Parameter Optimization for Walking Patterns and the Geometry of in-Pipe Robots

Abstract

This paper discusses the problem of generating walking pattern parameters for in-pipe robots using global optimization procedures. The choice of the objective function that prevents a robot from assuming a singular configuration or making collisions and promotes faster walking is discussed. The optimization is performed using a number of methods, including genetic algorithm, particle swarm optimization, simulated annealing and others. A comparative analysis of these methods under different optimization run time constraints was presented. The results obtained in this study suggest that the proposed method is a valid practical solution for tuning walking pattern parameters for in-pipe robots.