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 ( μ GA), and a micro-Artificial Immune System ( μ AIS); the fourth algorithm is a novel micro-segmented Genetic Algorithm ( μ 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 μ 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 μ sGA is better than the other three algorithms (GA, μ GA and μ 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, μ GA (76%), μ AIS (32%) and μ 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 μ sGA bio-micro algorithm and a mES architecture.
Highlights
The solutions found by genetic algorithm (GA) [1] methods can improve traditional techniques; they are well known for being robust when used for numerical optimization and are applied where thereAppl
We propose an alternative method of using μGA with small populations and use segmented elements or individuals to reduce the size of the binary chain
The GA showed the worst performance of around 7.188 s, with 911 generations needed to obtain the trotting CPG for robot’s locomotion
Summary
The solutions found by genetic algorithm (GA) [1] methods can improve traditional techniques; they are well known for being robust when used for numerical optimization and are applied where thereAppl. The solutions found by genetic algorithm (GA) [1] methods can improve traditional techniques; they are well known for being robust when used for numerical optimization and are applied where there. Given the strengths of these algorithms, some researchers have applied GA approaches to solving the problem of locomotion of legged robots. Chae and Park [2] used a GA for the locomotion of a quadruped robot. This approach automatically generated solutions and selected the best option for robot locomotion with 12 Degrees of Freedom (DOF). The GA is applied to find solutions to problems of optimization at systems with various DOF and in which traditional methods are not adjusted. In [13], a Central
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