Multi-agent path planning is a critical challenge in robotics, requiring agents to navigate complex environments while avoiding collisions and optimizing travel efficiency. This work addresses the limitations of existing approaches by combining Gaussian belief propagation with path integration and introducing a novel tracking factor to ensure strict adherence to global paths. The proposed method is tested with two different global path-planning approaches: rapidly exploring random trees and a structured planner, which leverages predefined lane structures to improve coordination. A simulation environment was developed to validate the proposed method across diverse scenarios, each posing unique challenges in navigation and communication. Simulation results demonstrate that the tracking factor reduces path deviation by 28% in single-agent and 16% in multi-agent scenarios, highlighting its effectiveness in improving multi-agent coordination, especially when combined with structured global planning.

翻译：多智能体路径规划是机器人学中的关键挑战，要求智能体在复杂环境中导航，同时避免碰撞并优化移动效率。本研究通过将高斯置信传播与路径积分相结合，并引入一种新颖的跟踪因子以确保严格遵循全局路径，从而解决了现有方法的局限性。所提出的方法使用两种不同的全局路径规划方法进行测试：快速探索随机树和一种结构化规划器，后者利用预定义的通道结构来改善协调性。开发了一个仿真环境，以在多种场景下验证所提出的方法，每个场景在导航和通信方面都提出了独特的挑战。仿真结果表明，跟踪因子在单智能体场景中将路径偏差降低了28%，在多智能体场景中降低了16%，突显了其在改善多智能体协调性方面的有效性，尤其是在与结构化全局规划结合使用时。