While the A* algorithm remains the industry standard for game pathfinding, its integration into dynamic 3D environments faces trade-offs between computational performance and visual realism. This paper proposes a multi-threaded framework that enhances standard A* through Recast-based mesh generation, Bezier-curve trajectory smoothing, and density analysis for crowd coordination. We evaluate our system across ten incremental phases, from 2D mazes to complex multi-level dynamic worlds. Experimental results demonstrate that the framework maintains 350+ FPS with 1000 simultaneous agents and achieves collision-free crowd navigation through density-aware path coordination.
Cite
@article{arxiv.2602.04130,
title = {Multi-threaded Recast-Based A* Pathfinding for Scalable Navigation in Dynamic Game Environments},
author = {Tiroshan Madushanka and Sakuna Madushanka},
journal= {arXiv preprint arXiv:2602.04130},
year = {2026}
}