Nature-Inspired Techniques for Autonomous Agents: A Theoretical Overview

This research paper explores the benefits of nature-inspired strategies, namely Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Ant Colony Optimization (ACO) in the field of autonomous agents like mobile robots and smart wheelchairs. The aim is to highlight the power of these computational paradigms inspired from the natural environments to enhance the autonomy, adaptability, accuracy and efficiency of operations in context of autonomous intelligent systems. By adopting these nature-bound strategies, researchers, scientists and engineers can design, operate and optimize robust control strategies, navigation operations and decision-making processes, enabling the autonomous agents to perform various complex functions and interact with outer environments more efficiently. This research work explores the theoretical foundations, practical implementations and potential future benefits of adding PSO, GA and ACO into the design and development of autonomous agents with a clear focus on mobile robots and smart wheelchairs. It also discusses recent developments, advancements, challenges and future directions in this interdisciplinary field, aiming to inspire further inventions and innovation.