首页|New Robotics Findings Has Been Reported by Investigators at Tsinghua University (Performance Analysis of Multi-tote Storage and Retrieval Autonomous Mobile Robo t Systems)
New Robotics Findings Has Been Reported by Investigators at Tsinghua University (Performance Analysis of Multi-tote Storage and Retrieval Autonomous Mobile Robo t Systems)
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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."
ShenzhenPeople's Republic of ChinaAs iaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsTsingh ua University
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