Immune evolutionary programming-based locomotion control of autonomous mobile robot

This paper investigates a robust locomotion control of a mobile robot based on a new evolutionary programming called immune genetic algorithm (IGA). It explores the principle in natural immune system (NIS) which focuses mainly on: somatic hypermutation, immune memory, and gene libraries, and then, the obtained enhanced genetic algorithm (GA) is used to evolve the three control parameters used in a robust locomotion controller to obtain time optimal, shortest path, and minimum energy performance. The simulation experiments demonstrated that this novel evolutionary algorithm is also more adaptable and accurate than the other algorithms proposed in the literature.