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

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. Th e application of combined soft computing techniques - neural network, fuzzy logic, genetic algorithms, tabu search and others - is becoming increasing ly popular among various researchers due to their ability to handle impreci sion and uncertainties that are often present in many real-world problems. In this study, genetic algorithms are used for tuning the scaling factors o f the state variables (keeping the relative spacing of the membership distr ibutions 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 i nvolving classical methods and using FLCs they are either computationally e xtensive or they do not attempt to find optimal controllers. The proposed g enetic-fuzzy approach optimizes the travel time of a robot off-line by simu ltaneously finding an optimal fuzzy rule base and optimal scaling factors o f the state variables. A mobile robot can 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. (C) 1999 Elsevier Science Inc. All right s reserved.