IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform
Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/1/19 |
| _version_ | 1797344299368579072 |
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| author | Jie Tang Ruofei Zhong Ruizhuo Zhang Yan Zhang |
| author_facet | Jie Tang Ruofei Zhong Ruizhuo Zhang Yan Zhang |
| author_sort | Jie Tang |
| collection | DOAJ |
| description | Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities. |
| first_indexed | 2024-03-08T11:00:21Z |
| format | Article |
| id | doaj.art-a20aa9b4c6f949bcb21f143deed16cae |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-08T11:00:21Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-a20aa9b4c6f949bcb21f143deed16cae2024-01-26T16:05:54ZengMDPI AGDrones2504-446X2024-01-01811910.3390/drones8010019IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing PlatformJie Tang0Ruofei Zhong1Ruizhuo Zhang2Yan Zhang3College of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, ChinaMulti-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities.https://www.mdpi.com/2504-446X/8/1/19multi-unmanned systemspayload managementcloud platformmulti-machine collaborationedge–device–cloud integrationdigital twins |
| spellingShingle | Jie Tang Ruofei Zhong Ruizhuo Zhang Yan Zhang IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform Drones multi-unmanned systems payload management cloud platform multi-machine collaboration edge–device–cloud integration digital twins |
| title | IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform |
| title_full | IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform |
| title_fullStr | IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform |
| title_full_unstemmed | IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform |
| title_short | IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform |
| title_sort | imuc edge end cloud integrated multi unmanned system payload management and computing platform |
| topic | multi-unmanned systems payload management cloud platform multi-machine collaboration edge–device–cloud integration digital twins |
| url | https://www.mdpi.com/2504-446X/8/1/19 |
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