By Lukas König
This booklet examines 4 new techniques for challenge fixing in evolutionary robotics and agent simulation. Of specific curiosity is a brand new process that imitates average evolution, permitting mutation and recombination operations to be tailored throughout the robot evolution method.
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Additional info for Complex Behavior in Evolutionary Robotics
4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 Fig. 14 Fig. 15 Fig. 16 Fig. 17 Fig. 18 Fig. 19 Fig. 20 Fig. 21 Fig. 22 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 Fig. 14 Fig. 15 Fig. 1 List of Notations ABS Agent-based Simulation AI Artificial Intelligence ANN Artificial Neural Network AUXB Auxiliary Battery Board BB Bottom Board CA Collision Avoidance ceGPM completely evolvable GPM CIS Completely Implicit Selection CTB Cortex Controller Board DC Direct Current EAS Easy Agent Simulation EC Evolutionary Computation EE Embodied Evolution EIS Explicit and Implicit Selection ER Evolutionary Robotics ESR Evolutionary Swarm Robotics FMG Finite Moore Generator FSM Finite State Machine GP Gate Passing GPM Genotype-Phenotype Mapping GUI Graphical User Interface , IPR Institute for Process Control and Robotics IR Infra Red KIT Karlsruhe Institute of Technology LJGL Lightweight Java Game Library LOC Lines Of Code MAPT Moore Automaton for Protected Translation MARB Moore Automaton for Robot Behavior MB Motor Board MVC Model/View/Controller OB ODeM-RF Board OCLR Organic Computing: Learning Robots OO Object-Oriented PCB Printed Circuit Board PSS Possibly Successful Setup SC Selection Confidence SD Standard Deviation SPI Simulation Plugin Interface SPL Scheduler/Plugin Layer TCP Transmission Control Protocol TDMA Time Division Multiple Access 1 Introduction Preamble: Nature has gained a huge variety of complex life forms which populate numerous biological niches all over the earth.
Various types of robot behaviors have been evolved in the past, the most popular of which has been Collision Avoidance (CA)a (or Obstacle Avoidance). ) around while avoiding to crash into obstacles or other robots . Other behaviors include Wall Following , Object Detection and Recognition , , Shortest Path Discovery , Object Transport , and others; the references denote exemplary examples out of bigger pools of publications referring to the respective behaviors. Beyond these behaviors which can be performed by a single robot or in parallel by a population of robots, a popular behavior specifically suited for swarms of robots is Group Transport  which denotes the collective transport of an object usually too heavy for a single robot to carry or push.
De. com Acknowledgements This book presents and concludes a major part of my research conducted from early 2007 until today. By far the greatest fraction of this time and, more importantly, the best of the presented ideas are strongly connected to the Institute AIFB where I worked as a research associate since December 2007. Here, my advisor Prof. Hartmut Schmeck gave me the opportunity to follow my research interests virtually limit-free in a creative and inspirational working environment, for which I am deeply grateful to him.