A comprehensive review of path planning algorithms for autonomous navigation

Path planning enables autonomous agents such as robots, self-driving vehicles, and UAVs to navigate from a starting point to a target destination while avoiding obstacles and adhering to operational constraints. As autonomous technologies become more prevalent in real-world applications, the demand for robust, adaptive, and computationally efficient path planning algorithms has intensified. This paper presents a comprehensive review of path planning strategies, focusing on classical, metaheuristic, and AI-based approaches. It explores the challenges posed by dynamic environments, non-holonomic constraints, and varying levels of environmental knowledge. The review also examines the strengths and limitations of each algorithmic category, highlighting their suitability for diverse applications ranging from industrial automation to autonomous navigation. Furthermore, the paper discusses emerging trends, including the integration of machine learning and reinforcement learning techniques, and outlines future research directions aimed at enhancing the adaptability and performance of path planning systems in complex, unstructured environments.