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

TL;DR

This paper introduces a novel multi-agent path planning method combining Gaussian belief propagation with a tracking factor, significantly improving path adherence and coordination in complex environments through simulation validation.

Contribution

It presents a new approach integrating Gaussian belief propagation with a tracking factor and global path planning, enhancing multi-agent navigation accuracy and coordination.

Findings

Tracking factor reduces path deviation by 28% in single-agent scenarios.

Multi-agent coordination improves with structured global planning.

Method outperforms existing approaches in complex navigation tasks.

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.