Multi-Agent Path Planning in Complex Environments using Gaussian Belief Propagation with Global Path Finding

2025-02-27

AI Summary

Multi-agent path planning faces dual challenges in complex dynamic environments: collision avoidance and strict global path tracking. This paper proposes a novel framework integrating Gaussian Belief Propagation (GBP) with global path planning. It introduces, for the first time, a differentiable tracking factor mechanism that enforces tight adherence of single or multiple agents to globally planned paths—generated either via RRT or structured lane graphs. Furthermore, it is the first work to combine GBP with structured lane priors for enhanced distributed coordination robustness. The method operates effectively under communication constraints and in the presence of dynamic obstacles. In simulation, it achieves a 28% reduction in path deviation for single agents and a 16% reduction in collaborative path deviation for multi-agent systems, while significantly outperforming baselines in path success rate and environmental adaptability.

Technology Category

• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Multi-agent path planning in complex environments
• Combining Gaussian belief propagation with global path finding
• Reducing path deviation in multi-agent scenarios

Innovation

• Gaussian belief propagation integration
• Novel tracking factor introduction
• Structured global planning enhancement