SoPC-based parallel elite genetic algorithm for global path planning of an autonomous omnidirectional mobile robot

This paper presents an efficient parallel elite genetic algorithm (PEGA) for global path planning of an omnidirectional mobile robot moving in a static environment expressed by a grid-based map. This efficient PEGA, consisting of two parallel EGAs along with a migration operator, is proposed for global path planning of the mobile robots. The PEGA takes advantages of maintaining better population diversity, inhibiting premature convergence and keeping parallelism than conventional GAs do. The generated collision-free path is optimal in the sense of the shortest distance. The pipelined hardware implementation of IP (intellectual property) core library on a field-programmable gate array (FPGA) chip is employed to significantly speedup the processing time. Furthermore, a soft-core processor and a real-time operating system (RTOS) are embedded into the same chip to perform the global path planning using hardware/software co-design technique and SoPC (system-on-a-programmable-chip) concept. The merit and performance of the proposed SoPC-based PEGA are illustrated by conducting several simulations and experiments.