# Online On-Demand Multi-Robot Coverage Path Planning

## TL;DR

This paper introduces an efficient online centralized multi-robot coverage path planning algorithm that scales to hundreds of robots in large, complex, unknown workspaces, outperforming existing methods in speed and scalability.

## Contribution

It proposes a horizon-based, on-demand path planning algorithm that reuses previously computed paths, reducing computation and enabling large-scale multi-robot coverage.

## Key findings

- Scales to hundreds of robots in large workspaces
- Outperforms state-of-the-art algorithms in coverage time
- Validated through simulations and real-world experiments

## Abstract

We present an online centralized path planning algorithm to cover a large, complex, unknown workspace with multiple homogeneous mobile robots. Our algorithm is horizon-based, synchronous, and on-demand. The recently proposed horizon-based synchronous algorithms compute all the robots' paths in each horizon, significantly increasing the computation burden in large workspaces with many robots. As a remedy, we propose an algorithm that computes the paths for a subset of robots that have traversed previously computed paths entirely (thus on-demand) and reuses the remaining paths for the other robots. We formally prove that the algorithm guarantees complete coverage of the unknown workspace. Experimental results on several standard benchmark workspaces show that our algorithm scales to hundreds of robots in large complex workspaces and consistently beats a state-of-the-art online centralized multi-robot coverage path planning algorithm in terms of the time needed to achieve complete coverage. For its validation, we perform ROS+Gazebo simulations in five 2D grid benchmark workspaces with 10 Quadcopters and 10 TurtleBots, respectively. Also, to demonstrate its practical feasibility, we conduct one indoor experiment with two real TurtleBot2 robots and one outdoor experiment with three real Quadcopters.