How Can 5G and Edge Computing Benefit Warehouse Automation?

The concept of Industry 4.0 is driving the popularity of private 5G networks, which are also increasingly being used in manufacturing and logistics due to lower spectrum costs. As a result, a large number of use cases in Industry 4.0 around smart manufacturing, logistics, warehouse automation, energy and utilities, smart grids, defect detection, etc. account for more than 60% of private 5G use cases.

The warehouse automation market is expected to reach $27 billion by 2025, with more than 4 million robots in operation and approximately 50,000 automated distribution warehouses. Therefore, there will be huge opportunities for autonomous mobile robots in our industry ecosystem.

5G, with its ultra-reliable low-latency communications and high-bandwidth capabilities, drives distributed computing efficiency and sets a new paradigm for autonomous mobile robots.

Edge computing is becoming more and more popular, which is a very good cycle. This will reduce the cost of autonomous mobile robots because the computing is closer to the data sources generated by autonomous mobile robots. At the same time, as warehouses plan to deploy hundreds of autonomous mobile robots, even the price of autonomous mobile robots will become more affordable.

Some tasks in an automated warehouse can be located on autonomous mobile robots, while some tasks can be offloaded to edge servers. In some cases, some tasks can be moved to data centers or the cloud.

Some of the tasks that can be performed on an autonomous mobile robot include sensor ingestion, path planning and localization, obstacle avoidance, motor control, functional safety, and navigation, while tasks that can be offloaded to edge servers include remote jamming, fleet management, mission management, battery management, traffic management, and analytics.

In order to enable this kind of compute and AI capabilities in autonomous mobile robots, they really need to be based on latency and other requirements. And then logically divide these workloads across these different locations to get the best efficiency and the best business value for the enterprise.

Specific use cases for autonomous mobile robots

The first one is Edge Insights for Autonomous Mobile Robots, a software stack optimized on an autonomous mobile robot platform with various building blocks such as simultaneous localization and mapping for truly enabling and controlling autonomous mobile robots.

The second use case is Intel’s open source suite of tools that integrates AI, computer vision, and deep learning inference. The suite accelerates visual inference from images captured by cameras on robots. This is critical for autonomous mobile robots to navigate on the factory floor, but also to ensure that autonomous mobile robots operate safely and coexist with humans on the factory floor.

The final use case is an intelligent edge product for managing and deploying autonomous mobile robotic applications.

Summarize

Warehouse automation can manage autonomous mobile robots from different suppliers, use edge computing to introduce extended AI capabilities for autonomous mobile robots, implement digital twins for predictive maintenance and operational optimization, and create a safe environment for autonomous mobile robots and humans to collaborate.