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Evolving Controllers for Real Robots: A Survey of the Literature
- Source :
- Adaptive Behavior. 11:179-203
- Publication Year :
- 2003
- Publisher :
- SAGE Publications, 2003.
-
Abstract
- For many years, researchers in the field of mobile robotics have been investigating the use of genetic and evolutionary computation (GEC) to aid the development of mobile robot controllers. Alongside the fundamental choices of the GEC mechanism and its operators, which apply to both simulated and physical evolutionary robotics, other issues have emerged which are specific to the application of GEC to physical mobile robotics. This article presents a survey of recent methods in GEC-developed mobile robot controllers, focusing on those methods that include a physical robot at some point in the learning loop. It simultaneously relates each of these methods to a framework of two orthogonal issues: the use of a simulated and/or a physical robot, and the use of finite, training phase evolution prior to a task and/or lifelong adaptation by evolution during a task. A list of evaluation criteria are presented and each of the surveyed methods are compared to them. Analyses of the framework and evaluation criteria suggest several possibilities; however, there appear to be particular advantages in combining simulated, training phase evolution (TPE) with lifelong adaptation by evolution (LAE) on a physical robot.
- Subjects :
- 0209 industrial biotechnology
Computer science
business.industry
Evolutionary robotics
Experimental and Cognitive Psychology
Mobile robot
Robotics
02 engineering and technology
Field (computer science)
Evolutionary computation
Task (project management)
Behavioral Neuroscience
020901 industrial engineering & automation
Human–computer interaction
0202 electrical engineering, electronic engineering, information engineering
Robot
020201 artificial intelligence & image processing
Artificial intelligence
business
Adaptation (computer science)
Subjects
Details
- ISSN :
- 17412633 and 10597123
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Adaptive Behavior
- Accession number :
- edsair.doi...........872d4f9ba393e321f3730099411287f0
- Full Text :
- https://doi.org/10.1177/1059712303113003