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Goal-oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approach

Wenchao Wu, Yuanqing Yang, Yansha Deng, A. Hamid Aghvami

TL;DR

This work proposes goal-oriented semantic communication in robotic control (GSRC) to exploit the context of data and its importance in achieving the task at both transmitter and receiver and achieves a 91.5% improvement in the mean square error compared to the traditional unmanned aerial vehicle control framework.

Abstract

The ultra-reliable and low-latency communication (URLLC) service of the fifth-generation (5G) mobile communication network struggles to support safe robot operation. Nowadays, the sixth-generation (6G) mobile communication network is proposed to provide hyper-reliable and low-latency communication to enable safer control for robots. However, current 5G/ 6G research mainly focused on improving communication performance, while the robotics community mostly assumed communication to be ideal. To jointly consider communication and robotic control with a focus on the specific robotic task, we propose goal-oriented semantic communication in robotic control (GSRC) to exploit the context of data and its importance in achieving the task at both transmitter and receiver. At the transmitter, we propose a deep reinforcement learning algorithm to generate optimal control and command (C&C) data and a proactive repetition scheme (DeepPro) to increase the successful transmission probability. At the receiver, we design the value of information (VoI) and age of information (AoI) based queue ordering mechanism (VA-QOM) to rank the queue based on the semantic information extracted from AoI and VoI. The simulation results validate that our proposed GSRC framework achieves a 91.5% improvement in the mean square error compared to the traditional unmanned aerial vehicle control framework.

Goal-oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approach

TL;DR

This work proposes goal-oriented semantic communication in robotic control (GSRC) to exploit the context of data and its importance in achieving the task at both transmitter and receiver and achieves a 91.5% improvement in the mean square error compared to the traditional unmanned aerial vehicle control framework.

Abstract

The ultra-reliable and low-latency communication (URLLC) service of the fifth-generation (5G) mobile communication network struggles to support safe robot operation. Nowadays, the sixth-generation (6G) mobile communication network is proposed to provide hyper-reliable and low-latency communication to enable safer control for robots. However, current 5G/ 6G research mainly focused on improving communication performance, while the robotics community mostly assumed communication to be ideal. To jointly consider communication and robotic control with a focus on the specific robotic task, we propose goal-oriented semantic communication in robotic control (GSRC) to exploit the context of data and its importance in achieving the task at both transmitter and receiver. At the transmitter, we propose a deep reinforcement learning algorithm to generate optimal control and command (C&C) data and a proactive repetition scheme (DeepPro) to increase the successful transmission probability. At the receiver, we design the value of information (VoI) and age of information (AoI) based queue ordering mechanism (VA-QOM) to rank the queue based on the semantic information extracted from AoI and VoI. The simulation results validate that our proposed GSRC framework achieves a 91.5% improvement in the mean square error compared to the traditional unmanned aerial vehicle control framework.
Paper Structure (22 sections, 23 equations, 11 figures, 4 algorithms)

This paper contains 22 sections, 23 equations, 11 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem formulation
  3. Real-time UAV Waypoint Transmission Task
  4. Channel Model
  5. The General Communication and Control Process
  6. C&C Data Transmission
  7. Queue
  8. The Overall Process
  9. Problem Formulation and Performance Metric
  10. Traditional UAV Control Framework
  11. The Proposed GSRC Framework
  12. DeepPro at the BS
  13. DRL-based C&C Data Generator
  14. Proactive Repetition Scheme
  15. DeepPro
  16. ...and 7 more sections

Figures (11)

  • Figure 1: User case.
  • Figure 2: Traditional UAV control framework
  • Figure 3: Exemplar timeline in the traditional UAV control framework.
  • Figure 4: The proposed GSRC framework
  • Figure 5: An example of the timeline of UAV and BS
  • ...and 6 more figures