Book description
Today, autonomous robots are used in a rather limited range of applications such as exploration of inaccessible locations, cleaning floors, mowing lawns etc. However, ongoing hardware improvements (and human fantasy) steadily reveal new robotic applications of significantly higher sophistication. For such applications, the crucial bottleneck in the engineering process tends to shift from physical boundaries to controller generation. As an attempt to automatize this process, Evolutionary Robotics has successfully been used to generate robotic controllers of various types. However, a major challenge of the field remains the evolution of truly complex behavior. Furthermore, automatically created controllers often lack analyzability which makes them useless for safety-critical applications. In this book, a simple controller model based on Finite State Machines is proposed which allows a straightforward analysis of evolved behaviors. To increase the model's evolvability, a procedure is introduced which, by adapting the genotype-phenotype mapping at runtime, efficiently traverses both the behavioral search space as well as (recursively) the search space of genotype-phenotype mappings. Furthermore, a data-driven mathematical framework is proposed which can be used to calculate the expected success of evolution in complex environments.
Table of contents
- Also of interest
- Title Page
- Copyright Page
- Acknowledgements
- Dedication
- Table of Contents
- Table of Figures
- List of Tables
- List of Notations
- 1 Introduction
-
2 Robotics, Evolution and Simulation
-
2.1 Evolutionary Training of Robot Controllers
- 2.1.1 Two Views on Selection in ER and ESR
- 2.1.2 Classification of Fitness Functions in ER
- 2.1.3 The Bootstrap Problem
- 2.1.4 The Reality Gap
- 2.1.5 Decentralized Online Evolution in ESR
- 2.1.6 Evolvability, Controller Representation and the Genotype-Phenotype Mapping
- 2.1.7 Controller Representation
- 2.1.8 Recombination Operators
- 2.1.9 Success Prediction in ESR
- 2.2 Agent-based Simulation
-
2.1 Evolutionary Training of Robot Controllers
- 3 The Easy Agent Simulation
-
4 Evolution Using Finite State Machines
-
4.1 Theoretical Foundations
- 4.1.1 Preliminaries
- 4.1.2 Definition of the MARB Controller Model
- 4.1.3 Encoding MARBs
- 4.1.4 Mutation and Hardening
- 4.1.5 Selection and Recombination
- 4.1.6 Fitness calculation
- 4.1.7 The Memory Genome: a Decentralized Elitist Strategy
- 4.1.8 Fitness Adjustment after Mutation, Recombination and Reactivation of the Memory Genome
- 4.1.9 The Robot Platforms
- 4.2 Preliminary Parameter Adjustment using the Example of Collision Avoidance
- 4.3 A Comprehensive Study Using the Examples of Collision Avoidance and Gate Passing
- 4.4 Experiments With Real Robots
- 4.5 Chapter Résumé
-
4.1 Theoretical Foundations
-
5 Evolution and the Genotype-Phenotype Mapping
- 5.1 Overview of the Presented Approach
- 5.2 A Completely Evolvable Genotype-Phenotype Mapping
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5.3 Evaluation of the Proposed Evolutionary Model
- 5.3.1 First Part – Method of Experimentation
- 5.3.2 First Part – Results and Discussion
- 5.3.3 Second Part – An Alternate Completely Evolvable Genotype-Phenotype Mapping and its Effects on Evolvability
- 5.3.4 Second Part – Method of Experimentation
- 5.3.5 Second Part – Results and Discussion
- 5.3.6 Third Part – Method of Experimentation
- 5.3.7 Third Part – Results and Discussion
- 5.4 Chapter Résumé
- 6 Data Driven Success Prediction of Evolution in Complex Environments
- 7 Conclusion
- References
- Index
Product information
- Title: Complex Behavior in Evolutionary Robotics
- Author(s):
- Release date: March 2015
- Publisher(s): De Gruyter Oldenbourg
- ISBN: 9783110409185
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