A Hybrid Implementation of Genetic Algorithm for Path Planning of Mobile Robots on FPGA

This paper proposes a hybrid design and implementation of Genetic Algorithm (GA) for the path planning of mobile robots on a Field Programmable Gate Array (FPGA). GAs have been widely used to generate an optimal path by taking the advantage of its strong optimization ability; however, GA’s computation time may be longer for complex problems. Especially, calculation of the fitness function takes a long time. A solution to accelerate it is to implement the GA in hardware. Intellectual Property (IP) hard core provides faster computation. In this study, fitness function of the GA is implemented on IP hard core while the other operators of GA run on a Microblaze soft processor. The experimental results showed that the fitness module by IP hard core can run 98.95 times faster than the fitness module by the Microblaze soft processor. The overall performance of the GA is accelerated 37.5 % by hybrid implementation with both hard and soft cores. We used the Pioneer P3-DX Mobile Robot and Xilinx XUPV5-LX110T FPGA device.