Fuzzy Inference System Optimization by Evolutionary Approach for Mobile Robot Navigation

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Fatma Boufera 1,* Fatima Debbat 1 Nicolas Monmarche 2 Mohamed Slimane 2 Mohamed Faycal Khelfi 3

1. Computer Science Department, Mascara University, Mascara, Algeria

2. Laboratoire d’Informatique, University François Rabelais, Tours, France

3. Computer Science Department, university of Ahmed Ben Bella1, Oran, Algeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2018.02.08

Received: 3 Apr. 2017 / Revised: 1 Aug. 2017 / Accepted: 15 Sep. 2017 / Published: 8 Feb. 2018

Index Terms

Mobile robots, autonomous navigation, obstacle avoidance, fuzzy logic, evolutionary algorithm, learning


The problem in the autonomous navigation of a mobile robot is to define a strategy that allows it to reach the final destination and avoiding obstacles. Fuzzy logic is considered as an important tool to solve this problem. It can mimic reasoning abilities of the human being in navigation tasks. However a major problem of fuzzy systems is obtaining their parameters which are generally specified by human experts. This process can be long and complex. In order to generate optimal parameters of fuzzy controller, this work propose a learning and optimization process based on ant colony algorithm ACO and genetic algorithm operators (crossover and mutation).We present a comparison between inference system for autonomous navigation based on fuzzy logic before and after learning. The simulated results show clearly the impact of the optimization approach improves the fuzzy controller performance mainly in obstacle avoidance and detection of the shortest path.

Cite This Paper

Fatma Boufera, Fatima Debbat, Nicolas Monmarché, Mohamed Slimane, Mohamed Faycal Khelfi, "Fuzzy Inference System Optimization by Evolutionary Approach for Mobile Robot Navigation", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.2, pp.85-93, 2018. DOI:10.5815/ijisa.2018.02.08


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