Genetic Programming Approach to the Construction of a Neural Network for Control of a Walking Robot (1992)
| Venue: | In IEEE International Conference on Robotics and Automation |
| Citations: | 38 - 2 self |
BibTeX
@INPROCEEDINGS{Lewis92geneticprogramming,
author = {M. Anthony Lewis and Andrew H. Fagg and Alan Solidum},
title = {Genetic Programming Approach to the Construction of a Neural Network for Control of a Walking Robot},
booktitle = {In IEEE International Conference on Robotics and Automation},
year = {1992},
pages = {2618--2623},
publisher = {IEEE Press}
}
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Abstract
This paper describes the staged evolution of a complex motor pattern generator (MPG) for the control of a walking robot. The MPG is composed of a network of neurons with weights determined by Genetic Algorithm (GA) optimization. Staged evolution is used to improve the convergence rate of the algorithm. First, an oscillator for the individual leg movements is evolved. Then, a network of these oscillators is evolved to coordinate the movements of the different legs. By introducing a staged set of manageable challenges, the algorithm's performance is improved. These techniques may be applicable to other complex or ill-posed control problems in robot control. 1 Introduction It is well known that intelligent robots must interact closely with the world. This interaction might be considered a discourse between the the computational structure of the robot and structure of world, mediated by sensor and actuators. The design of this computational structure is a formidable task. The engineer mu...
