Multi-threaded Recast-Based A* Pathfinding for Scalable Navigation in Dynamic Game Environments

TL;DR

This paper introduces a multi-threaded A* pathfinding framework using Recast-based mesh generation and crowd density analysis, enabling real-time, collision-free navigation for thousands of agents in dynamic 3D environments.

Contribution

It presents a novel multi-threaded approach that combines mesh generation, trajectory smoothing, and crowd density analysis for scalable, real-time pathfinding in complex game worlds.

Findings

Maintains over 350 FPS with 1000 agents

Enables collision-free crowd navigation

Effective in complex multi-level environments

Abstract

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.