Task allocation plays a vital role in multi-robot autonomous cleaning systems, where multiple robots work together to clean a large area. However, there are several problems in relevant research to date. 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, we contribute to multi-robot hybrid-task allocation for uncertain autonomous cleaning systems by addressing these problems. First, we model the uncertainties in the cleaning environment via robust optimization and propose a novel robust mixed-integer linear programming model with practical constraints including hybrid cleaning task order and robot's ability. Second, we establish a dataset of 100 instances made from floor plans, each of which has 2D manually-labeled images and a 3D model. Third, 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 formulation meets the needs of multi-robot cleaning task allocation and the robust solver can protect the system from the worst cases with little additional cost. The benchmark will be available at {https://github.com/iamwangyabin/Multi-robot-Cleaning-Task-Allocation}.

翻译：任务分配在多机器人自主清洁系统中至关重要，多台机器人协同工作以覆盖大面积区域。然而，现有研究仍存在若干问题。当前研究主要聚焦于清洁机器人的确定性单任务分配，未考虑不确定工作环境下的混合任务。此外，相关研究缺乏数据集与基准。本文通过解决上述问题，为不确定自主清洁系统的多机器人混合任务分配做出贡献。首先，我们通过鲁棒优化对清洁环境的不确定性进行建模，并提出一种新颖的鲁棒混合整数线性规划模型，该模型包含清洁任务顺序与机器人能力等实际约束。其次，我们基于楼层平面图构建了包含100个实例的数据集，每个实例均配备二维人工标注图像与三维模型。第三，我们采用三种传统优化方法与一种基于深度强化学习的求解器，在收集的数据集上提供了全面的结果分析。评估结果表明，我们的建模方案能满足多机器人清洁任务分配需求，且鲁棒求解器能以极小的额外成本保护系统免受最坏情况影响。该基准可在 {https://github.com/iamwangyabin/Multi-robot-Cleaning-Task-Allocation} 获取。