A systematic review and analysis of A-star pathfinding algorithms and its variations

This paper presents a comprehensive review and analysis of the A-star(A*) pathfinding algorithm and its variations. A-star algorithm's core principles, practical representations, and diverse applications are examined. The study extends to various A* derivatives, including Weighted A*, IDA*(Iterative Deepening A*), ARA*(Anytime Repairing A*), D*(Dynamic A*), LPA*(Lifelong Planning A*), D* Lite, and AD*(Anytime Dynamic D*). Each variant's unique adaptations and efficiencies are explored, highlighting their suitability for specific challenges in pathfinding tasks. This work aims to elucidate the intricacies of these algorithms, demonstrating their significance and versatility in solving complex navigational problems, thus offering valuable insights for future research and application development in the field of artificial intelligence and game industry.