Task-Oriented Edge-Assisted Cooperative Data Compression, Communications and Computing for UGV-Enhanced Warehouse Logistics

TL;DR

A Deep Reinforcement Learning (DRL)-based two-stage point cloud data compression algorithm that dynamically and collaboratively adjusts compression ratios according to task requirements is developed, significantly reducing communication overhead.

Abstract

This paper explores the growing need for task-oriented communications in warehouse logistics, where traditional communication Key Performance Indicators (KPIs)-such as latency, reliability, and throughput-often do not fully meet task requirements. As the complexity of data flow management in large-scale device networks increases, there is also a pressing need for innovative cross-system designs that balance data compression, communication, and computation. To address these challenges, we propose a task-oriented, edge-assisted framework for cooperative data compression, communication, and computing in Unmanned Ground Vehicle (UGV)-enhanced warehouse logistics. In this framework, two UGVs collaborate to transport cargo, with control functions-navigation for the front UGV and following/conveyance for the rear UGV-offloaded to the edge server to accommodate their limited on-board computing resources. We develop a Deep Reinforcement Learning (DRL)-based two-stage point cloud data compression algorithm that dynamically and collaboratively adjusts compression ratios according to task requirements, significantly reducing communication overhead. System-level simulations of our UGV logistics prototype demonstrate the framework's effectiveness and its potential for swift real-world implementation.

Figures (6)

• Figure 1: Edge computing enabled autonomous driving.
• Figure 2: Integrated Feature Extraction, Reinforcement Learning, and Data Compression in a Cooperative Semantic Communication System.
• Figure 3: Prototype design in Isaac Sim (The demonstration video is available athttps://youtu.be/egEIaBiVmpA).
• Figure 4: Performance evaluation for three agent $\pi_{\theta_1}$, $\pi_{\theta_2}$ and $\pi_{\theta_3}$.
• Figure 5: Average task success rate in each training episode.