Investigations on meta-heuristic algorithms for intelligent speed regulation of mobile robot

Abstract

With technical advances in robotics and technology, mobile robots (MRs) have become more significant in medical applications that require great subtlety, accuracy and efficiency. MRs are primarily time-varying and nonlinear systems that require intelligent controller rather conventional controllers. When artificial intelligence (AI) is employed in MRs, they become intelligent or autonomous. So, metaheuristic algorithms firefly algorithm (FFA), genetic algorithm (GA), particle swarm optimization (PSO) and grey wolf optimization (GWO) have been investigated in this paper to get the optimal outcome and improve the performance of the MR controller, resulting in an overall system improvement. The study exhibit that GWO control is superior for time-varying and nonlinear MR than conventional control in terms of time response parameters and accuracy assessment.

Keywords

• Mobile robots
• Artificial intelligence (AI)
• Firefly algorithm (FFA)
• Genetic algorithm (GA)
• Particle swarm optimization (PSO)
• Grey wolf optimization (GWO)