# Improved Discrete RRT for Coordinated Multi-robot Planning

## TL;DR

This paper introduces an improved discrete RRT algorithm for multi-robot planning that is faster and more practical, capable of solving large-scale coordination problems in seconds, with near-optimal solutions.

## Contribution

The paper presents a novel probabilistic multi-robot RRT approach that enhances speed and applicability for discrete environments, outperforming existing methods in certain large-scale scenarios.

## Key findings

- Solves multi-robot coordination problems with tens of robots in seconds.
- Produces solutions slightly worse than state-of-the-art but succeeds where others fail.
- Improves practicality and speed of multi-robot trajectory planning algorithms.

## Abstract

This paper addresses the problem of coordination of a fleet of mobile robots - the problem of finding an optimal set of collision-free trajectories for individual robots in the fleet. Many approaches have been introduced during the last decades, but a minority of them is practically applicable, i.e. fast, producing near-optimal solutions, and complete. We propose a novel probabilistic approach based on the Rapidly Exploring Random Tree algorithm (RRT) by significantly improving its multi-robot variant for discrete environments. The presented experimental results show that the proposed approach is fast enough to solve problems with tens of robots in seconds. Although the solutions generated by the approach are slightly worse than one of the best state-of-the-art algorithms presented in (ter Mors et al., 2010), it solves problems where ter Mors's algorithm fails.