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

TL;DR

This paper introduces a task-oriented, edge-assisted framework for cooperative data compression, communication, and computing in UGV-enhanced warehouse logistics, utilizing deep reinforcement learning to optimize data handling and reduce communication overhead.

Contribution

It presents a novel framework integrating edge computing and DRL-based data compression for UGV logistics, addressing complex data management challenges.

Findings

Significant reduction in communication overhead through DRL-based compression.

Effective collaboration between UGVs for cargo transport and data processing.

System simulations demonstrate promising real-world applicability.

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.