A Comprehensive Survey of Path Planning Algorithms for Autonomous Systems and Mobile Robots: Traditional and Modern Approaches

Path planning is a crucial component for robotics and autonomous systems, which facilitate navigation through dynamic and uncertain environments while avoiding obstacles. This review paper provides an extensive examination of various path planning methodologies, the challenges they face in various uncertain environments, and recent advancements in the field. It provides a thorough overview of path planning, beginning with its significance and followed by a detailed study of path planning techniques in both static and dynamic environments. The paper reviews traditional path planning algorithms, highlighting their strengths, limitations, and applications. The paper also examines state-of-the-art methods for modeling environments, with an emphasis on dynamic constraints and evolving topography. Key representations such as occupancy grids, potential fields, and dynamic maps are discussed. Additionally, the real-time flexibility and anticipatory capacities of various reactive, predictive, optimization, and machine learning approaches included in modern path planning are studied. Later hybrid approaches that combine both for robust performance and its evolving application are detailed. Finally, it offers a comparative analysis of various algorithms through evaluation metrics, benchmarking, and application studies. The primary objective of this review is to provide an in-depth insight into path planning by encasing both traditional and modern techniques in the field of recent robotics research.