用高斯信念传播提升多智能体路径规划精度，减少碰撞与偏离。
Multi-Agent Path Planning in Complex Environments using Gaussian Belief Propagation with Global Path Finding
- 结合高斯信念传播与路径积分，引入追踪因子保证全局路径遵循。
- 单机场景路径偏差降低28%，多机场景降低16%。
- 适合复杂环境中的多机器人协同导航，尤其配合结构化规划时效果更优。
多智能体路径规划是机器人领域的重要挑战，要求智能体在复杂环境中避障并优化行进效率。本文通过将高斯信念传播与路径积分相结合，并引入一种新型追踪因子，确保严格遵循全局路径。该方法在两种全局路径规划策略下进行了测试：快速探索随机树（RRT）和基于预设车道结构的结构化规划器，后者提升了协作效率。构建了仿真环境以验证方法在多种导航与通信挑战下的表现。结果表明，追踪因子使单智能体路径偏差减少28%，多智能体场景减少16%，显著提升了多智能体协调能力，尤其在结合结构化全局规划时效果更佳。
原文摘要 · Abstract (English)
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.
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