Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
This research explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis tasks while avoiding obstacles in a simulated 30 physics environment, to overcome problems involving more than one objective, where these objectives us...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Research Report |
| Language: | en |
| Published: |
Universiti Malaysia Sabah
2006
|
| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/23190/1/Development%20of%20a%20Bioinspired%20optimization%20algorithm%20for%20the%20automatic%20generation%20of%20multiple%20distinct%20behaviors%20in%20simulated%20mobile%20robots.pdf https://eprints.ums.edu.my/id/eprint/23190/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis tasks while avoiding
obstacles in a simulated 30 physics 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 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 (POE) 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 POE-MOEA algorithm. The generated robot controllers allowed the robots to move towards to the light source even the simulation and testing environments are noticeably different. |
|---|