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

TY - CONF

T1 - Multi-Agent Path Planning in Complex Environments using Gaussian Belief Propagation with Global Path Finding

AU - Sejersen, Jonas le Fevre

AU - Sarabakha, Andriy

AU - Plagborg Bak Sørensen, Kristoffer

AU - Høigaard Jensen, Jens

N1 - Conference code: 2025

PY - 2025/5/28

Y1 - 2025/5/28

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105016580269

U2 - 10.1109/ICRA55743.2025.11128006

DO - 10.1109/ICRA55743.2025.11128006

M3 - Paper

SP - 4359

EP - 4365

T2 - 2025 IEEE International Conference on Robotics and Automation (ICRA)

Y2 - 19 May 2025 through 23 May 2025

ER -