Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control
This paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/15/4666 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849239656458092544 |
|---|---|
| author | Daniel Poul Mtowe Lika Long Dong Min Kim |
| author_facet | Daniel Poul Mtowe Lika Long Dong Min Kim |
| author_sort | Daniel Poul Mtowe |
| collection | DOAJ |
| description | This paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing or direct sensor-to-controller communication, are inherently limited by excessive network latency, bandwidth bottlenecks, and a lack of predictive decision-making, thus constraining their effectiveness in real-time multi-agent systems. To overcome these limitations, we propose a novel framework that seamlessly integrates edge computing with digital twin (DT) technology. By performing localized preprocessing at the edge, the system extracts semantically rich features from raw sensor data streams, reducing the transmission overhead of the original data. This shift from raw data to feature-based communication significantly alleviates network congestion and enhances system responsiveness. The DT layer leverages these extracted features to maintain high-fidelity synchronization with physical robots and to execute predictive models for proactive collision avoidance. To empirically validate the framework, a real-world testbed was developed, and extensive experiments were conducted with multiple mobile robots. The results revealed a substantial reduction in collision rates when DT was deployed, and further improvements were observed with E-DTNCS integration due to significantly reduced latency. These findings confirm the system’s enhanced responsiveness and its effectiveness in handling real-time control tasks. The proposed framework demonstrates the potential of combining edge intelligence with DT-driven control in advancing the reliability, scalability, and real-time performance of multi-robot systems for industrial automation and mission-critical cyber-physical applications. |
| format | Article |
| id | doaj-art-3410e1fe611b485fbe9e1a00a9eb9708 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3410e1fe611b485fbe9e1a00a9eb97082025-08-20T04:00:54ZengMDPI AGSensors1424-82202025-07-012515466610.3390/s25154666Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote ControlDaniel Poul Mtowe0Lika Long1Dong Min Kim2Department of ICT Convergence, Graduate School, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of ICT Convergence, Graduate School, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of ICT Convergence, Graduate School, Soonchunhyang University, Asan 31538, Republic of KoreaThis paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing or direct sensor-to-controller communication, are inherently limited by excessive network latency, bandwidth bottlenecks, and a lack of predictive decision-making, thus constraining their effectiveness in real-time multi-agent systems. To overcome these limitations, we propose a novel framework that seamlessly integrates edge computing with digital twin (DT) technology. By performing localized preprocessing at the edge, the system extracts semantically rich features from raw sensor data streams, reducing the transmission overhead of the original data. This shift from raw data to feature-based communication significantly alleviates network congestion and enhances system responsiveness. The DT layer leverages these extracted features to maintain high-fidelity synchronization with physical robots and to execute predictive models for proactive collision avoidance. To empirically validate the framework, a real-world testbed was developed, and extensive experiments were conducted with multiple mobile robots. The results revealed a substantial reduction in collision rates when DT was deployed, and further improvements were observed with E-DTNCS integration due to significantly reduced latency. These findings confirm the system’s enhanced responsiveness and its effectiveness in handling real-time control tasks. The proposed framework demonstrates the potential of combining edge intelligence with DT-driven control in advancing the reliability, scalability, and real-time performance of multi-robot systems for industrial automation and mission-critical cyber-physical applications.https://www.mdpi.com/1424-8220/25/15/4666digital twinedge computinglow latencynetworked control systemcollision avoidance |
| spellingShingle | Daniel Poul Mtowe Lika Long Dong Min Kim Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control Sensors digital twin edge computing low latency networked control system collision avoidance |
| title | Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control |
| title_full | Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control |
| title_fullStr | Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control |
| title_full_unstemmed | Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control |
| title_short | Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control |
| title_sort | low latency edge enabled digital twin system for multi robot collision avoidance and remote control |
| topic | digital twin edge computing low latency networked control system collision avoidance |
| url | https://www.mdpi.com/1424-8220/25/15/4666 |
| work_keys_str_mv | AT danielpoulmtowe lowlatencyedgeenableddigitaltwinsystemformultirobotcollisionavoidanceandremotecontrol AT likalong lowlatencyedgeenableddigitaltwinsystemformultirobotcollisionavoidanceandremotecontrol AT dongminkim lowlatencyedgeenableddigitaltwinsystemformultirobotcollisionavoidanceandremotecontrol |