Neural Fuzzy Techniques in Sonar-Based Collision Avoidance
In this chapter we report application of neuro-fuzzy control to sonar based collision avoidance of our TRC labmate robot, Figure 5. To this end, we will first provide the reader with a brief overview of existing concepts of neuro-fuzzy control and then present our own approach based on Radial Basis Functions. This particular Fuzzy-RBF (FRBF) approach is innovative w.r.t. three aspects of neuro-fuzzy control. First, it alleviates the covering problem in fuzzy control, i.e. the problem of an exponential growth of the number of rules with the dimension of the input space. Second, it provides a means for exact interpolation, i.e. inspite of overlapping membership functions the output of the controller can be guaranteed to take the value of the i-th rule if it has degree of fulfillment one. Finally, by using DCS, , instead of RBF networks, output calculation of the controller is very fast on average, since only a few rules (the best matching ones) are evaluated on presentation of an input to the controller.
KeywordsMobile Robot Kalman Filter Fuzzy Rule Fuzzy Control Fuzzy Controller
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