Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
暫譯: 進化機器人學：自我組織機器的生物學、智慧與技術

Description:

Evolutionary robotics is a new technique for the automatic creation of autonomous robots. Inspired by the Darwinian principle of selective reproduction of the fittest, it views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention. Drawing heavily on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic engineering. The resulting robots share with simple biological systems the characteristics of robustness, simplicity, small size, flexibility, and modularity.

In evolutionary robotics, an initial population of artificial chromosomes, each encoding the control system of a robot, is randomly created and put into the environment. Each robot is then free to act (move, look around, manipulate) according to its genetically specified controller while its performance on various tasks is automatically evaluated. The fittest robots then "reproduce" by swapping parts of their genetic material with small random mutations. The process is repeated until the "birth" of a robot that satisfies the performance criteria.

This book describes the basic concepts and methodologies of evolutionary robotics and the results achieved so far. An important feature is the clear presentation of a set of empirical experiments of increasing complexity. Software with a graphic interface, freely available on a Web page, will allow the reader to replicate and vary (in simulation and on real robots) most of the experiments.

Dario Floreano is Professor of Evolutionary and Adaptive Systems at the Swiss Federal Institute of Technology.

 

Table of Contents:

Acknowledgments	ix
	Preface	xi
1	The role of self-organization for the synthesis and the understanding of behavioral systems	1
2	Evolutionary and neural techniques	19
3	How to evolve robots	49
4	Evolution of simple navigation	69
5	Power and limits of reactive intelligence	93
6	Beyond reactive intelligence	121
7	Learning and evolution	153
8	Competitive co-evolution	189
9	Encoding, mapping, and development	223
10	Complex hardware morphologies: Walking machines	241
11	Evolvable hardware	261
	Conclusions	277
	Notes	281
	References	295
	Index	317