Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning
Yabin Wang, Xiaopeng Hong, Zhiheng Ma, Tiedong Ma, Baoxing Qin, Zhou, Su

TL;DR
This paper introduces a robust optimization framework for multi-robot hybrid cleaning task allocation in uncertain environments, supported by a new dataset and evaluated with traditional and deep learning methods.
Contribution
It formulates a novel robust mixed-integer linear programming model for hybrid tasks, creates a comprehensive dataset, and evaluates multiple optimization approaches including deep reinforcement learning.
Findings
The robust solver effectively handles worst-case scenarios.
The dataset enables benchmarking of task allocation methods.
Deep reinforcement learning shows promising results.
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.…
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Taxonomy
TopicsRobot Manipulation and Learning · Advanced Manufacturing and Logistics Optimization · Robotic Path Planning Algorithms
