Shutong Chen;Emmanouil Spyrakos-Papastavridis;Yichao Jin;Yansha Deng
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Unlike the traditional reconstruction framework that periodically transmits a reconstruction message for real-time DT reconstruction, our framework implements a feature selection (FS) algorithm to extract the semantic information from the reconstruction message, and a deep reinforcement learning-based temporal selection algorithm to selectively transmit the semantic information over time. We validate our proposed GSC framework through both Pybullet simulations and lab experiments based on the Franka Research 3 robot arm. For a range of distinct robotic tasks, simulation results show that our framework can reduce the communication load by at least 59.5% under strict reconstruction error constraints and 80% under relaxed reconstruction error constraints, compared with traditional communication framework. Also, experimental results confirm the effectiveness of our framework, where the communication load is reduced by 53% in strict constraint case and 74% in relaxed constraint case. 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引用次数: 0
摘要
作为工业中最有前途的技术之一,数字孪生(DT)通过精确重建物理实体的虚拟副本,促进了对现实世界系统的实时监控和预测分析。然而,由于通信开销的不断增加,特别是对于数字机械臂的重建,这种重建面临着前所未有的挑战。为此,我们提出了一种新的面向目标的语义通信(GSC)框架,用于提取DT中机器人手臂重构任务的GSC信息,以期在严格和宽松的重构误差约束下最小化通信负荷。与传统的重建框架周期性地传输重建消息进行实时DT重建不同,我们的框架实现了特征选择(FS)算法来从重建消息中提取语义信息,并实现了基于深度强化学习的时间选择算法来选择性地随时间传输语义信息。我们通过Pybullet模拟和基于Franka Research 3机械臂的实验室实验验证了我们提出的GSC框架。仿真结果表明,与传统通信框架相比,该框架在严格重构误差约束下的通信负载至少减少59.5%,在宽松重构误差约束下的通信负载至少减少80%。实验结果也证实了该框架的有效性,在严格约束情况下,通信负载减少53%,在宽松约束情况下,通信负载减少74%。该演示可在https://youtu.be/2OdeHKxcgnk上获得
Goal-Oriented Semantic Communication for Robot Arm Reconstruction in Digital Twin: Feature and Temporal Selections
As one of the most promising technologies in industry, the Digital Twin (DT) facilitates real-time monitoring and predictive analysis for real-world systems by precisely reconstructing virtual replicas of physical entities. However, this reconstruction faces unprecedented challenges due to the ever-increasing communication overhead, especially for digital robot arm reconstruction. To this end, we propose a novel goal-oriented semantic communication (GSC) framework to extract the GSC information for the robot arm reconstruction task in the DT, with the aim of minimising the communication load under the strict and relaxed reconstruction error constraints. Unlike the traditional reconstruction framework that periodically transmits a reconstruction message for real-time DT reconstruction, our framework implements a feature selection (FS) algorithm to extract the semantic information from the reconstruction message, and a deep reinforcement learning-based temporal selection algorithm to selectively transmit the semantic information over time. We validate our proposed GSC framework through both Pybullet simulations and lab experiments based on the Franka Research 3 robot arm. For a range of distinct robotic tasks, simulation results show that our framework can reduce the communication load by at least 59.5% under strict reconstruction error constraints and 80% under relaxed reconstruction error constraints, compared with traditional communication framework. Also, experimental results confirm the effectiveness of our framework, where the communication load is reduced by 53% in strict constraint case and 74% in relaxed constraint case. The demo is available at: https://youtu.be/2OdeHKxcgnk