High-quality Path Planning for Autonomous Mobile Robots with η3-Splines and Parallel Genetic Algorithms

Abstract- In this paper we apply the composite η3 splines to collision-free curvature-derivative continuous shorter path planning of wheeled mobile robots, modelled as unicycle, within known static environments. The path planning problem is formulated as a bi-objective optimization problem of search-ing a sequence of N ordered intermediate configurations be-tween start and goal configurations over the group of all possi-ble configurations that avoid collisions with obstacles while minimize the path length. We employ Pareto-based evolution-ary multi-objective optimization using island parallel genetic algorithm (IPGA) with migration to tackle this problem. Ex-tensive simulations demonstrate that the generated paths are statistically both shorter and smoother, up to third order geo-metric continuity, when compared to path planning by cubic spiral. In particular, the effect of N that represents path com-plexity on path planning performance of the evolutionary path planner based on IPGA is extensively tested on environments consisting of similar number of obstacles but different configu-rations. Index Terms- genetic algorithm, multiobjective optimiza