Robots and Lattice Automata

319 Pages·2015·20.467 MB·English

by  Georgios Ch. Sirakoulis, Andrew Adamatzky (eds.)

Emergence, Complexity and Computation ECC
Georgios Ch. Sirakoulis
Andrew Adamatzky E ditors
Robots
and Lattice
Automata
Emergence, Complexity and Computation
Volume 13
Series editors
Ivan Zelinka, Technical University of Ostrava, Ostrava, Czech Republic
e-mail: [email protected]
Andrew Adamatzky, University of the West of England, Bristol, UK
e-mail: [email protected]
Guanrong Chen, City University of Hong Kong, Hong Kong
e-mail: [email protected]
Editorial Board
Ajith Abraham, MirLabs, USA
Ana Lucia C. Bazzan, Universidade Federal do Rio Grande do Sul, Porto Alegre
RS, Brazil
Juan C. Burguillo, University of Vigo, Spain
Sergej Čelikovský, Academy of Sciences of the Czech Republic, Czech Republic
Mohammed Chadli, University of Jules Verne, France
Emilio Corchado, University of Salamanca, Spain
Donald Davendra, Technical University of Ostrava, Czech Republic
Andrew Ilachinski, Center for Naval Analyses, USA
Jouni Lampinen, University of Vaasa, Finland
Martin Middendorf, University of Leipzig, Germany
Edward Ott, University of Maryland, USA
Linqiang Pan, Huazhong University of Science and Technology, Wuhan, China
Gheorghe Păun, Romanian Academy, Bucharest, Romania
Hendrik Richter, HTWK Leipzig University of Applied Sciences, Germany
Juan A. Rodriguez-Aguilar, IIIA-CSIC, Spain
OttoRössler,InstituteofPhysicalandTheoreticalChemistry,Tübingen,Germany
Vaclav Snasel, Technical University of Ostrava, Czech Republic
Ivo Vondrák, Technical University of Ostrava, Czech Republic
Hector Zenil, Karolinska Institute, Sweden
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Georgios Ch. Sirakoulis Andrew Adamatzky
(cid:129)
Editors
Robots and Lattice
Automata
123
Editors
Georgios Ch.Sirakoulis Andrew Adamatzky
Department of Electricaland Computer Unconventional ComputingCentre
Engineering Universityof theWest ofEngland
Democritus University ofThrace Bristol
Xanthi UK
Greece
ISSN 2194-7287 ISSN 2194-7295 (electronic)
ISBN 978-3-319-10923-7 ISBN 978-3-319-10924-4 (eBook)
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Preface
Robots and Automata are notionally related. In this context, Automata (originated
fromthelatinizationoftheGreekword“αυτόµατoν”)asself-operatingautonomous
machines,inventedfromancientyearscanbeeasilyconsideredasthefirststepsof
these robotic-like efforts. On other words, an Automaton is a self-operating
machine, while a robot is a hardware agent with role(s) to operate usually without
animmediatehumanoperator.Automataareusefultoolsforformaldescriptionsof
robots. Automata themselves are formally represented by final state machines: the
abstract machines which take finite number of states and change their state while
triggered by certain conditions. Authors of the book bring together concepts,
architectures and implementations of Lattice Automata and Robots. Lattice Auto-
mataareminimaluniversalinstantiationofspaceandtime.ALatticeAutomatonis
either a regular array of finite state machines or collectives of mobile finite state
machines inhabiting a discrete space. In both cases the finite states machines, or
Automata, update their states by the same rules depending on states of their
immediate neighbours. Automata and Robots often share the same notional
meaning: Automata are mathematical models of robots and also they are integral
parts of robotic control systems.
ThebookopenswithinspiringtextbyRosenberg—Chap.1—oncomputational
potential of groups of identical finite-state machines. The chapter lays somewhat
foundational theoretical background for the rest of the book.
Modular robots are kinematic machines of many units capable for changing its
topology by dynamically updating connections between the units. To develop
efficient algorithms of reconfiguration, we represent the robotic units by configu-
rations ofLatticeAutomataandstudyAutomatontransitionrulescorrespondingto
reconfiguration. The topic is studied in full details in three chapters: Chap. 2 by
Stoy introduces the reader to the theoretical and general aspects of modular
reconfigurable robots in Lattice Automata; Chap. 3 by Eckenstein and Yim
reproduces all the up-to-date related works and corresponding modular reconfigu-
rable robotic systems;whileinChap.4,Tomitaandco-authorsprovidefulldetails
forsomeofthesemodular systems,namelyFractumandM-Traninevery possible
aspect and discuss the general problems of Lattice-based robotic systems.
v
vi Preface
Motion control and path planning are amongst key problems of robotics, they
put high demands on detailed knowledge of environment and consume substantial
computational resources. Five chapters explicitly deal with these problems. Thus,
Arena and co-authors, in Chap. 5, use Automaton networks to control locomotion
of the fly-inspired robot. Efficient ways of routing, an abstract version of path
planning, are designed and analysed by Hoffman and Désérable in Chap. 6. Mar-
cheseproposestouseparticularfamiliesofCellularAutomatatoprovideanoptimal
representation of space and maps in precise parallel motion planning, in Chap. 7.
Charalampous and co-authors in Chap. 8 adapt classical designs of Cellular
Automaton based shortest path finders to undertake autonomous collision-free
navigation. Moreover, Ioannidis and co-authors proposed the employment of Cel-
lular Automata advanced with Ant Colony Optimization techniques resulting to
CellularRoboticAntssynergycoordinationfortacklingthepathplanningproblem
for robotic teams in Chap. 9.
Further applications of Lattice Automata in Robotics are presented in the fol-
lowingchapters.AnovelmethodofmaprepresentationisproposedinChap.10by
Kapoutsis and his co-authors. There, a configuration of elevation heights is con-
verted to cells’ states; thus, an entire map is represented by a Cellular Automaton
configuration. Cellular Automata have been a classical tool in image processing
communitysincemid-1970s,yet,thereisstillvastlandsofunexploredfeaturesand
algorithms. In his Chap. 11, Nalpantidis demonstrates practical, real-life imple-
mentation of Cellular Automaton algorithms onboard of a mobile robot.
The last two chapters deal with cooperative actions in large-scale robotic
collectives.Inbothchapters,robotsareoscillatingmechanisms arrangedonatwo-
dimensional array: their aim is to adjust their oscillations or states to produce a
specifiedvibrationpattern.Silvaandco-authors,inChap.12providemodellingand
analysis of the space–time behaviour of such collectives and transitions between
different modes of behaviour. Application of the vibrating automaton array to
physicalmanipulatorofobjectsinreallifeisstudiedbyGeorgilasandco-authorsin
Chap. 13. They show how Automaton model of an sub-excitable medium can be
used to purposefully transport objects.
All chapters are written in an accessible manner and lavishly illustrated. The
book will help computer and robotic scientists and engineers to understand
mechanismsofdecentralisedfunctioningofroboticcollectivesandtodesignfuture
and emergent reconfigurable, parallel and distributed robotic systems.
Georgios Ch. Sirakoulis
Andrew Adamatzky
Contents
1 Algorithmic Insights into Finite-State Robots. . . . . . . . . . . . . . . . 1
Arnold L. Rosenberg
1.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Technical Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 FSM “Robots” and Their Domains . . . . . . . . . . . . . . 4
1.2.2 Algorithmic Standards and Simplifications. . . . . . . . . 8
1.3 Mn’s “Walls” as an Algorithmic Tool. . . . . . . . . . . . . . . . . . 9
1.3.1 Algorithms Based on “Bouncing Off” Mn’s
“Walls”. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.2 Algorithms Based on “Hugging” Mn’s “Walls”. . . . . 15
1.4 The Power of Cooperation. . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.4.1 The Need for Cooperation . . . . . . . . . . . . . . . . . . . . 21
1.4.2 Tasks Enabled by Cooperative Behavior . . . . . . . . . . 23
1.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.5.1 Retrospective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.5.2 Prospective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2 Lattice Automata for Control of Self-Reconfigurable Robots. . . . . 33
Kasper Stoy
2.1 Self-Reconfigurable Robots . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.1.1 Origin, Features, and Applications . . . . . . . . . . . . . . 35
2.1.2 Mechatronic Implementation . . . . . . . . . . . . . . . . . . 36
2.2 Assumptions of Lattice Automata. . . . . . . . . . . . . . . . . . . . . 37
2.3 Lattice Automata-Based Control. . . . . . . . . . . . . . . . . . . . . . 40
2.4 Hybrid Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
vii
viii Contents
3 Modular Reconfigurable Robotic Systems: Lattice Automata . . . . 47
Nick Eckenstein and Mark Yim
3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.1.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.2 Key Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.3 Environments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2 Challenges and Practical Issues for MRR. . . . . . . . . . . . . . . . 50
3.2.1 General Limitations. . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2.2 Key Metrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2.3 Modular Robot Morphology—Shape
and Connectedness . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3 Example Lattice System Hardware . . . . . . . . . . . . . . . . . . . . 52
3.3.1 Key Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.2 Lattice Locomotion. . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3.3 Connection Types. . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.4 Software Systems for MRR . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4.1 Reconfiguration Planning. . . . . . . . . . . . . . . . . . . . . 68
3.5 Assembly Robotics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.5.1 Self-Assembly and Self-Repair. . . . . . . . . . . . . . . . . 69
3.6 Conclusions and the Future of MRR. . . . . . . . . . . . . . . . . . . 72
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4 Lattice-Based Modular Self-Reconfigurable Systems. . . . . . . . . . . 77
Kohji Tomita, Haruhisa Kurokawa, Eiichi Yoshida,
Akiya Kamimura, Satoshi Murata and Shigeru Kokaji
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 Fractum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.2.1 Basic Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.2.2 Algorithm I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2.3 Algorithm II. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2.4 Meta Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3 3D Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.3.1 Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.3.2 Reconfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.4 M-TRAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.4.1 Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.4.2 Reconfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.4.3 Robotic Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.5 General Problems of Lattice-Based Mechanical Systems . . . . . 90
4.5.1 Improvement in M-TRAN Hardware. . . . . . . . . . . . . 91
4.5.2 General, Physical Problem in Modular
Reconfigurable Systems. . . . . . . . . . . . . . . . . . . . . . 91
4.5.3 Achievement by M-TRAN. . . . . . . . . . . . . . . . . . . . 94
4.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Contents ix
5 Speed Control on a Hexapodal Robot Driven by a CNN-CPG
Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
E. Arena, P. Arena and L. Patané
5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.2 The Neural Network for Locomotion Control. . . . . . . . . . . . . 99
5.2.1 Leg Motor Neuron Network. . . . . . . . . . . . . . . . . . . 102
5.3 Reward-Based Learning for Speed Control. . . . . . . . . . . . . . . 103
5.4 Dynamic Simulator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6 Routing by Cellular Automata Agents in the Triangular
Lattice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Rolf Hoffmann and Dominique Désérable
6.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.1.1 Cellular Automata Agents . . . . . . . . . . . . . . . . . . . . 118
6.1.2 CA and CA–w Models . . . . . . . . . . . . . . . . . . . . . . 119
6.1.3 Lattice Topology . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.1.4 The Problem: Routing. . . . . . . . . . . . . . . . . . . . . . . 123
6.2 Minimal Routing in the Triangular Grid. . . . . . . . . . . . . . . . . 125
6.2.1 Topology of S and T. . . . . . . . . . . . . . . . . . . . . . . . 125
6.2.2 Minimal Routing Schemes in S and T. . . . . . . . . . . . 126
6.2.3 Computing the Minimal Route in T
(XYZ–Protocol). . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
6.2.4 Deterministic Routing . . . . . . . . . . . . . . . . . . . . . . . 129
6.2.5 Adaptive Routing . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6.3 Modeling the Multi-Agent System . . . . . . . . . . . . . . . . . . . . 130
6.3.1 Dynamics of the Multi-Agent System . . . . . . . . . . . . 130
6.3.2 The CA–w and CA Copy–Delete Rules. . . . . . . . . . . 135
6.3.3 Programming Issues . . . . . . . . . . . . . . . . . . . . . . . . 137
6.4 Router Efficiency and Deadlocks . . . . . . . . . . . . . . . . . . . . . 138
6.4.1 Efficiency of Deterministic Routing . . . . . . . . . . . . . 139
6.4.2 Efficiency of Adaptive Routing . . . . . . . . . . . . . . . . 141
6.4.3 Deadlocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.5 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
7 Multi-Resolution Hierarchical Motion Planner
for Multi-Robot Systems on Spatiotemporal
Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Fabio M. Marchese
7.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
7.2 MRS Motion Planning Problem . . . . . . . . . . . . . . . . . . . . . . 150