摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的详细数据已经公布。据新华社记者从深圳发来的消息报道，研究人员称：“(MTSR)型自主移动机器人可以携带多个产品包，从不同的货架层进行存储和回收，并将其运送到一个工作站，在这里挑选产品来完成客户订单。在每次机器人旅行中，"必须存储上一次行程中检索到的tot。"本研究的资助者包括国家自然科学基金(NSFC)、深圳市科技计划。新闻记者引用清华大学的一篇研究文章:“这导致了一种混合存储和检索路径。我们分析了这种混合存储和检索路径问题,并用分层图算法导出了多库仓库的最优路径。”摘要:在存储优先-检索秒策略和混合存储和检索策略的基础上,提出了一种基于最短路径的高效路由策略——最近检索(CR)序列策略.数值结果表明,该策略比著名的S形策略具有更短的访问时间.而最优混合存储检索策略在实际场景中与最优混合存储检索策略的差距较小。本文利用(SOQN)半开放式排队网络对系统的随机行为进行建模,该模型可以准确地估计平均运输时间和系统运输能力与系统中机器人数量的函数关系,并利用SOQN和相应的封闭排队网络模型优化全年总成本与仓库形状、仓库数量、库存数量和库存数量的函数关系。"在给定的平均手提通过时间和吞吐量下,在机器人上的手提缓冲位置."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news originating from Shenzhen, People's Republic of China , by NewsRx correspondents, research stated, "Multi -tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retr ieve them from different shelf rack tiers, and transport them to a workstation w here the products are picked to fulfill customer orders. In each robot trip, tot es retrieved during the previous trip must be stored." Funders for this research include National Natural Science Foundation of China ( NSFC), Shenzhen Science and Technology Program. Our news journalists obtained a quote from the research from Tsinghua University , "This leads to a mixed storage and retrieval route. We analyze this mixed stor age and retrieval route problem and derive the optimal travel route for a multib lock warehouse by a layered graph algorithm, based on storage first -retrieval s econd and mixed storage and retrieval policies. We also propose an effective heu ristic routing policy, the closest retrieval (CR) sequence policy, based on a lo cal shortest path. Numerical results show that the CR policy leads to shorter tr avel times than the well-known S -shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on th e CR policy, we model the stochastic behavior of the system using a semiopen que uing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to opti mize the total annual cost as a function of the warehouse shape, the number of r obots, and tote buffer positions on the robots for a given average tote throughp ut time and throughput capacity."
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