Cheng, N. Song and Teo, Jason Tze Wi (2008) Exploring multi-objective evolution of robot brains in obstacle and maze environments with varying complexities. In: 4th IASTED International Conference on Advances in Computer Science and Technology (ACST 2008), 2 - 4 April 2008, Langkawi, Malaysia.
Full text not available from this repository.Abstract
This paper explores a new approach of using a multiobjective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis task while avoiding obstacles or navigating through a maze in a simulated environment, to overcome problems involving more than one objective, where these objectives usually trade-off among each other and are expressed in different units. Experiments were conducted in six sets within a 10% noise environment with different task environment complexities to investigate whether the MOEA is effective for controller synthesis. A simulated Khepera robot is evolved by a Pareto-frontier Differential Evolution (PDE) algorithm, and learned through a 3-layer feed-forward artificial neural network, attempting to simultaneously fulfill two conflicting objectives of maximizing robot phototaxis behavior while minimizing the neural network's hidden neurons by generating a Pareto optimal set of controllers. Results showed that robot controllers could be successfully developed using the MOEA.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Keyword: | Evolutionary robotics, Khepera, Multi-objective evolutionary algorithm, Neural network, Phototaxis |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) |
| Department: | SCHOOL > School of Engineering and Information Technology |
| Depositing User: | ADMIN ADMIN |
| Date Deposited: | 25 Mar 2011 09:31 |
| Last Modified: | 30 Dec 2014 14:47 |
| URI: | https://eprints.ums.edu.my/id/eprint/1621 |
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