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}.

翻译：任务分配在多机器人自主清洁系统中至关重要，多个机器人需协同完成大面积清洁工作。然而，现有研究主要聚焦于清洁机器人的确定性单一任务分配，未考虑不确定工作环境中的混合任务。此外，相关领域缺乏标准数据集与评测基准。针对上述问题，本文将不确定清洁环境下的多机器人混合任务分配建模为鲁棒优化问题。首先，提出一种新型鲁棒混合整数线性规划模型，融入实际约束条件，包括不同任务间的顺序约束及混合机器人能力约束。其次，基于楼层平面图构建包含100个实例的数据集，每个实例配有二维人工标注图像与三维模型。最后，采用三种传统优化方法与基于深度强化学习的求解器，在收集的数据集上完成全面性能评估。结果表明，所提方案满足多机器人清洁任务分配需求，且鲁棒求解器能在极低额外成本下保护系统免受最坏场景影响。该基准数据集将于{https://github.com/iamwangyabin/Multi-robot-Cleaning-Task-Allocation}公开。