Sensing-Communication-Computing-Control Closed-Loop Optimization for 6G Unmanned Robotic Systems

TL;DR

This paper investigates an unmanned robotic system comprising a multi-functional unmanned aerial vehicle (UAV), sensors, and actuators, and proposes a goal-oriented closed-loop optimization scheme that achieves a two-tier task-level balance within and across loops.

Abstract

Rapid advancements in field robots have brought a new kind of cyber physical system (CPS)--unmanned robotic system--under the spotlight. In the upcoming sixth-generation (6G) era, these systems hold great potential to replace humans in hazardous tasks. This paper investigates an unmanned robotic system comprising a multi-functional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that transfers and processes information. During the task execution, the EIH gathers sensing data, calculates control commands, and transmits commands to actuators--leading to reflex-arc-like sensing-communication-computing-control ($\mathbf{SC}^3$) loops. Unlike existing studies that design $\mathbf{SC}^3$ loop components separately, we take each $\mathbf{SC}^3$ loop as an integrated structure and propose a goal-oriented closed-loop optimization scheme. This scheme jointly optimizes uplink and downlink (UL&DL) communication and computing within and across the $\mathbf{SC}^3$ loops to minimize the total linear quadratic regulator (LQR) cost. We derive optimal closed-form solutions for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency availability, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate that the proposed scheme achieves a two-tier task-level balance within and across $\mathbf{SC}^3$ loops.

Figures (10)

• Figure 1: Comparisons between the reflex arc and the $\mathbf{SC}^3$ loop. The reflex arc consists of five parts: receptor, afferent nerve, nerve center, efferent nerve, and effector. By analogy, the $\mathbf{SC}^3$ loop also consists of five parts: sensor, uplink, EIH, downlink, and actuator. The similarity of these two structures motivates us to take the $\mathbf{SC}^3$ loop as an integrated structure and devise the unmanned robotic system from a structured lens.
• Figure 2: Illustration of the unmanned robotic system. The system comprises a multi-functional UAV and $K$ pairs of sensors and actuators, which synergistically form $K$$\mathbf{SC}^3$ loops. The UAV carries communication and computing modules, acting as an EIH that transfers and processes information. A digital twin is integrated within it to simulate the physical process in real time. In this figure, the objects marked in black font represent the physical world and the objects marked in red font represent the digital twin.
• Figure 3: The closed-loop SE varying with the task-level SEs of UL&DL. The left subfigure illustrates the case of imbalanced task-level SEs between UL&DL, and the right figure illustrates the balanced case. The red arrow and black arrow indicate the steepest and slowest directions to improve the closed-loop SE.
• Figure 4: The task-related information and LQR cost under three UL&DL configuration schemes.
• Figure 5: The required communication bandwidth to exchange $1$ MHz computing CPU frequency varies with ($B$,$f$,$\frac{r^{\text{comm}}}{r^{\text{comp}}}$).

Theorems & Definitions (3)

• Lemma 1
• Theorem 1
• Theorem 2