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 increasingly being used in manufacturing and logistics due to lower spectrum costs. Therefore, 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.

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

With its ultra-reliable low-latency communication and high-bandwidth capabilities, 5G 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 calculations are closer to the data sources generated by autonomous mobile robots. Meanwhile, even autonomous mobile robots are becoming more affordable as warehouses plan to deploy hundreds of them.

Some tasks of automated warehouses 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 autonomous mobile robots 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, Task management, battery management, traffic management and analytics.

In order to enable such computing and artificial intelligence capabilities in autonomous mobile robots, they do need to be based on latency and other requirements. These workloads are then logically partitioned across these different locations to deliver optimal efficiency and best business value to the enterprise.

Specific use cases for autonomous mobile robots

The first one is for edge insights for autonomous mobile robots, a software stack optimized on the autonomous mobile robot platform with various building blocks such as simultaneous positioning and Surveying and mapping for truly realizing and controlling autonomous mobile robots.

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

The final use case is intelligent edge products for managing and deploying autonomous mobile robot applications.

Summary

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

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