Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models

TL;DR

This paper introduces a novel method combining constrained optimization with diffusion models to solve multi-agent path finding in continuous spaces, ensuring collision avoidance and kinematic feasibility.

Contribution

It presents a new approach that integrates constrained optimization with diffusion models, enabling feasible multi-agent trajectory generation in continuous environments.

Findings

Successfully generates collision-free multi-agent paths in high-dimensional spaces.

Outperforms traditional methods in scalability and feasibility.

Demonstrates effectiveness across diverse simulated scenarios.

Abstract

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to goal positions. Coordinating multiple agents in a shared environment poses significant challenges, especially in continuous spaces where traditional optimization algorithms struggle with scalability. Moreover, these algorithms often depend on discretized representations of the environment, which can be impractical in image-based or high-dimensional settings. Recently, diffusion models have shown promise in single-agent path planning, capturing complex trajectory distributions and generating smooth paths that navigate continuous, high-dimensional spaces. However, directly extending diffusion models to MAPF introduces new challenges since these models struggle to ensure constraint feasibility, such as inter-agent collision avoidance. To overcome this limitation, this work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces. This unique combination directly produces feasible multi-agent trajectories that respect collision avoidance and kinematic constraints. The effectiveness of our approach is demonstrated across various challenging simulated scenarios of varying dimensionality.