Frontiers in Evolutionary Robotics
eBook - PDF

Frontiers in Evolutionary Robotics

  1. 598 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Frontiers in Evolutionary Robotics

About this book

This book presented techniques and experimental results which have been pursued for the purpose of evolutionary robotics. Evolutionary robotics is a new method for the automatic creation of autonomous robots. When executing tasks by autonomous robots, we can make the robot learn what to do so as to complete the task from interactions with its environment, but not manually pre-program for all situations. Many researchers have been studying the techniques for evolutionary robotics by using Evolutionary Computation (EC), such as Genetic Algorithms (GA) or Genetic Programming (GP). Their goal is to clarify the applicability of the evolutionary approach to the real-robot learning, especially, in view of the adaptive robot behavior as well as the robustness to noisy and dynamic environments. For this purpose, authors in this book explain a variety of real robots in different fields.For instance, in a multi-robot system, several robots simultaneously work to achieve a common goal via interaction; their behaviors can only emerge as a result of evolution and interaction. How to learn such behaviors is a central issue of Distributed Artificial Intelligence (DAI), which has recently attracted much attention. This book addresses the issue in the context of a multi-robot system, in which multiple robots are evolved using EC to solve a cooperative task. Since directly using EC to generate a program of complex behaviors is often very difficult, a number of extensions to basic EC are proposed in this book so as to solve these control problems of the robot.

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Yes, you can access Frontiers in Evolutionary Robotics by Hitoshi Iba in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Automation in Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Frontiers in Evolutionary Robotics
  2. Preface
  3. Contents
  4. 1. A Comparative Evaluation of Methods for Evolving a Cooperative Team
  5. 2. An Adaptive Penalty Method for Genetic Algorithms in Constrained Optimization Problems
  6. 3. Evolutionary-Based Control Approaches for Multirobot Systems
  7. 4. Learning by Experience and by Imitation in Multi-Robot Systems
  8. 5. Cellular Non-linear Networks as a New Paradigm for Evolutionary Robotics
  9. 6. Optimal Design of Mechanisms for Robot Hands
  10. 7. Evolving Humanoids: Using Artificial Evolution as an Aid in the Design of Humanoid Robots
  11. 8. Real-Time Evolutionary Algorithms for Constrained Predictive Control
  12. 9. Applying Real-Time Survivability Considerations in Evolutionary Behavior Learning by a Mobile Robot
  13. 10. An Evolutionary MAP Filter for Mobile Robot Global Localization
  14. 11. Learning to Walk with Model Assisted Evolution Strategies
  15. 12. Evolutionary Morphology for Polycube Robots
  16. 13. Mechanism of Emergent Symmetry Properties on Evolutionary Robotic System
  17. 14. A Quantitative Analysis of Memory Usage for Agent Tasks
  18. 15. Evolutionary Parametric Identification of Dynamic Systems
  19. 16. Evolutionary Computation of Multi-robot/agent Systems
  20. 17. Embodiment of Legged Robots Emerged in Evolutionary Design: Pseudo Passive DynamicWalkers
  21. 18. Action Selection and Obstacle Avoidance using Ultrasonic and Infrared Sensors
  22. 19. Multi-Legged Robot Control Using GA-Based Q-Learning Method With Neighboring Crossover
  23. 20. Evolved Navigation Control for Unmanned Aerial Vehicles
  24. 21. Application of Artificial Evolution to Obstacle Detection and Mobile Robot Control
  25. 22. Hunting in an Environment Containing Obstacles:A Combinatory Study of Incremental Evolution and Co-evolutionary Approaches
  26. 23. Evolving Behavior Coordination for Mobile Robots using Distributed Finite-State Automata
  27. 24. An Embedded Evolutionary Controller to Navigate a Population of Autonomous Robots
  28. 25. Optimization of a 2 DOF Micro Parallel Robot Using Genetic Algorithms
  29. 26. Progressive Design through Staged Evolution
  30. 27. Emotional Intervention on Stigmergy Based Foraging Behaviour of Immune Network Driven Mobile Robots
  31. 28. Evolutionary Distributed Control of a Biologically Inspired Modular Robot
  32. 29. Evolutionary Motion Design for Humanoid Robots