多机器人协同中的目的地到传送口任务映射优化
Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems
AI总结：
本文提出基于进化算法和混合整数线性规划的任务映射优化方法，用于提升多机器人分拣系统的吞吐量。
摘要：
我们研究了优化目的地到传送口的任务映射以提高分拣系统（RSS）的吞吐量，其中一组机器人通过将包裹从导入工作站运输到出口传送口来分拣包裹，根据其目的地（如洛杉矶或匹兹堡）。目的地到传送口的任务映射用于确定机器人可以将包裹丢入哪个传送口。找到高质量的任务映射具有挑战性，因为现实世界的RSS复杂性很高。首先，优化任务映射与机器人目标分配和路径规划相互依赖。其次，一旦传送口接收足够多的包裹，就会关闭一段时间以允许下游处理。第三，任务映射的质量直接影响下游处理，因为相同目的地的分散传送口会增加包裹处理时间。在本文中，我们首先正式定义任务映射和任务映射优化（TMO）问题。然后我们提出一个RSS模拟器来评估任务映射。然后我们提出了一种基于进化算法和混合整数线性规划的简单TMO方法，展示了我们优化的任务映射在各种RSS设置中（不同地图大小、传送口数量和目的地数量）相对于贪心生成的任务映射的优势。最后，我们使用质量多样性算法分析了一组多样任务映射的吞吐量。我们的代码可在https://github.com/lunjohnzhang/tmo_public上获得。
英文摘要：
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.
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