# Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning

arXiv: https://arxiv.org/abs/2303.06531 · HTML: https://mlsanity.com/paper/2303.06531/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 12 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention](https://mlsanity.com/paper/1908.11025/) (2019)
- [A concise guide to existing and emerging vehicle routing problem variants](https://mlsanity.com/paper/1906.06750/) (2019)
- [A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem](https://mlsanity.com/paper/2009.00363/) (2020)
- [Multi-Robot Task Allocation -- Complexity and Approximation](https://mlsanity.com/paper/2103.12370/) (2021)

## Abstract (arXiv)

Task allocation plays a vital role in multi-robot autonomous cleaning systems, where multiple robots work together to clean a large area. However, most current studies mainly focus on deterministic, single-task allocation for cleaning robots, without considering hybrid tasks in uncertain working environments. Moreover, there is a lack of datasets and benchmarks for relevant research. In this paper, to address these problems, we formulate multi-robot hybrid-task allocation under the uncertain cleaning environment as a robust optimization problem. Firstly, we propose a novel robust mixed-integer linear programming model with practical constraints including the task order constraint for different tasks and the ability constraints of hybrid robots. Secondly, we establish a dataset of 100 instances made from floor plans, each of which has 2D manually-labeled images and a 3D model. Thirdly, we provide comprehensive results on the collected dataset using three traditional optimization approaches and a deep reinforcement learning-based solver. The evaluation results show that our solution meets the needs of multi-robot cleaning task allocation and the robust solver can protect the system from worst-case scenarios with little additional cost. The benchmark will be available at https://github.com/iamwangyabin/Multi-robot-Cleaning-Task-Allocation.