We study optimizing a destination-to-chutes task mapping to improve throughput in Robotic Sorting Systems (RSS), where a team of robots sort packages on a sortation floor by transporting them from induct workstations to eject chutes based on their shipping destinations (e.g. Los Angeles or Pittsburgh). The destination-to-chutes task mapping is used to determine which chutes a robot can drop its package. Finding a high-quality task mapping is challenging because of the complexity of a real-world RSS. First, optimizing task mapping is interdependent with robot target assignment and path planning. Second, chutes will be CLOSED for a period of time once they receive sufficient packages to allow for downstream processing. Third, task mapping quality directly impacts the downstream processing, as scattered chutes for the same destination increase package handling time. In this paper, we first formally define task mappings and the problem of Task Mapping Optimization (TMO). We then present a simulator of RSS to evaluate task mappings. We then present a simple TMO method based on the Evolutionary Algorithm and Mixed Integer Linear Programming, demonstrating the advantage of our optimized task mappings over the greedily generated ones in various RSS setups with different map sizes, numbers of chutes, and destinations. Finally, we use Quality Diversity algorithms to analyze the throughput of a diverse set of task mappings. Our code is available online at https://github.com/lunjohnzhang/tmo_public.

翻译：本研究旨在优化目的地至滑槽的任务映射策略，以提升机器人分拣系统（RSS）的吞吐效率。在该系统中，机器人团队在分拣场地上将包裹从导入工作站运送至对应寄送目的地（如洛杉矶或匹兹堡）的弹出滑槽。目的地至滑槽的任务映射决定了机器人可投放包裹的滑槽集合。由于实际RSS系统的复杂性，寻找高质量的任务映射面临多重挑战：首先，任务映射优化与机器人目标分配及路径规划存在相互依赖关系；其次，滑槽在接收足够数量包裹后需暂时关闭以进行下游处理；再者，任务映射质量直接影响下游处理效率，因为同一目的地的包裹若分散在不同滑槽将增加处理时间。本文首先形式化定义了任务映射及任务映射优化问题，随后构建了RSS仿真系统以评估任务映射方案。我们提出了一种基于进化算法与混合整数线性规划的简易TMO方法，并通过不同地图尺寸、滑槽数量及目的地配置的RSS场景验证了优化任务映射相较于贪婪生成方法的优势。最后，采用质量多样性算法对多样化任务映射集合的吞吐性能进行了系统性分析。相关代码已开源：https://github.com/lunjohnzhang/tmo_public。