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.
| Original language | English |
|---|---|
| Pages (from-to) | 1 |
| Number of pages | 1 |
| Journal | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS |
| Volume | 23 |
| Issue number | 12 |
| DOIs | |
| Publication status | Accepted/In press - 2024 |
Keywords
- AoI
- Autonomous aerial vehicles
- C&C data
- DRL
- Goal-oriented semantic communications
- Measurement
- proactive repetition scheme
- Real-time systems
- robotic control
- Semantics
- Task analysis
- Trajectory
- VoI
- Wireless communication