Multiobjective Evolution of Neural Controllers and Task Complexity

Robots operating in everyday life environments are often required to switch between different tasks. While learning and evolution have been effectively applied to single task performance, multiple task performance still lacks methods that have been demonstrated to be both reliable and efficient. Thi...

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Bibliographic Details
Published inIEEE transactions on robotics Vol. 23; no. 6; pp. 1225 - 1234
Main Author Capi, G.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.12.2007
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Applied sciences
Chromium
Cognitive robotics
Computer science; control theory; systems
Control theory. Systems
Controllers
Evolutionary computation
Evolutionary robotics
Exact sciences and technology
Intelligent agent
multiobjective evolution
multiple task performance
neural controller
Neural networks
Protection
Robot sensing systems
Robotics
Robots
Rodents
Scattering
Simulation
Switches
Neurocontrollers
Multiagent system
Task complexity
Moving robot
Evolutionary algorithm
Object oriented
Multiobjective programming
Objective function
Neural network
Robotics
Intelligent control
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Summary:Robots operating in everyday life environments are often required to switch between different tasks. While learning and evolution have been effectively applied to single task performance, multiple task performance still lacks methods that have been demonstrated to be both reliable and efficient. This paper introduces a new method for multiple task performance based on multiobjective evolutionary algorithms, where each task is considered as a separate objective function. In order to verify the effectiveness, the proposed method is applied to evolve neural controllers for the Cyber Rodent (CR) robot that has to switch properly between two distinctly different tasks: 1) protecting another moving robot by following it closely and 2) collecting objects scattered in the environment. Furthermore, the tasks and neural complexity are analyzed by including the neural structure as a separate objective function. The simulation and experimental results using the CR robot show that the multiobjective-based evolutionary method can be applied effectively for generating neural networks that enable the robot to perform multiple tasks simultaneously.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2007.910773