Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

TLDR

The paper introduces a parallel elite genetic algorithm (PEGA) for global path planning of autonomous mobile robots in structured environments. PEGA employs two parallel elite genetic algorithms with a migration operator to preserve diversity and prevent premature convergence, generates an initial feasible path that is then smoothed by cubic B‑splines, and implements both planner and smoother on an FPGA using SoC and pipelined hardware to accelerate computation. Simulations and experiments confirm that the PEGA planner and smoother improve global path planning performance for autonomous mobile robots.

Abstract

This paper presents a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.