Author:
Sendari Siti, ,Mabu Shingo,Tjahjadi Andre,Hirasawa Kotaro
Abstract
Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms, which is represented by graph structures. It was extended to GNP with Reinforcement Learning (GNP-RL) which combines online learning and evolution. GNP-RL succeeded in implementing the wall following behaviors of a Khepera robot. The objective of this paper is to improve the robustness of GNP-RL by introducing fuzzy GNP with noises. Fuzzy GNP overcomes the sharp boundary problem using the probabilistic transition on fuzzy judgment nodes, which improves the exploration ability. Furthermore, the robustness of fuzzy GNP can be improved by adding Gaussian noises to the sensors during the training phase. In order to evaluate the robustness of fuzzy GNP with noises, the wall following of a Khepera robot is simulated. Simulation results show that fuzzy GNP with noises is superior to GNP-RL.
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
Reference18 articles.
1. S. Parasuraman, V. Ganapathy, and B. Shirinzadeh, “Behavior based Mobile Robot Navigation Technique for Real World Environment using Fuzzy Logic System,” in Proc. of the IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 3359-3364, 2004.
2. P. Rusu, E. M. Petriu, T. E. Whalen, A. Cornell, and H. J. W. Spoelder, “Behavior-Based Neuro-Fuzzy Controller for Mobile Robot Navigation,” IEEE Trans. on Instrumentation and Measurement, Vol.52, No.4, pp. 1335-1340, 2003.
3. K. J. Lee and B. T. Zhang, “Learning Robot Behavior by Evolving Genetic Programs,” in Proc. of the 26th Annual Conf. of the IEEE Industrial Electronic Society IECON, Vol.4, pp. 2867-2872, 2000.
4. S. Mabu, H. Hatakeyama, M. T. Thu, K. Hirasawa, and J. Hu, “Genetic Network Programming with Reinforcement Learning and Its Application to Making Mobile Robot Behavior,” IEEJ Trans. on Electronics, Information and System, Vol.126. No.8, pp. 1009-1015, 2006.
5. M. J. Er and Y. Zhou, “Automatic Generation of Fuzzy Inference System via Unsupervised Learning,” Neural Networks, Vol.21, pp. 1556-1566, 2008.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献