• Title/Summary/Keyword: FMS (Fleet Management System)

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Implementation of MAPF-based Fleet Management System (다중에이전트 경로탐색(MAPF) 기반의 실내배송로봇 군집제어 구현)

  • Shin, Dongcheol;Moon, Hyeongil;Kang, Sungkyu;Lee, Seungwon;Yang, Hyunseok;Park, Chanwook;Nam, Moonsik;Jung, Kilsu;Kim, Youngjae
    • 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.

OPTIMAL ROUTE DETERMINATION TECHNOLOGY BASED ON TRAJECTORY QUERYING MOVING OBJECT DATABASE

  • Min Kyoung-Wook;Kim Ju-Wan;Park Jong-Hyun
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.317-320
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    • 2005
  • The LBS (Location-Based Services) are valuable information services combined the location of moving object with various contents such as map, POI (point of Interest), route and so on. The must general service of LBS is route determination service and its applicable parts are FMS (Fleet Management System), travel advisory system and mobile navigation system. The core function of route determination service is determination of optimal route from source to destination in various environments. The MODB (Moving Object Database) system, core part of LBS composition systems, is able to manage current or past location information of moving object and massive trajectory information stored in MODB is value-added data in CRM, ERP and data mining part. Also this past trajectory information can be helpful to determine optimal route. In this paper, we suggest methods to determine optimal route by querying past trajectory information in MODB system and verify the effectiveness of suggested method.

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Validation of Cloud Robotics System in 5G MEC for Remote Execution of Robot Engines (5G MEC 기반 로봇 엔진 원격 구동을 위한 클라우드 로보틱스 시스템 구성 및 실증)

  • Gu, Sewan;Kang, Sungkyu;Jeong, Wonhong;Moon, Hyungil;Yang, Hyunseok;Kim, Youngjae
    • The Journal of Korea Robotics Society
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    • v.17 no.2
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    • pp.118-123
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    • 2022
  • We implemented a real-time cloud robotics application by offloading robot navigation engine over to 5G Mobile Edge Computing (MEC) sever. We also ran a fleet management system (FMS) in the server and controlled the movements of multiple robots at the same time. The mobile robots under the test were connected to the server through 5G SA network. Public 5G network, which is already commercialized, has been temporarily modified to support this validation by the network operator. Robot engines are containerized based on micro-service architecture and have been deployed using Kubernetes - a container orchestration tool. We successfully demonstrated that mobile robots are able to avoid obstacles in real-time when the engines are remotely running in 5G MEC server. Test results are compared with 5G Public Cloud and 4G (LTE) Public Cloud as well.