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arXiv · 2303.06531

Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning

Yabin Wang·Xiaopeng Hong·Zhiheng Ma·Tiedong Ma·Baoxing Qin·Zhou Su
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Abstract

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 \emph{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}.

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Yabin Wang, Xiaopeng Hong, Zhiheng Ma, Tiedong Ma, Baoxing Qin, Zhou Su. 2023-03-12. Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning. https://arxiv.org/abs/2303.06531

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