Fuzzy genetic Network Programming with Reinforcement Learning for mobile robot navigation

Author(s):  
Siti Sendari ◽  
Shingo Mabu ◽  
Kotaro Hirasawa
Author(s):  
Siti Sendari ◽  
◽  
Shingo Mabu ◽  
Andre Tjahjadi ◽  
Kotaro Hirasawa

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.


Author(s):  
Siti Sendari ◽  
Arif Nur Afandi ◽  
Ilham Ari Elbaith Zaeni ◽  
Yogi Dwi Mahandi ◽  
Kotaro Hirasawa ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document