Evolutionary Algorithms (EAs) now provide mature optimization tools that have successfully been applied to many problems, from designing antennas to complete robots, and provided many human-competitive results. In robotics, the integration of EAs within the engineer's toolbox made tremendous progress in the last 20 years and proposes new methods to address challenging problems in various setups: modular robotics, swarm robotics, robotics with non-conventional mechanics (e.g. high redundancy, dynamic motion, multi-modality), etc.

This book takes its roots in the workshop on "New Horizons in Evolutionary Design of Robots" that brought together researchers from Computer Science and Robotics during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2009) in Saint Louis (USA). This book features extended contributions from the workshop, thus providing various examples of current problems and applications, with a special emphasis on the link between Computer Science and Robotics. It also provides a comprehensive and up-to-date introduction to Evolutionary Robotics after 20 years of maturation as well as thoughts and considerations from several major actors in the field.

This book offers a comprehensive introduction to the current trends and challenges in Evolutionary Robotics for the next decade.

Inhalt

Part I Introduction

.- 1 Evolutionary Robotics: Exploring New Horizons

.- Part II Invited position papers

.-2 The 'What', 'How' and the 'Why' of Evolutionary Robotics

.-3 Why Evolutionary Robotics Will Matter

.-4 Evolutionary Algorithms in the Design of Complex Robotic Systems

.- Part III Regular contributions

.-5 Evolving Monolithic Robot Controllers Through Incremental Shaping

.-6 Evolutionary Algorithms to Analyse and Design a Controller for a FlappingWings Aircraft

.-7 On Applying Neuroevolutionary Methods to Complex Robotic Tasks

.-8 Evolutionary Design of a Robotic Manipulator for a Highly Constrained Environment

.-9 A Multi-Cellular based Self-Organizing Approach for Distributed Multi-Robot Systems

.-10 Novelty-based Multiobjectivization

.-11 Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

.-12 Automated Planning Logic Synthesis for Autonomous Unmanned Vehicles in Competitive Environments with Deceptive Adversaries

.-13 Major Feedback Loops Supporting Artificial Evolution in Multi-Modular Robotics

.-14 Evolutionary Design and Assembly Planning for Stochastic Modular Robots.