Performance Comparisons of Bio-Micro Genetic Algorithms on Robot Locomotion

Abstract

This paper presents a comparison of four algorithms and identifies the better one in terms of convergence to the best performance for the locomotion of a quadruped robot designed. Three algorithms found in the literature review: a standard Genetic Algorithm (GA), a micro-Genetic Algorithm ( $\mu$ GA), and a micro-Artificial Immune System ( $\mu$ AIS); the fourth algorithm is a novel micro-segmented Genetic Algorithm ( $\mu$ sGA). This research shows how the computing time affects the performance in different algorithms of the gait on the robot physically; this contribution complements other studies that are limited to simulation. The $\mu$ sGA algorithm uses less computing time since the individual is segmented into specific bytes. In contrast, the use of a computer and the high demand in computational resources for the GA are avoided. The results show that the performance of $\mu$ sGA is better than the other three algorithms (GA, $\mu$ GA and $\mu$ AIS). The quadruped robot prototype guarantees the same conditions for each test. The structure of the platform was developed by 3D printing. This structure was used to accommodate the mechanisms, sensors and servomechanisms as actuators. It also has an internal battery and a multicore Embedded System (mES) to process and control the robot locomotion. The computing time was reduced using an mES architecture that enables parallel processing, meaning that the requirements for resources and memory were reduced. For example, in the experiment of a one-second gait cycle, GA uses 700% of computing time, $\mu$ GA (76%), $\mu$ AIS (32%) and $\mu$ sGA (13%). This research solves the problem of quadruped robot’s locomotion and gives a feasible solution (Central Pattern Generators, (CPGs)) with real performance parameters using a $\mu$ sGA bio-micro algorithm and a mES architecture.