A genetic-fuzzy approach for mobile robot navigation among moving obstacles

Pratihar, Dilip Kumar ; Deb, Kalyanmoy ; Ghosh, Amitabha (1999) A genetic-fuzzy approach for mobile robot navigation among moving obstacles International Journal of Approximate Reasoning, 20 (2). pp. 145-172. ISSN 0888-613X

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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S08886...

Related URL: http://dx.doi.org/10.1016/S0888-613X(98)10026-9


In this paper, a genetic-fuzzy approach is developed for solving the motion planning problem of a mobile robot in the presence of moving obstacles. The application of combined soft computing techniques - neural network, fuzzy logic, genetic algorithms, tabu search and others - is becoming increasingly popular among various researchers due to their ability to handle imprecision and uncertainties that are often present in many real-world problems. In this study, genetic algorithms are used for tuning the scaling factors of the state variables (keeping the relative spacing of the membership distributions constant) and rule sets of a fuzzy logic controller (FLC) which a robot uses to navigate among moving obstacles. The use of an FLC makes the approach easier to be used in practice. Although there exist many studies involving classical methods and using FLCs they are either computationally extensive or they do not attempt to find optimal controllers. The proposed genetic-fuzzy approach optimizes the travel time of a robot off-line by simultaneously finding an optimal fuzzy rule base and optimal scaling factors of the state variables. A mobile robot cant then use this optimal FLC on-line to navigate in presence of moving obstacles. The results of this study on a number of problem scenarios show that the proposed genetic-fuzzy approach can produce efficient knowledge base of an FLC for controlling the motion of a robot among moving obstacles.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Genetic Algorithms; Fuzzy Logic Controller; Genetic-fuzzy System; GA-based Learning; Dynamic Motion Planning
ID Code:9453
Deposited On:02 Nov 2010 12:11
Last Modified:12 Feb 2011 04:58

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