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