What is included with this book?
Introduction | p. 1 |
Concepts of Symbiotic Robot Organisms | p. 5 |
From Robot Swarm to Artificial Organisms: Self-organization of Structures, Adaptivity and Self-development | p. 5 |
Mono- and Multi- functional Artificial Self-organization | p. 7 |
Collective Robotics: Problem of Structures | p. 11 |
Adaptability and Self-development | p. 14 |
Artificial Symbiotic Systems: Perspectives and Challenges | p. 21 |
Towards a Synergetic Quantum Field Theory for Evolutionary, Symbiotic Multi-Robotics | p. 25 |
Cooperative (Coherent) Operations between Fermionic Units | p. 28 |
Individual Contributions of the Eigenanteile | p. 36 |
Separate Perturbations of the Eigenanteile | p. 40 |
Coupling of the Disturbed Eigenanteil Equations | p. 42 |
Information Model and Interactions of Structured Components | p. 45 |
Functional and Reliability Modelling of Swarm Robotic Systems | p. 54 |
Macroscopic Probabilistic Modelling in Swarm Robotics | p. 54 |
Reliability Modelling of Swarm Robotic Systems | p. 65 |
Concluding Discussion | p. 76 |
Heterogeneous Multi-Robot Systems | p. 79 |
Reconfigurable Heterogeneous Mechanical Modules | p. 79 |
A Heterogeneous Approach in Modular Robotics | p. 80 |
Integration and Miniaturization | p. 82 |
Locomotion Mechanisms | p. 84 |
Docking Mechanisms and Strategies | p. 86 |
Mechanical Degrees of Freedoms: Actuation for the Individual Robot and for the Organism | p. 88 |
Tool Module: Active Wheel | p. 88 |
Summary of the Three Robotic Platforms | p. 91 |
Computation, Distributed Sensing and Communication | p. 92 |
Electronic Architectures in Related Works | p. 93 |
General Hardware Architecture in Symbrion/Replicator | p. 94 |
General Sensor Capabilities | p. 97 |
Vision and IR-Based Perception | p. 100 |
Triangulation Laser Range Sensor for Obstacle Detection and Interpretation of Basic Geometric Features | p. 105 |
Powerful Wireless Communication and 3D Real Time Localisation Systems | p. 107 |
Integration Issues | p. 113 |
Energy Autonomy and Energy Harvesting in Reconfigurable Swarm Robotics | p. 114 |
Energy Autonomy | p. 115 |
Energy Harvesting | p. 116 |
Energy Trophallaxis | p. 119 |
Energy Sharing within a Robot Organism | p. 121 |
Energy Management | p. 122 |
Modular Robot Simulation | p. 133 |
Simulation Environments | p. 134 |
The Symbricator3D Simulation Environment | p. 137 |
Showcase: The Dynamics Predictor | p. 149 |
Conclusion and Future Work | p. 162 |
Cognitive Approach in Artificial Organisms | p. 165 |
Cognitive World Modeling | p. 165 |
Methodology | p. 166 |
Spatial World Modeling | p. 166 |
Evolution Map | p. 167 |
Map | p. 169 |
Jockeys | p. 170 |
Reasoning | p. 172 |
Executor | p. 173 |
Porting the EMa onto a Robot | p. 174 |
EMa Care-Taking Procedures | p. 175 |
Physical Layout | p. 176 |
Logical Layout and Communication | p. 177 |
Experiments | p. 179 |
Functional World Modelling | p. 180 |
Emergent Cognitive Sensor Fusion | p. 183 |
Scenarios | p. 185 |
Towards Embodied and Emergent Cognition | p. 188 |
Sensor Fusion Model | p. 192 |
Application of Embodied Cognition to the Development of Artificial Organisms | p. 202 |
Natural vs. Artificial Systems: Collectivity and Adaptability in Inanimated Nature | p. 203 |
Definition of Information and Knowledge Related to Restrictions | p. 211 |
Collectivity and Adaptability in Animated Nature | p. 219 |
Information Based Learning to Develop and Maintain Artificial Organisms | p. 221 |
Adaptive Control Mechanisms | p. 229 |
General Controller Framework | p. 229 |
Controller Framework in Symbrion/Replicator | p. 229 |
Bio-inspiration for the Structure of Artificial Genome | p. 232 |
Action Selection Mechanism | p. 234 |
Overview of Different Control Mechanisms | p. 235 |
Hormone-Based Control for Multi-modular Robotics | p. 240 |
Micro-organisms' Cell Signals and Hormones as Source of Inspiration | p. 241 |
Related Work | p. 246 |
Artificial Homeostatic Hormone System (AHHS) | p. 247 |
Encoding an AHHS into a Genome | p. 249 |
Self-organised Compartmentalisation | p. 250 |
Evolutionary Adaptation | p. 255 |
Single Robots | p. 256 |
Forming Robot Organisms | p. 257 |
Locomotion of Robot Organisms | p. 259 |
Feedbacks | p. 261 |
Conclusion | p. 262 |
Evolving Artificial Neural Networks and Artificial Embryology | p. 263 |
Shaping of ANN in Literature | p. 264 |
Overview over Section | p. 266 |
Concept of Adapting Virtual Embryogenesis for Controller Development | p. 266 |
Diffusion Processes | p. 267 |
Genetics and Cellular Behaviour | p. 268 |
Simulated Physics | p. 269 |
Cell Specialisation | p. 270 |
Linkage | p. 270 |
Depicting Genetic Structures and Feedbacks | p. 272 |
Stable Growth due to Feedbacks in Genetic Structure | p. 275 |
Developing Complex Shapes | p. 276 |
The Growth of Neurons | p. 277 |
Translation | p. 278 |
Usability of Virtual Embryogenesis in the Context of Artificial Evolution for Shaping Artificial Neural Networks and Robot Controllers | p. 279 |
Subsumption of Section | p. 281 |
An Artificial Immune System for Robot Organisms | p. 282 |
A Biological and Engineering Perspective | p. 283 |
An Immune-inspired Architecture for Fault Tolerance in Swarm and Collective Robotic Systems | p. 290 |
Innate Layer | p. 293 |
Adaptive Layer | p. 294 |
Summary | p. 305 |
Structural Self-organized Control | p. 306 |
Representation of Structures | p. 308 |
Compact Representation: The Topology Generator | p. 313 |
Scalability of Structures and Appearing Constraints | p. 314 |
Morphogenesis as an Optimal Decision Problem | p. 317 |
Self-organized Morphogenesis | p. 322 |
Collective Memory and Further Points | p. 325 |
Kinematics and Dynamics for Robot Organisms | p. 326 |
Modeling of Multi-robot Organisms | p. 328 |
Inverse Kinematics | p. 332 |
Dynamics | p. 333 |
Computational Analysis | p. 335 |
Conclusion | p. 336 |
Learning, Artificial Evolution and Cultural Aspects of Symbiotic Robotics | p. 337 |
Machine Learning for Autonomous Robotics | p. 337 |
Related Work | p. 338 |
Challenges for ML-Based Robotics | p. 347 |
The WOALA Scheme | p. 349 |
First Experiments with WOALA | p. 353 |
Discussion and Perspectives | p. 361 |
Embodied, On-Line, On-Board Evolution for Autonomous Robotics | p. 362 |
Controllers, Genomes, Learning, and Evolution | p. 363 |
Classification of Approaches to Evolving Robot Controllers | p. 364 |
The Classical Off-Line Approach Based on a Master EA | p. 368 |
On-Line Approaches | p. 369 |
Testing Encapsulated Evolutionary Approaches | p. 372 |
Conclusions and Future Work | p. 382 |
Artificial Sexuality and Reproduction of Robots Organisms | p. 384 |
The Role of Sexuality for Robots | p. 385 |
Artificial Reproduction | p. 388 |
Implementation of Artificial Sexuality on Real Robots | p. 390 |
Evolutionary Engineering | p. 392 |
Evolution of Multicellular Organisms | p. 397 |
Sex and Reproduction of Symbiotic Robots | p. 399 |
Conclusion | p. 403 |
Self-learning Behavior of Virus-Like Artificial Organisms | p. 403 |
Effectiveness of Evolutionary Optimization for Genetic Cloud | p. 405 |
Interaction between Evolution and Learning in an Evolutionary Process | p. 412 |
Evolutionary Emergence of a Cooperation between Agents | p. 418 |
Discovering of Chains of Actions by Self-learning Agents | p. 421 |
Virus-Like Organisms: New Adaptive Paradigm? | p. 424 |
Towards the Emergence of Artificial Culture in Collective Robotic Systems | p. 425 |
Project Aims | p. 425 |
The Artificial Culture Laboratory | p. 426 |
The Challenges and the Case for an Emerging Robot Culture | p. 428 |
Robot Memes and Meme Tracking | p. 430 |
Concluding Remarks | p. 433 |
Final Conclusions | p. 435 |
References | p. 437 |
Index | p. 467 |
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