Autonomous transport systems for ships, enabled by edge cloud and machine learning. Will that really work and be able to minimize energy consumption as well as increase safety and reduce docking time? Project: Distributed cloud environment for Autonomous Ships.
The project is part of a national Finnish research and innovation program called Design4Value (D4V). Ericsson participates together with other partners in the DIMECC ecosystem.
In the project we focus on autonomous ships that sail independently between multiple harbors. The autonomous ships take advantage of edge computing in order to collect sensor data, fuse data from sensors, utilize control algorithms and machine learning, among many other things. Since the ships use edge computation in processing, we deploy edge clouds for each ship.
A distributed cloud is composed of multiple inter-connected clouds, such as central, regional and edge clouds. The last one, edge cloud, usually has the least amount of processing and other resources but has the lowest latency due to its close proximity to the physical assets. In the case of an autonomous ship, the edge cloud is located aboard the ship itself and, thus, its resources are always available even if the ship loses all connectivity at open sea. When the ship approaches a harbor, it can connect via cellular networks and harness resources from a regional or central cloud for more demanding operations, such as machine learning. In the same way, the ship is disconnected from the cloud environment when it sails outside of the harbor area and then it relies solely on edge computing.
Click here to read the complete article on Ericsson’s website.
Image: A simplified overview how an autonomous ship sails between two harbours.
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