Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

Abstract EN

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem.

The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit.

In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other.

This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation.

GPU-based calculations are high-performance solution and free up the CPU.

The experimental results show that the performance of the GPU is many times faster than CPU.