Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search

TL;DR

This paper introduces MASS, a three-level framework combining MAPF and stationary state search to generate realistic, kinodynamically-feasible plans for differential drive robots, significantly improving throughput in multi-agent scenarios.

Contribution

The paper presents a novel MASS framework that integrates MAPF with stationary state search for realistic multi-agent motion planning of differential drive robots.

Findings

Up to 400% throughput improvement over existing methods.

Effective in grid map and warehouse domains.

Addresses lifelong multi-agent motion planning challenges.

Abstract

Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, differential drive robots are commonly used. These robots have a kinodynamic model that allows only in-place rotation and movement along their current orientation, subject to speed and acceleration limits. However, existing Multi-Agent Path Finding (MAPF)-based methods often use simplified models for robot kinodynamics, which limits their practicality and realism. In this paper, we introduce a three-level framework called MASS to address these challenges. MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans. We further extend MASS using an adaptive window mechanism to address the lifelong MAMP problem. Empirically, we tested our methods on the single-shot grid map domain and the lifelong warehouse domain. Our method shows up to 400% improvements in terms of throughput compared to existing methods.