Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology
The core of the research focuses on analyzing the discharge characteristic of a lithium NMC battery in an autonomous mobile robot, which can be used as a model to predict its future states depending on the amount of missions queued. In the presented practical example, an autonomous mobile robot is u...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/19/6561 |
| _version_ | 1797478472205991936 |
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| author | Kamil Krot Grzegorz Iskierka Bartosz Poskart Arkadiusz Gola |
| author_facet | Kamil Krot Grzegorz Iskierka Bartosz Poskart Arkadiusz Gola |
| author_sort | Kamil Krot |
| collection | DOAJ |
| description | The core of the research focuses on analyzing the discharge characteristic of a lithium NMC battery in an autonomous mobile robot, which can be used as a model to predict its future states depending on the amount of missions queued. In the presented practical example, an autonomous mobile robot is used for in-house transportation, where its missions are queued or delegated to other robots in the system depending on the robots’ predicted state of charge. The system with the implemented models has been tested in three scenarios, simulating real-life use cases, and has been examined in the context of the number of missions executed in total. The main finding of the research is that the battery discharge characteristic stays consistent regardless of the mission type or length, making it usable as a model for the predictive monitoring system, which allows for detection of obstruction of the default shortest paths for the programmed missions. The model is used to aid the maintenance department with information on any anomalies detected in the robot’s path or the behavior of the battery, making the transportation process safer and more efficient by alerting the employees to take action or delegate the excessive tasks to other robots. |
| first_indexed | 2024-03-09T21:32:22Z |
| format | Article |
| id | doaj.art-5b8cfebe90ae4705aabb9abd298119e7 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T21:32:22Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-5b8cfebe90ae4705aabb9abd298119e72023-11-23T20:52:49ZengMDPI AGMaterials1996-19442022-09-011519656110.3390/ma15196561Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things TechnologyKamil Krot0Grzegorz Iskierka1Bartosz Poskart2Arkadiusz Gola3Faculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, PolandFaculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, PolandFaculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, PolandFaculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, PolandThe core of the research focuses on analyzing the discharge characteristic of a lithium NMC battery in an autonomous mobile robot, which can be used as a model to predict its future states depending on the amount of missions queued. In the presented practical example, an autonomous mobile robot is used for in-house transportation, where its missions are queued or delegated to other robots in the system depending on the robots’ predicted state of charge. The system with the implemented models has been tested in three scenarios, simulating real-life use cases, and has been examined in the context of the number of missions executed in total. The main finding of the research is that the battery discharge characteristic stays consistent regardless of the mission type or length, making it usable as a model for the predictive monitoring system, which allows for detection of obstruction of the default shortest paths for the programmed missions. The model is used to aid the maintenance department with information on any anomalies detected in the robot’s path or the behavior of the battery, making the transportation process safer and more efficient by alerting the employees to take action or delegate the excessive tasks to other robots.https://www.mdpi.com/1996-1944/15/19/6561predictive monitoringautonomous mobile robotAMRIIoTNode-REDautomation stack |
| spellingShingle | Kamil Krot Grzegorz Iskierka Bartosz Poskart Arkadiusz Gola Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology Materials predictive monitoring autonomous mobile robot AMR IIoT Node-RED automation stack |
| title | Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology |
| title_full | Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology |
| title_fullStr | Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology |
| title_full_unstemmed | Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology |
| title_short | Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology |
| title_sort | predictive monitoring system for autonomous mobile robots battery management using the industrial internet of things technology |
| topic | predictive monitoring autonomous mobile robot AMR IIoT Node-RED automation stack |
| url | https://www.mdpi.com/1996-1944/15/19/6561 |
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