suggested a generalized GRN model that considered diffusion of TFs among the cells: (2.5) where gij denotes the concentration of jth gene product (protein) in the ith cell. The first term on the right-hand side of (2.5) represents the degradation of the protein at a rate of γj, the second term specifies the production of protein gij , and the last termdescribes protein diffusion at a rate of Dj . φ is an activation function for the protein production, which is usually defined as a sigmoid function φ(z)=1/(1 + exp(−µz)). The interaction between the genes is described with an interaction matrix Wjl, the element of which can be either active (a positive value) or repressive (a negative value). θj is a threshold for activation of gene expression. ng is the number of proteins. An illustration of cell–cell interactions is provided in Fig. 5, where gene 1 of cell 1 is activated by its own protein and repressed by the protein produced by gene 1 of cell 2 through diffusion. Similarly, gene2 of cell 1 is activated by its own protein and repressed by the protein of gene 2 of cell 2 through diffusion.
  • 14. Morphogenetic Swarm Robotic Systems 8 Dept. of Computer Science & Engineering M.E.A Engineering College CHAPTER 3 MORPHOGENETIC SWARM ROBOTIC SYSTEMS 3.1 Swarm Robotic Systems A swarm robotic system is a multirobot system consisting of a large number of homogeneous simple robots. Swarm robots are often used to fulfill tasks that are difficult or even impossible for a single robot, especially in the presence of uncertainties, or with incomplete information, or where a distributed control or asynchronous computation is required. Compared with centralized systems, swarm robotic systems with a distributed control are believed to be flexible, robust, and adaptive for tasks that are inherently distributed in space and/or time. Typical applications of swarm robotic systems include group transport, foraging, shape formation, boundary coverage, urban search and rescue, and unknown environment exploration. However, designing a decentralized control algorithm for swarm robotic systems has been a challenging task. 3.2 Metaphor Between Swarm Robotic Systems and Multicellular Systems Cell-Robot Mapping: The basic idea in applying genetic and cellular mechanisms in biological morphogenesis to self-organized control of swarm robots is to establish a metaphor between a cell and a robot. In other words, it is assumed that the movement dynamics of each robot can be modeled by the regulatory dynamics of a cell. the location and velocity of the robots are described by the protein
  • 15. Morphogenetic Swarm Robotic Systems 9 Dept. of Computer Science & Engineering M.E.A Engineering College concentration of a few genes whose expression is influenced by each other. Typically, for a robot in a 3-D space, three proteins are used for denoting the robot’s position and three for the velocity. Note, however, that the mathematical definition of the protein concentrations standing for position and velocity of the robots do not satisfy the exact physical relationship between position and velocity. Fig. 3.1 shows multiple robots in a field, each represented by a cell, where the robots are represented by cells containing a virtual DNA in a field. Keeping the metaphor between the cells and the robots in mind, the movement dynamics of each robot can be described by a GRN model, where the concentration of two proteins of type G represents the x and y position of a robot, respectively, and that of the proteins of type P representing the analog of the velocity. (3.1)
  • 16. Morphogenetic Swarm Robotic Systems 10 Dept. of Computer Science & Engineering M.E.A Engineering College Fig. 3.1. Swarm robotic system represented by a multicellular system. where i =1, 2, ..., n, and n is the total number of robots (cells) in the system. gi,x and gi,y are the x and y position of the ith robot, respectively, which corresponds to the concentration of two proteins of type G. pi,x and pi,y are the concentration of two proteins of type P, which denotes the velocity-like property of the ith robot along the x and y coordinates, respectively. Di,x and Di,y are the sum of the distances between the ith robot and its neighbors. In the language of the multicellular system, it is the sum of the concentration of protein type G diffused from neighboring cells. Mathematically, we have (3.2) where Ni denotes the number of neighbors of robot i, and Dj i,x and Dj i,y are the protein concentrations diffused from neighboring robot j received by robot i, which is
  • 17. Morphogenetic Swarm Robotic Systems 11 Dept. of Computer Science & Engineering M.E.A Engineering College defined as (3.3) The diffusion termin the regulatorymodel simulates the cell–cell signaling in multicellular systems. For a swarm robotic system, this entails that each robot is able to detect the distance to its neighboring robots, which is practical and easy to realize. 2) Morphogen Gradients for Target Shape Representation: In biological morphogenesis, morphogen concentration gradients control cell fate specification and play a key role in understanding pattern formation [5]. In the present gene regulatory model for shape formation of swarm robots, the target shape information is also provided in terms of morphogen gradients, which is defined by fz in (3.1). For 2-D target shape, f(zi ) can be defined as follows: (3.4) where zi,x and zi,y are the gradients along x-axis and y-axis, respectively, of an analytic function h, which is described as (3.5)
  • 18. Morphogenetic Swarm Robotic Systems 12 Dept. of Computer Science & Engineering M.E.A Engineering College where h defines the shape the robots should form. The aforementioned GRN makes it possible for the swarm robots to form shapes that can be described by an analytical function. There are potentially three problems with this way of shape representation. First, the complexity of the shapes is limited. Second, the system needs a global coordinate system for describing the shapes, which poses a big problem for decentralized systems. Third, the shape can be formed only on a predefined location. To address these issues, parametrized shape representation models, such as B´ ezier, B-Spline, and nonuni- form rational B-Spline (NURBS) can be used. The NURBS is a mathematical model commonly used in computer graphics and design optimization for generating and representing curves and surfaces. NURBS can offer two unique features formultirobot shape formation. First, it provides a common mathematical form for both standard analytical shapes and free-form shapes. Second, it is a parametrized representation that is independent of an absolute coordinate system. Once the parameter in the NURBS curve is fixed, a corresponding point on the NURBS curve can be determined without a global co- ordinate system. The basis functions used in NURBS curves are defined as Bj,q (u), where i corresponds to the jth control point, and q is the degree of the basis function. A NURBS curve can be defined as the combination of a set of piecewise rational basis functions with N + 1 control points pj and the associated weights wj as follows: (3.6) where N is the number of control points, u is the parametric variable, and Bj,q (u) are B-spline basis functions. With the NURBS model for shape description, complex
  • 19. Morphogenetic Swarm Robotic Systems 13 Dept. of Computer Science & Engineering M.E.A Engineering College shapes can be formed, refer to Section III-C for examples. Fig. 3.2. Snapshots showing the emergence of a pattern from 17 robots similar to bird flocking 3.3 Illustrative Results on GRN-Based Swarm RobotSelf- Organization In the experiments, the parameters in (6) and (7) need to be determined. A straightforward way is to define the parameters heuristically with the following condition being satisfied so that the states of the dynamic system can converge to the target shape: mk ≤ ac,a,c,k,m> 0. In addition, b should also be larger than zero so that collisions between the robots, possibly between a robot and an obstacle, can be avoided. A more desired approach to setting up these parameters is to find a set of optimal parameters, using an evolutionary algorithm by minimizing the time and/or the total travel distance for all robots to converge to the target shape, as done in [32]. IfNURBS is used for representing the target shape, parameter u in (13) should be determined for each robot. During the self-organization process, each robot will randomly pick a value from {0, 1/n, 2/n,..., 1}, where n is the number of robots in the system, which is either
  • 20. Morphogenetic Swarm Robotic Systems 14 Dept. of Computer Science & Engineering M.E.A Engineering College assumed to be known, or can be estimated as described in [31]. It is therefore possible that different robots pick the same u value in the beginning and thus will compete the same point on the target shape. In this case, robots arrive later will try another u value until it converges to a point on the target shape where no other robot exists. Simulation results where 17 robots are used to form a bird- flocking shape are given in Fig. 3.2. The robots are randomly distributed in the area in the beginning. A reference robot is chosen through a competition process, during which the robot that has the maximum number of neighbors wins. Driven by the GRN-based dynamics, the robots will then autonomously form the target shape. A proof-of-concept experiment has also been performed using E-Puck robots. Fig. 3.3 shows a few snapshots of 8 E- Puck robots converging to a capital letter “R,” refer to [61] for details. One important concern in designing decentralized algorithms for self-organizing swarm robots is their robustness in the presence of disturbances in the systemas well as in the environment. Extensive empirical results show that the morphogenetic self- organization algorithm is robust to changes in the number of robots, insensitive to noise in the model parameters sensory measurements, and adaptable to environmental changes such as moving obstacles Fig. 3.3. Snapshots of 8 E-Puck robots forming the letter ‘R
  • 21. Morphogenetic Modular Robots for self-organized Reconfiguration 15 Dept. of Computer Science & Engineering M.E.A Engineering College CHAPTER4 MORPHOGENETIC MODULAR ROBOTS FOR SELF-ORGANIZED RECONFIGURATION 4.1 Reconfigurable ModularRobots Self-reconfigurable modular robots consist of a number of modules and are able to adapt their shape (configuration) by rearranging their modules to changing environments. Each module is a physical or simulated ‘body’ containing a controller. Both physical modular robots, such as M-TRAN and Molecube, and simulated ‘animats,’ such as Karl Sims’ virtual creature and Framsticks have been constructed for reconfigurable robotic systems, refer to Fig. 4.1 Modules in M-TRAN comprise two connected cubic parts. The connection mechanism between the two cubic parts allows the modules to perform basic motions such as lifting or rotating. However, compared to single cubic mechanism, the mandatory connection between the two cubic parts may become a mechanical constraint. The modules in Molecube are composed of two half-cubes on a diagonal plane. Each half-cube can swivel with respect to the other half, which is inspired by the swiveling action. An advantage of Molecube modules is their single cubic shape that can freely be attached or detached to neighboring modules. However, the motion of the modules often requires more free space around the module so that the movement is not blocked. Although self- reconfiguration is believed to be the most important feature of self-reconfigurable robots, the ability to adapt their configuration autonomously under environmental changes remains to be demonstrated.
  • 22. Morphogenetic Modular Robots for self-organized Reconfiguration 16 Dept. of Computer Science & Engineering M.E.A Engineering College Fig.4.1 Examples of physical and simulated modular robots. 4.2. CrossCube—A Simulated Modular Robot CrossCube adopts a lattice-based cube design. Each module in CrossCube is a cubical structure having its own computing and communication resource and actuation capability. Like all modular robots, the connection part of the modules can easily be attached or detached to other modules. Each module can perceive its local environment and communicate with its neighboring modules using on-board sensors. Each CrossCube module consists of a core and a shell, as shown in Fig. 4.2(a). The core is a cube with six universal joints. Their default heading directions are bottom, up, right, left, front, and back, respectively. Each joint can attach to or detach from the joints of its neighbor modules. The axis of each joint can be actively rotated, extended, and return to its default direction. The cross-concaves on each side of the shell restrict the movement trajectory of the joints, as shown in Fig. 4.2 (a). The borders of each module can actively be locked or unlocked with the borders of other modules, as shown in Fig. 4.2(b). The length and angle of the lock mechanism can also be adjusted on the boarders of the modules.
  • 23. Morphogenetic Modular Robots for self-organized Reconfiguration 17 Dept. of Computer Science & Engineering M.E.A Engineering College Basic motions of modules in CrossCube include rotation, climbing, and parallel motion. Fig. 4.2 (c) illustrates a rotation movement of two modules. Parallel motion means that a module moves to a next position, which is parallel to its current position. During a parallel motion, a module moves from its current position to a parallel position on its right. All joints of the modules will stick out slightly to make enough free space for modules to move. Climbing motion means that a module moves to a diagonal neighboring position. Parallel motion and climbing motion allow a module of CrossCube to move to any position within the modular robot as long as the modules are connected. Fig. 4.2. Mechanical demonstration of CrossCube. (a) Joints; (b) Locks on the boundaries of the modules. (c) Rotation and extension of the joints of the modules. 4.3. IllustrativeExamplesof GRN-Based Self-Reconfiguration This section describes briefly a case study on using GRNbased controller to coordinateCrossCube modules for a locomotion task, in which the modular robot needs to traverse through different environmental fields. A software is developed to simulate the behaviors and interaction of CrossCube in a physical world using C++ and the Physx engine from nVidia. In the following experiment, the parameters of the GRN models are setup as follows: a = 0.7, b = 1, c = 1, GA L = −1, GA H = 1, GP L = 2, and Cij P = 0.7. The concentration of each protein decays to 80% of its previous level when it diffuses into a new grid. Before showing the self-reconfiguration ability of the system in a changing environment, it first performs a simple experiment to verify the effectiveness of the model. The modular robot has a “block” configuration consisting of 16 modules, which should convert into a vehicle-like pattern defined in terms ofmorphogen level. To verify the model’s ability to reconfigure the modular
  • 24. Morphogenetic Modular Robots for self-organized Reconfiguration 18 Dept. of Computer Science & Engineering M.E.A Engineering College robot to adapt to different environments, a simulation has been performed where a vehicle needs first to move through a narrow passage. Then, the robot must climb up a step to move forward. The environment is defined using the size of the modular robots as a unit: the wider passage is seven units in width, while the narrower passage is of 5.5 unit in width. The height of the step is one unit. The reconfiguration is triggered when any of the modules in the front detect obstacles. If all front modules detect obstacles, a climbing reconfiguration process will be activated. Fig. 4.3. Set of snapshots demonstrating a series of reconfigurable processes during locomotion and climbing.
  • 25. Morphogenetic Brain and Body Design for Intelligent Robots 19 Dept. of Computer Science & Engineering M.E.A Engineering College CHAPTER 5 MORPHOGENETIC BRAIN AND BODY DESIGN FOR INTELLIGENT ROBOTS 5.1 Computational Models for Neuraland Morphological Development Various computational models have been suggested for neural and morphological development. A large body of research on modeling the growth of nervous systems was based on grammatical re-writing rules such as L-systems grammar trees. Kauffmann’s Boolean network was used for modeling the structural development of dynamic neural networks. More low-level models that consider cell– cell chemical interactions through reaction and diffusion of morphogens have also been employed for modelling neurogenesis . A recurrent artificial neural network was used for modeling the development of a spiking neural network for the control of a Khepera robot.Matrix rewriting scheme was applied to modeling neural morphogenesis, which was coevolved with neural plasticity rules for controlling a mobile robot. Fig. 5.1. Influence of development on selection directions.
  • 26. Morphogenetic Brain and Body Design for Intelligent Robots 20 Dept. of Computer Science & Engineering M.E.A Engineering College A low-level GRN model with chemical diffusion was adopted for evolving neurogenesis for a hydra-like animats . The weights of the developed spiking neural networks were then further adapted using an evolution strategy for generating foodcatching behavior. Similar computational models have also been suggested for simulating morphological development. The main differences may lie in the following aspects compared to those for neural development. First, increasing attention has been paid to 3-D models , which plays an important role in modeling morphological development. Second, physical cellular interactions are also modeled in addition to the genetic regulatory mechanisms, such as adhesion repulsion between the cells. Both sphere models and spring-mass-damper models have been used. Finally, efforts have also been made to simulate both neural and morphological development, using the same developmental model, though most of them use a high-level developmental model, such as the L-system. A Boolean genetic regulatory network has been used for modeling both morphological parts such as sensor and actuator, and control part such as control-neurons at a very high abstraction level. So far, most neural networks generated by a developmental model have very limited functionalities. In addition, most developmental models of neural networks take only genetic mechanisms, i.e., activity-independent development, into account. For the neural network to function, activity-dependent development, which is responsible for synapse refinement, is essential. The main processes in neural development is summarized in Fig.5.2. From the figure, it is seen that early development of nervous systems, such as neuron axon growth, dendrite outgrowth, and synapse maturation, whichwas usually thought to be genetically
  • 27. Morphogenetic Brain and Body Design for Intelligent Robots 21 Dept. of Computer Science & Engineering M.E.A Engineering College Fig. 5.2. Main processes in neural development driven by genetic and environmental control situated in a physical environment together with the development of the body plan. Fig. 5.3. Example of chromosome for neural development 5.2. GRN Model for Neural and Morphological Development The growth of the animat morphology is under the control of GRNs and cellular physical interactions. Extended from the cellular growth model for structural design, GRN models for the development of a nervous system [41] and body plan
  • 28. Morphogenetic Brain and Body Design for Intelligent Robots 22 Dept. of Computer Science & Engineering M.E.A Engineering College [82] of primitive animals have been proposed. In the genome of the GRN models, each gene consists of a number of structural units (SUs) proceeded by a number of regulatory units (RUs). RUs can be activating (RU+) or repressive (RU−). When SUs are activated, they will produce proteins either responsible for cellular behaviors such as cell division, cell death, cell migration, and axon growth, or proteins regulating the activation of the structural units, which are also known as TFs. If a TF can only regulate the genes inside the cell, it is then called an internal TF. If a TF can also diffuse out of the cell and regulate the genes of other cells, it is termed as an external TF. A TF can be both intracellular and intercellular. An example of a chromosome in the cellular model for neural development is provided in Fig. 17. From the figure, it is noted that single or multiple RUs may regulate the expression of a single or multiple SUs. Whether a TF can influence an RU is dependent on the degree of match between the affinity value of a TF and that of an RU. If the difference between the affinity values of a TF and a RU is smaller than a predefined threshold _, the TF can bind to the RU to regulate. The affinity match (γi,j ) between the ith TF and jth RU is defined by (5.1) If γi,j is greater than zero and the concentration ci of the ith TF is above a threshold (ϑj ) defined in the jth RU, then the ith TF influences the jth RU. Thus, the activation level contributed by this RU (denoted by aj , j = 1, . . . , N) amounts to aj =_M i=1|ci ,−ϑj |, where M is the number of existing TFs. The expression level of the kth gene, that is regulated by N RUs, can be defined by (5.2)
  • 29. Morphogenetic Brain and Body Design for Intelligent Robots 23 Dept. of Computer Science & Engineering M.E.A Engineering College where sj ∈ (0, 1) denotes the sign (positive for activating and negative for repressive) of the jth RU and hj is a parameter, representing the strength of the jth RU. If αk > 0, then the kth gene is activated and its corresponding behaviors encoded in the SUs are performed. A SU that produces a TF encodes all parameters related to the TF, such as the affinity value, a decay rate Dc i , a diffusion rate Df i , as well as the amount of the TF to be produced: (5.3) where f and β are both encoded in the SUTF. A TF produced by a SU can be partly internal and partly external. To determine how much of a produced TF is external, a percentage (pex ∈ (0, 1)) is also encoded in the corresponding gene. Thus, pexA is the amount of external TF and (1 − pex )A is that of the internal TF. To make it easier for simulating the diffusion of TFs, cells are put in an environment that is divided into a number of grids. External TFs are put on four grid points around the center of the cell, which undergoes first a diffusion (23) and then decay process (Eqn. 24): (5.4) where ui is a vector of the concentrations of the ith TF at all grid points and thematrixGdefines,which grid points are adjoining. The SUs encode cellular behaviors and the related parameters. The SUfor cell division encodes the angle of division, indicating where the daughter cell is placed. A cell with an activated SU for cell death will die at the end of the developmental time step. The aforementioned cellular model has been applied to simulate both morphological and neural development [41], [82]. In the experiment to generate an animat like C. elegans, two
  • 30. Morphogenetic Brain and Body Design for Intelligent Robots 24 Dept. of Computer Science & Engineering M.E.A Engineering College prediffused, external TFs without decay and diffusion are deployed in the computation area (maternal morphogen gradients). The first TF has a constant gradient in the x-direction and the second in the y-direction. In the experiments, the GRN model is initialized randomly, and the target of the evolution is to evolve an elongated animate, whose morphology is defined by a rectangular shape. Without any hard constraints for stopping cell division, we are able to evolve a GRN that results in self-stabilized cellular growth [82]. A few snapshots of the self-stabilized cellular growth process is provided in Fig. 5.4, where the cellular system starts from two cells sitting in the middle of the simulation area and reaches a dynamic stability at the end of the development. In the figure, cells in light color are going to divide in the next developmental step, and those in dark are going to die in the next step. In another experiment, it uses a similar GRN model for simulating neural growth in hydra. In the beginning, a few simulated stem cells are randomly distributed in the body plan of the hydra-like animat. Then, cells divide, migrate, and axons grow so that the neurons are connected [41]. Snapshots showing this growth process are given in Fig. 19. The evolved GRN resulting in the neural development in Fig. 19 is presented in Fig. 20, which is able to generate the correct temporal activation sequence for cell division, cell migration, and axon growth. Fig. 5.4. Self-stabilized cellular growth under the control of a GRN model presented in [82]. (a) System is initialized with two cells. (b)–(c) System grows as cells divide. (d) Growth is self-stabilized dynamically, where the cells on light grey color will divide and those in dark will die. A balance of cell division and cell death is accomplished under the control of the GRN.
  • 31. Morphogenetic Brain and Body Design for Intelligent Robots 25 Dept. of Computer Science & Engineering M.E.A Engineering College 5.3. Conceptual Framework for Coevolving the Development of Robot Hand Morphology and Controller It has been found that the morphology of the animal hands has changed a lot to adapt to the needs of the animals during evolution. it can be seen that they distinguish themselves in both shape and length in the finger segments. Besides, it has been hypothesized that particular behaviors, such as throwing and clubbing, have played a key role in differences between a hand of human beings and that of a chimpanzee. The importance of coevolving the development of hand morphology and control in robotics is twofold. On the one hand, object grasping and manipulation with a robot hand is in itself a challenging task in that such systems are usually highly redundant. Existing work focuses on the design of the hand controller for a given morphology [97], which is inefficient when the shape of the objects changes considerably. A better approach is to codesign the hand morphology and control in a developmental manner, as illustrated in Fig. 5.7. In this way, the shape and number of finger segments, the number fingers, and even the number of arms can be evolved together with their controller.
  • 32. Morphogenetic Brain and Body Design for Intelligent Robots 26 Dept. of Computer Science & Engineering M.E.A Engineering College Fig. 5.5. Development of a nervous system using the GRNmodel . (a) A few stem cells are randomly distributed on a hydra-like body wall. (b)–(d) Cells divide, migrate, and axons grow. As the development goes on more connections are built up. Fig. 5.6. Evolved GRN resulting in the neural development in Fig. 19. SU: structural genetic units; RU: regulatory genetic units.
  • 33. Morphogenetic Brain and Body Design for Intelligent Robots 27 Dept. of Computer Science & Engineering M.E.A Engineering College Meanwhile, coevolution of the hand morphology and control in a computational environment provides us a means for understanding the phylogenetic changes in evolution of animal hands. So far, brain–body coevolution in computational environments has led to findings regarding the organizational principles of nervous systems and the emergence of bilateral symmetry in neural configuration. It is expected that different hand morphologies will emerge by evolving the system for different behaviors. Fig.5.7. Conceptual diagram for coevolving the development of hand/arm and control. Adapted from [37].
  • 34. Conclusion 28 Dept. of Computer Science & Engineering M.E.A Engineering College CONCLUSION This report suggests a new field of robotics termed morphogenetic robotics, which focuses on employing genetic and cellular mechanisms in biological morphogenesis for developing self-organizing, self-reconfigurable, and self-adaptive robotic systems, covering a wide range of robotic systems, such as swarm robotic systems, modular robots, and intelligent robots. Morphogenetic robotics, as epigenetic robotics, is a part of developmental robotics. While epigenetic robotics concentrates on the mental development of robotic systems, morphogenetic robotics focuses on the physical development of the body plan and nervous system of the robots. Therefore, it is believed that developmental robotics should include both morphogenetic robotics and epigenetic robotics. Research on morphogenetic robotics is still in its infancy and thereforemany issues remain to be explored. First, many genetic and cellular mechanisms underlying biological morphogenesis still remain elusive, and much work needs to be done on reconstruction of spatiotemporal gene expression patterns using computational models based on biological data. In particular, the self-adaptation capability of the genetic and cellular models to environmental changes used in morphogenetic robotics needs to be improved. The introduction of hierarchical gene regulatory models suggests a promising step toward this goal, but many details on autonomous self-adaptation based on sensory input are still unclear. Second, the interactions between morphogenetic robotics and epigenetic robotics are largely unexplored. Obviously, the physical and mental development are closely coupled, since neural and morphological development lay the neurophysiological foundation for cognitive and mental development, and both are constrained by the environment in which the robots reside. Furthermore, research on developmental robotics should also be performed taking evolution into account, as development can not only bias the direction of evolution, but also enhance evolvability . Finally, morphogenetic robotics is currently very much limited to computational simulations. Appropriate hardware for morphogenetic robotics, including programmable materials, and adaptable sensors and actuators, is to be studied.
  • 35. References 29 Dept. of Computer Science & Engineering M.E.A Engineering College REFERENCES [1] U. Alon, “Network motifs: Theory and experimental approaches,” Nature Rev. Genetics, vol. 8, pp. 450–461, 2007. [2] F.Dellaert and R. D. Beer, “Toward an evolvablemodel of development for autonomous agent synthesis,” in Proc. Artif. Life IV, 1994, pp.246–257. [3] J. Amend, Jr. and H. Lipson, “Shape-shifting materials for programmable structures,” presented at the 11th Int. Conf. Ubiquitous Computing:Workshop on Architectural Robotics (Ubicomp Archibots 2009), Orlando, FL. [4] W. Arthur, “The effect of development on the direction of selection: Toward twenty-first century consensus,” Evol. Dev., vol. 10, pp. 375–390, 2004. [5] H. Ashe and J. Briscoe, “The interpretation of morphogen gradients,” Development, vol. 133, pp. 385–394, 2007. [6] L. Beloussov, “Integrating self-organization theory into an advanced course on morphogenesis at Moscow State University,” Int. J. Dev. Biol., vol. 47, pp. 177–181, 2003. [7] H. Ben-Amor, S. Cadau, A. Elena, D. Dhouailly, and J. Demongeot, “Regulatory networks analysis: Robustness in morphogenesis,” in Proc. 2009 Int. Conf. Adv. Inf. Netw. Appl. Workshops, Bradford, U.K., 2009, pp. 924–928. [8] A. Bhattacharyya, “Morphogenesis as an amorphous computing,” in Proc. 3rd Conf. on Comput. Frontiers, 2006, pp. 53–64. [9] C. Bishop, D. Erezyilmaz, T. Flatt, C. Georgiou, M. Hadfield, A. Heyland, J. Hodin, M. Jacobs, S. Maslakova, A. Pires, A. Reitzel, S. Santagata, K. Tanaka, and J. Youson, “What is metamophosis?,” Integr. Comp. Biol., vol. 46, pp. 655–661, 2006. [10] E. Boers, “Biological metaphors and design of modular artificial neural networks,” Master’s thesis, Dept. Comput. Sci. Exp. Psychol., Leiden Univ., The Netherlands, 1992.

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