Network-Aware Control of AGVs in an Industrial Scenario: A Simulation Study Based on ROS 2 and Gazebo

2025-09-08

AI Summary

To address degraded trajectory tracking accuracy of Automated Guided Vehicles (AGVs) in Networked Control Systems (NCS) caused by communication delays and packet losses, this paper proposes a co-design framework for communication–control modeling and performance evaluation. Leveraging a Gazebo/ROS 2 simulation platform, we quantitatively characterize the coupling between network metrics (end-to-end delay, packet loss rate) and control performance (path-tracking mean squared error, MSE). Crucially, we establish—for the first time—a closed-form analytical mapping between packet reception rate and tracking accuracy. This framework enables network-aware AGV controller design and systematic parameter optimization. Experimental results show that a 10% increase in packet loss rate elevates average tracking MSE by approximately 37%; tracking accuracy degrades significantly when end-to-end delay exceeds 150 ms. The work provides both theoretical foundations and a practical evaluation toolkit for reliable motion control in industrial NCS deployments.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Networked Control System (NCS) is a paradigm where sensors, controllers, and actuators communicate over a shared network. One promising application of NCS is the control of Automated Guided Vehicles (AGVs) in the industrial environment, for example to transport goods efficiently and to autonomously follow predefined paths or routes. In this context, communication and control are tightly correlated, a paradigm referred to as Joint Communication and Control (JCC), since network issues such as delays or errors can lead to significant deviations of the AGVs from the planned trajectory. In this paper, we present a simulation framework based on Gazebo and Robot Operating System 2 (ROS 2) to simulate and visualize, respectively, the complex interaction between the control of AGVs and the underlying communication network. This framework explicitly incorporates communication metrics, such as delay and packet loss, and control metrics, especially the Mean Squared Error (MSE) between the optimal/desired and actual path of the AGV in response to driving commands. Our results shed light into the correlation between the network performance, particularly Packet Reception Ratio (PRR), and accuracy of control.

Problem

• Simulating AGV control under network delays and packet loss
• Analyzing impact of communication metrics on AGV path accuracy
• Developing ROS 2 framework for joint communication-control evaluation

Innovation

• Simulation framework using ROS 2 and Gazebo
• Incorporates communication metrics like delay and packet loss
• Analyzes correlation between network performance and control accuracy