PSO and GA optimization methods comparison on simulation model of a real hexapod robot

Abstract

The Szabad(ka)-II hexapod robot with 18 DOF is a suitable mechatronic device for the development of hexapod walking algorithm and engine control [1, 2]. The required full dynamic model has already been built [3], which is used as a black-box for the walking optimizations in this research. The ellipse-based walking trajectory has been generated that was required by the low-cost straight line walking [4], and the purpose was to optimize its parameters. The Particle Swarm Optimization (PSO) method was chosen for simple and effective working, which does not require the model's mathematical description or differentiation. Previously the authors performed an evolutionary Genetic Algorithm (GA) optimization for a similar trial case [5], and posed the principles of the quality measurement of hexapod walking [4, 5]. The same visual evaluation and comparison was applied in this paper for the results of both optimization methods. PSO has produced better and faster results compared to GA.