Metaheuristic Algorithms for Robot Path Planning

Abstract

This book chapter provides an in-depth exploration of metaheuristic algorithms in the context of robot path planning, addressing the challenges and opportunities in this dynamic field. Robot path planning is crucial for autonomous navigation in diverse environments, and traditional methods often struggle with complex scenarios. Metaheuristic algorithms offer promising solutions by leveraging optimization and search techniques. This chapter presents an overview of prominent metaheuristic algorithms, including genetic algorithms, simulated annealing, particle swarm optimization, ant colony optimization, and evolutionary strategies, discussing their principles and applicability. It also examines factors influencing algorithm performance and recent advancements, such as hybridization and machine learning integration. Through real-world examples and case studies, this chapter aims to provide insights and guidance for researchers, practitioners, and students interested in employing metaheuristic algorithms for effective robot path planning.