Synthetic Evolution for Wheeled Robot Cognition in RF-Localization Behavior

2009 International Conference on Future Computer and Communication Pub Date : 2009-04-03 DOI:10.1109/ICFCC.2009.102

C. K. On, J. Teo, A. Saudi

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Abstract

This paper discussed the utilization of a multi-objective approach for evolving artificial neural networks (ANNs) that act as a controller for radio frequency (RF)-localization behavior of a virtual Khepera robot simulated in a 3D, physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal sets of ANNs that optimize the conflicting objectives of maximizing the virtual Khepera robot’s behavior for homing towards a RF signal source and minimizing the number of hidden neurons used in its feed-forward ANNs controller. A fitness function used for mobile robot RF-localization behavior is proposed. The experimentation results showed the virtual Khepera robot was able to navigate towards signal source with using only a small number of hidden neurons. Furthermore, the Pareto-frontier solutions have been utilized for robustness testing purposes in the environment differs as that used during evolution. The results showed the PDE-EMO algorithm can be practically used in generating the required robot controllers for RF-localization behavior.

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轮式机器人射频定位行为认知的综合进化

本文讨论了在基于物理的3D环境中模拟虚拟Khepera机器人的射频(RF)定位行为的控制器中，进化人工神经网络(ann)的多目标方法的应用。利用Pareto-frontier Differential Evolution (PDE)算法生成了Pareto最优人工神经网络集，该集优化了虚拟Khepera机器人对射频信号源的归向行为最大化和前馈人工神经网络控制器中隐藏神经元数量最小化的冲突目标。提出了一种用于移动机器人射频定位行为的适应度函数。实验结果表明，虚拟Khepera机器人仅使用少量隐藏神经元就能导航到信号源。此外，帕累托边界解决方案已被用于鲁棒性测试的目的，在不同的环境中使用的进化。结果表明，PDE-EMO算法可以实际用于生成射频定位行为所需的机器人控制器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2009 International Conference on Future Computer and Communication

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