• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.407-416
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• 2022

Multiple AMRs have been proved to be effective in improving warehouse productivity by eliminating workers' wasteful walking time. Although Multi-agent Path Finding (MAPF)-based solution is an optimal approach for this task, its deployment in practice is challenging mainly due to its imperfect plan-execution capabilities and insufficient computing resources for high-density environments. In this paper, we present a MAPF-based fleet management system architecture that robustly manages multiple robots by re-computing their paths whenever it is necessary. To achieve this, we defined four events that trigger our MAPF solver framework to generate new paths. These paths are then delivered to each AMR through ROS2 message topic. We also optimized a graph structure that effectively captures spatial information of the warehouse. By using this graph structure we can reduce computational burden while keeping its rescheduling functionality. With proposed MAPF-based fleet management system, we can control AMRs without collision or deadlock. We applied our fleet management system to the real logistics warehouse with 10 AMRs and observed that it works without a problem. We also present the usage statistic of adopting AMRs with proposed fleet management system to the warehouse. We show that it is useful over 25% of daily working time.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.369-372
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• 2021

Scheduling the movements of trains in the modern railway system is becoming essential and important. Swiss Federal Railway Company (SBB) and machine learning researchers began collaborating to make a simulation environment and held a Flatland challenge. In this paper, the methodologies of the winners of this competition are analyzed to achieve insight and research trends. This problem is similar to the Multi-Agent Path Finding (MAPF) and Vehicle Rescheduling Problem (VRSP). The potential of the attempted methods from the Flatland challenge to be applied to various transportation systems as well as railways is discussed.