Safe Interval RRT* for Scalable Multi-Robot Path Planning in Continuous Space

TL;DR

This paper introduces Safe Interval RRT*, a scalable sampling-based planner for multi-robot path planning in continuous space, combining a low-level collision-free trajectory method with high-level conflict resolution techniques.

Contribution

The paper presents a novel two-level approach integrating SI-RRT* with conflict resolution methods, enhancing scalability and solution quality in continuous multi-robot path planning.

Findings

SI-RRT* finds high-quality solutions quickly with few samples.

SI-CPP improves scalability of multi-robot planning.

SI-CCBS yields higher-quality solutions than existing methods.

Abstract

In this paper, we consider the problem of Multi-Robot Path Planning (MRPP) in continuous space. The difficulty of the problem arises from the extremely large search space caused by the combinatorial nature of the problem and the continuous state space. We propose a two-level approach where the low level is a sampling-based planner Safe Interval RRT* (SI-RRT*) that finds a collision-free trajectory for individual robots. The high level can use any method that can resolve inter-robot conflicts where we employ two representative methods that are Prioritized Planning (SI-CPP) and Conflict Based Search (SI-CCBS). Experimental results show that SI-RRT* can quickly find a high-quality solution with a few samples. SI-CPP exhibits improved scalability while SI-CCBS produces higher-quality solutions compared to the state-of-the-art planners for continuous space.