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Symbiotic Multi-Robot Organisms Reliability, Adaptability, Evolution

:	Paul Levi, Serge Kernbach
:	Paul Levi, Serge Kernbach
:	Symbiotic Multi-Robot Organisms Reliability, Adaptability, Evolution
:	Springer-Verlag
:	9783642116926
:	1
:	CHF 135.40
:

:	Allgemeines, Lexika
:	English

:	470
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

This book examines the evolution of self-organised multicellular structures, and the remarkable transition from unicellular to multicellular life. It shows the way forward in developing new robotic entities that are versatile, cooperative and self-configuring.

"Chapter 3 Cognitive Approach in Arti?cial Organisms (p. 165-166)

3.1 CognitiveWorld Modeling

Libor P?reu?cil, Petr ? St?ep´an, Tom´a?s Krajn´ik, Karel Ko?snar, Anne van Rossum, Alfons Salden The chapter introduces possibilities and principal approaches to knowledge gathering, preprocessing and keeping in autonomous mobile robots’ arti?cial organisms. These may comprise“classical AI” concepts as well as“new AI principles”, whereas both approaches themselves may bring up either major advantages, or suffer from certain drawbacks.

The classical approach relying on sensor-fusion-model-planning and actuation schema takes the advantage of explicit representation of the organism knowledge which may be represented by varied types of world model structure (Barrera, 2005). Subsequently, these structures are mainly understood as geometric and other environmental features carriers. Features and data are considered for explicit representation of world properties and typically have precisely known location, meaning and con?dence.

These properties serve for inputs to cognitive or planning subsystems allowing to execute reasoning processes over this data. Major advantages of this stand in predictable behaviors, strong data reduction ratio, and therefore better possibility of tracking and safety of the robot operation. The disadvantage of this class of methods remains in a stiff way of combining speci?c cognitive methods, typically capable of adjustment to slowly changing organism operating conditions.

Therefore, the process of adaptation/evolution to rapidly changing environment condition becomes a hard problem in this approach. The other approach aiming to store knowledge in an implicit form tends to be more ?exible in adaptation/learning and evolution aspects and ranges from Brooks’ principles to Neural Net knowledge representations. Hence, this advantage is balanced by unknown or fuzzy localization and form of particular knowledge.

Estimation of particular behaviors and possibility of their determination remains low. Moreover, due to undetermined meanings of particular knowledge/data components, ef?cient ?ltration of data amounts becomes ineffective. The leading target in here stands in elaboration of novel approaches to representation of world knowledge based of combination of selected features of the above mentioned methods. A hybrid approach, that combines strong data amount reductions and easy understanding of a World Map content with high ?exibility of the“new AI” principles is proposed."

	Title Page	2
	Foreword	5
	Acknowledgements	8
	Contents	9
	List of Contributors	15
	Acronyms	21
	Introduction	23
	Concepts of Symbiotic Robot Organisms	27
	From Robot Swarm to Artificial Organisms: Self-organization of Structures, Adaptivity and Self-development	27
	Mono- and Multi- functional Artificial Self-organization	29
	Collective Robotics: Problem of Structures	33
	Adaptability and Self-development	36
	Artificial Symbiotic Systems: Perspectives and Challenges	43
	Towards a Synergetic Quantum Field Theory for Evolutionary, Symbiotic Multi-Robotics	47
	Cooperative (Coherent) Operations between Fermionic Units	50
	Individual Contributions of the Eigenanteile	58
	Separate Perturbations of the Eigenanteile	62
	Coupling of the Disturbed Eigenanteil Equations	64
	Information Model and Interactions of Structured Components	67
	Functional and Reliability Modelling of Swarm Robotic Systems	76
	Macroscopic Probabilistic Modelling in Swarm Robotics	76
	Reliability Modelling of Swarm Robotic Systems	87
	Concluding Discussion	98
	Heterogeneous Multi-Robot Systems	100
	Reconfigurable Heterogeneous Mechanical Modules	100
	A Heterogeneous Approach in Modular Robotics	101
	Integration and Miniaturization	103
	Locomotion Mechanisms	105
	Docking Mechanisms and Strategies	107
	Mechanical Degrees of Freedoms: Actuation for the Individual Robot and for the Organism	109
	Tool Module: Active Wheel	109
	Summary of the Three Robotic Platforms	112
	Computation, Distributed Sensing and Communication	113
	Electronic Architectures in Related Works	114
	General Hardware Architecture in SYMBRION/REPLICATOR	115
	General Sensor Capabilities	118
	Vision and IR-Based Perception	121
	Triangulation Laser Range Sensor for Obstacle Detection and Interpretation of Basic Geometric Features	126
	Powerful Wireless Communication and 3D Real Time Localisation Systems	128
	Integration Issues	134
	Energy Autonomy and Energy Harvesting in Reconfigurable Swarm Robotics	135
	Energy Autonomy	136
	Energy Harvesting	137
	Energy Trophallaxis	140
	Energy Sharing within a Robot Organism	142
	Energy Management	143
	Modular Robot Simulation	154
	Simulation Environments	155
	The Symbricator3D Simulation Environment	158
	Showcase: The Dynamics Predictor	170
	Conclusion and Future Work	183
	Cognitive Approach in Artificial Organisms	185
	Cognitive World Modeling	185
	Methodology	186
	Spatial World Modeling	186
	Evolution Map	187
	Map	189
	Jockeys	190
	Reasoning	192
	Executor	193
	Porting the EMa onto a Robot	194
	EMa Care-Taking Procedures	195
	Physical Layout	196
	Logical Layout and Communication	197
	Experiments	199
	Functional World Modelling	200
	Emergent Cognitive Sensor Fusion	203
	Scenarios	205
	Towards Embodied and Emergent Cognition	208
	Sensor Fusion Model	212
	Application of Embodied Cognition to the Development of Artificial Organisms	222
	Natural vs. Artificial Systems: Collectivity and Adaptability in Inanimated Nature	223
	Definition of Information and Knowledge Related to Restrictions	231
	Collectivity and Adaptability in Animated Nature	239
	Information Based Learning to Develop and Maintain Artificial Organisms	241
	Adaptive Control Mechanisms	249
	General Controller Framework	249
	Controller Framework in SYMBRION/REPLICATOR	249
	Bio-inspiration for the Structure of Artificial Genome	252
	Action Selection Mechanism	254
	Overview of Different Control Mechanisms	255
	Hormone-Based Control for Multi-modular Robotics	260
	Micro-organisms Cell Signals and Hormones as Source of Inspiration	261
	Related Work	266
	Artificial Homeostatic Hormone System (AHHS)	267
	Encoding an AHHS into a Genome	269
	Self-organised Compartmentalisation	270
	Evolutionary Adaptation	275
	Single Robots	276
	Forming Robot Organisms	277
	Locomotion of Robot Organisms	279
	Feedbacks	281
	Conclusion	282
	Evolving Artificial Neural Networks and Artificial Embryology	283
	Shaping of ANN in Literature	284
	Overview over Section	286
	Concept of Adapting Virtual Embryogenesis for Controller Development	286
	Diffusion Processes	287
	Genetics and Cellular Behaviour	288
	Simulated Physics	289
	Cell Specialisation	290
	Linkage	290
	Depicting Genetic Structures and Feedbacks	292
	Stable Growth due to Feedbacks in Genetic Structure	295
	Developing Complex Shapes	296
	The Growth of Neurons	297
	Translation	298
	Usability of Virtual Embryogenesis in the Context of Artificial Evolution for Shaping Artificial Neural Networks and Robot Controllers	299
	Subsumption of Section	301