Energy-aware Joint Orchestration of 5G and Robots: Experimental Testbed and Field Validation

TL;DR

This paper tackles energy efficiency for outdoor, 5G‑connected robots by proposing OROS, a joint orchestration framework that couples robot operations with 5G network management. It implements a cloud‑native, robot‑as‑sensor aware platform consisting of a Robot Orchestrator, a 5G Orchestrator, and an online MILP optimizer with a planning window $W$ to coordinate navigation and edge offloading. Field tests with two Kobuki robots on a private 5G campus demonstrate that OROS can achieve about $15\%$ energy savings on robots and roughly $70\%$ lower CPU load, enabling longer operation times. The work highlights the practical viability of energy‑aware, cross‑domain orchestration for 5G‑enabled robotic deployments and outlines scalability strategies for larger multi‑robot scenarios. These results suggest meaningful improvements for real‑world mobile robotics in energy‑constrained environments.

Abstract

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of OROS, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed OROS in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that OROS significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately 15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.

Figures (15)

• Figure 1: Overview of the architectural building blocks.
• Figure 2: OROS Optimization model structure.
• Figure 3: OROS algorithm.
• Figure 4: Proof-of-Concept Implementation.
• Figure 5: Virtual environment with random obstacle positions.