Robots have brought a massive change in the present era, and so we expect them to change the next generation. While it may not be true that the next generation of robotics will do all human work but the robotic solution help with automation and productivity improvements. Learn More!
In the past few years, we have constantly seen a steady increase and witnessed increased adoption of robots for different use-cases. When industries use robots, multiple robots do similar tasks in the same vicinity. Typically, robots consist of embedded AI Processors to ensure they make inference in real-time to prevent lags in the server.
Robots have become an integral part of production technology, manufacturing and Industrial 4.0. These robots need to be used daily. Though embedded AI makes inference faster, the high-end processors significantly increase the cost per unit of each robot. Since the processing is localized, the battery life per robot also reduces.
Since the robots perform a similar task in the same vicinity, we can intelligently use a minimum architecture for each robot, and connect to a central server to maximize the usage.
The new architecture significantly brings down the cost of each robot, and the technology is scaled commercially.
Key challenges and drivers:
- Reduced Backhaul
- Light device
How and why can we use Edge Computing?
The device's latency is a critical component for robotics applications. Any variance can hinder the performance of the robot and its usage. One may argue if edge computing can help at all. If not anything, the edge might increase the latency.
Nife has an intelligent robotics solution that enables edge computing reducing the cost of the hardware while ensuring that application performance is not at all compromised. Hence, the end-user and the enterprise does not have to compromise while they get the best of both the world.
Edge computing also helps increase the battery life of each robot since high-end local inference is removed from the robot without any hindrance in services.
The energy consumption is high for robotics applications that work on computer vision for navigation and recognizing objects. Traditionally, this data cannot be computed on a cloud; hence, an embedded AI processor makes transactions faster.
Along with that, using virtualization and deploying the same image multiple times on different robots can be avoided.
We are making the solution more attractive to the end-users and the industries by reducing cost, offloading device computation and helping with the battery life.
Solutions in Robotics are very attractive to IoT and Agriculture, Engineering and construction services, healthcare, and the manufacturing sectors.
Logistics and transportation are extensive areas for robotics, especially in shipping and usage in airports.
Robots have brought a massive change in the present era, and so they are expected to change the next generation. However, Edge computing further extends to reduce the hardware cost while ensuring that application performance is retained.
How does Nife Help?
Use Nife to offload device compute and deploy applications close to the Robots. Nife works with Computer Vision.
- Offload local computation
- No difference in application performance (70% improvement from Cloud)
- Reduce the overall price of the Robots (40% Cost Reduction)
- Manage and Monitor all applications in a single pane of glass.
- Seamlessly deploy and manage navigation functionality (5 min to deploy, 3 min to scale)
A real-life example of the edge deployment and the results
In this customer scenario, the robots were picking up the packages and moving them to another location.
If you would like to learn more about the solution, do reach out to us!
Check out our previous case study : https://blogs.nife.io/case-study-1-scaling-up-deployment-of-ar-mirrors-ckt8cr32p52881npfdlz5iotw/