A*Grid2D: Optimized Grid-Based A* Pathfinding for 2D Game Environments
Year: 2025
Type: article
Abstract: In recent years, the complexity of 2D game environments has increased, requiring more sophisticated AI-driven solutions to ensure seamless and immersive gameplay experiences. Traditional pathfinding algorithms often struggle to adapt in real time to dynamic obstacles and multi-agent interactions, making advancements like A*Grid2D crucial for modern game development. This study significantly impacts modern 2D game development by refining NPC pathfinding and enhancing gameplay immersion, efficiency, and realism. A*Grid2D addresses key challenges—dynamic obstacles, multiple agents, goal shifts, and smooth movement—ensuring seamless navigation in complex environments. Its optimized performance benefits developers by enabling more responsive AI behaviors without compromising computational efficiency. In today's 2D games, this results in smarter NPCs, faster decision-making, and more dynamic interactions, ultimately improving the player’s experience. By providing a tailored pathfinding solution, A*Grid2D contributes to the evolution of AI-driven mechanics in 2D games, ensuring fluid movement, realistic behavior, and strategic gameplay while shaping the future of NPC navigation.
Author: Randy Tolentino
Institution: Hanseo University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Hanseo University
OpenAlex ID: W4413452167