The evolution and current frontiers of path planning algorithms for mobile robots: A comprehensive review

Abstract

The success of robotics heavily relies on path planning, which is a crucial link between computational processes and actual robot actions. This review deeply explores the evolution of path-planning algorithms tracing their development from strategies to advanced and adaptable methodologies powered by artificial intelligence. Moreover, this paper examines techniques like grid-based methods and potential fields, discussing their strengths and inherent limitations. Moving forward, this review delves into the game-changing potential of methods highlighting advancements such as Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT). Furthermore, this paper dissects the impact of intelligence on path planning emphasizing the synergy between machine learningparticularly deep reinforcement learningand robotic navigation. This review also sheds light on the challenges faced by these algorithms, including real-world implementation hurdles and potential risks associated with reliance on AI-centric approaches. Lastly, this study offers insights into trends. Speculate how emerging technologies like quantum computing may shape next-generation path planning. With its overview, this review aims to be a resource for researchers, academics, and practitioners interested, in exploring the vast realm of robotic path planning.