Grab and Go Edge-AI for Industry 4.0

First published in Industrial Automation India, Aug 2020 issue. View article here.

In the rapidly changing industrial robotics landscape, machine intelligence is playing an increasing role. Companies are venturing into advanced software-defined solutions to improve the capability and flexibility of their automation to suit a variety of tasks, thereby increasing resource utilization. In other domains, the same power of software is leveraged in computer vision systems in order to accommodate greater variability in workpieces that are handled by industrial robots. Robots and automation cells also strive to attain greater flexibility in inspection, pick and place, and machine tending tasks using complex algorithms that are tailor- made to the specific project or application.

A few shining examples of such systems in India include the Danaher Motion Control platform for AGVs used by Bajaj Auto; the Intelligent Plant framework employed by Godrej and Welspun, which enables tracking of machinery and productivity on the floor in real-time; and the Manjushree Technopack Ltd, Bidadi (Bangalore) plant, which leverages multiple packaging machines, which are connected to a central network that assimilates metrics and KPIs that can be used for tracking maintenance issues.

The problem with Industrial AI #

Despite automation and interconnected systems being no strangers to shop floors, much of these systems still fail to employ state-of-the-art advances in robotics, computer vision and machine learning that continue to develop at a fast pace in western nations. The AGVs in Bajaj do not perform advanced path planning and perception to manoeuver around obstacles, but rather, stop in the case an obstacle crosses their way. The intelligent plant framework does not provide active inputs to the machines on the line based on data they analyse. Networked robots in assembly lines do not learn over time what movements lead to increased maintenance issues. The intelligent task of triaging the issues and implementing optimizations, often falls to the human, who uses the data acquired using these platforms to make informed decisions. Can the feedback loop be completed without human intervention? The growing consensus in the artificial intelligence community suggests that given sufficient data neural networks can perform this task on par, or even better than humans.

Take the case of computer vision in the robot above. The robot's task is to detect bolt locations on the engine cover and tighten them. The current vision system relies on an operator manually adjusting the threshold settings and blob sizes using trial and error in order to pattern match to find the location of the bolts in the captured image. Can this be eliminated using an AI approach? Self-driving vehicle software such as the Tesla Autopilot does these countless times a day while detecting road signs and traffic lights. These systems can provide greater tolerance to variation, because the networks are trained directly on data, and not humanly codified into thresholds, colors and blob size values. This disparity between industrial automation systems and cutting- edge AI highlights the gap between industrial automation in India and the state-of-the-art software advances made around the world.

Proposed solutions #

One proposed solution for industries is to employ a team of software engineers to further the data-driven approach towards manufacturing, in their assembly lines. These engineers would custom-build AI solutions to plant problems, such as monitoring vibration and motor current signals on turbines in order to alert for predictive maintenance. General Electric, for example, uses a custom-built software they developed in-house for predictive maintenance of jet turbines. AI based predictive maintenance systems can save millions of dollars in plant downtime by scheduling maintenance even before the faults can occur.

Despite the obvious advantages of custom-built AI to drive decisions and optimisations in the automation sector, it isn’t economically or practically feasible for every factory in the country to hire top AI experts, to man their software department. This is due to the shortage of skilled experts in AI and the high cost of retaining highly skilled manpower. This leads us to the question, can such AI solutions to industrial automation problems be generalised? Fortunately, due to:

1. Similarities in the nature of problems faced across the automation industry;
2. Increased access to high-quality data from monitoring devices; and
3. The high flexibility of AI solutions to train on diverse inputs, there exists a golden opportunity to leverage generalized machine learning solutions to solve industrial automation challenges.

These Grab-and-Go solutions as one may call it, would be a game-changer in the automation sector. Imagine being able to spot a computer vision problem, such as inspection, or object detection and going to a vendor, to buy a neural network, train it on gathered data, test and then directly deploy the network to the assembly line robot/automation cell, in the same manner as you would install a sensor or a reed switch! All that without having to write a single line of code! In another factory, the automation team simply connects sensor data to a streaming platform where an AI model watches the signals and trains on faults to predict future faults. The flexibility in neural networks means that the individual data stream and fault predictions are agnostic to the type of sensor, or nature of the fault, and can be used to monitor anything from motor vibration signals in a bakery, to turbine characteristics in a power plant.

Fortunately, this future isn’t far off. There are multiple established companies and startups in this sector that provide solutions with varying levels of generalisation and customisation to tailor to

Hirebotics rents robots on hourly wages for customer needs. For example, multinational companies such as Google (Google Cloud Auto ML) and Microsoft (Azure Custom Vision) provide generic APIs for computer vision that companies can use, with a limited staff of software engineers in their robotics and automation team. Other companies such as Quantiphi, Infinite Uptime Inc, etc., act as solution providers to the industry, delivering turnkey projects using their AI expertise. Many lesser-known startups have also developed zero-code GUI based train-test-deploy solutions to generalize object detection, inspection, predictive maintenance, etc., for industrial automation.

The last piece of the pie is in bringing grab-and-go solutions to the edge. With the advent of smaller form factor computers such as the Intel NUC, Jetson Nano and Superlogics embedded PCs, AI computations are cheaper and faster than ever. The small form factor, low cost and computational power of GPU based computers such as Jetson Nano, mean that neural networks can now be deployed closer to the edge than ever. Control panels on current automation can be retrofitted with these portable computers, some of which even have a form factor that can be packaged into a standard DIN rail assembly. These computers can then run advanced grab-and- go neural networks that only require training once over a sample labelled data, to make autonomous intelligent decisions by themselves.

Closing Thoughts #

The future of intelligent automation looks bright and is closer than ever to fruition. The indigenous GreyOrange’s butler robots use a suite of AI and ML software to revolutionise the e-commerce warehousing industry. Tech Mahindra employed off-the-shelf Facial Recognition Attendance Software to significantly reduce the time taken by employees to manually update an attendance sheet. There are even robots-for-hire companies such as Hirebotics (US) that charge hourly wages for using the robots, just like human workers. The key takeaway is that companies with legacy automation need to seek out and deploy grab-and-go AI solutions, or else they stand the risk of being outpaced by smaller and more agile software companies with custom-built solutions.