Why and how to build autonomous systems

Wind turbines are shown on a mountain peak

This is one part of a four-part introductory series about autonomous systems. To learn more, read the rest of the series and the free e-book linked at the bottom of this post. 

Automated control systems were one of the most disruptive applications of industrial technology in the 20th century. The ability to control workflows and processes based on specific inputs and outputs streamlined even the most complex manufacturing processes.

These systems, however, need specific parameters and, in some cases, require extensive human oversight and planning to ensure optimal execution. Innovations in AI training methodologies are pushing past these limitations to produce the next wave of disruption to industrial technology: autonomous systems. Autonomous machines do more than address the limitations of automated systems, however. They introduce an entirely new way to amplify human knowledge.

Autonomous vs. automated: What is the difference?

Autonomous systems are AI-powered industrial controls that optimize operations by sensing and dynamically responding to changing physical environments. To understand the impact of autonomous systems, it is best to understand how they differ from automated systems. While they share similar-sounding names, the two are significantly different in how they operate and what they enable in an industrial setting.

An automated system manages how different machines perform different tasks, like a proportional integrator (PI) device in heavy equipment like a bulldozer. PI devices in bulldozers provide controls for activities like lifting and lowering the bulldozer scoop blades to cut level earth for construction purposes. Under ideal conditions, a PI can help position the blade to maintain a level cut. When environmental conditions change, however (due to mud, sand, gravel, etc.) the automated PI can’t compensate effectively.

Autonomous systems can react to changing conditions and adapt operations to maintain efficiency and accuracy. Autonomous systems learn from human experts and practice safely in a simulated environment before responding to real-world scenarios. This means the same machine, powered by an autonomous control system, understands how variables like air temperature, soil consistency or even the age of the machine itself will affect the outcome, and will take or recommend the best course of action to meet the desired objective, in this case efficiency and accuracy.

3 best practices for autonomous systems

The transformative power of an autonomous system might seem self-evident, but there are several ways in which engineers, operators and organizations can maximize these systems.

  1. Leverage your human experts. Autonomous systems do not replace expertise, they amplify it. Infusing AI with the technical expertise and wisdom of your experts will help your organization deploy the most effective control systems possible.
  2. Deploy autonomous systems in partnership with people or autonomously. There are several ways to deploy autonomous systems: They can assist your workers on a task, act as an advisor to find the best path forward in a task or execute that task autonomously. In each case, there is an integral partnership between people and system.
  3. Start wherever it makes sense. Focus on where you need autonomous solutions and make them happen there first. If you need a single control, do that before planning an entire overhaul of your process. Conversely, if you want to build autonomous processes, then plan there and see what controls fit into that plan.

While automation helped transform several industries by mobilizing technology to build products at scale, autonomous systems take that transformation several steps further by recognizing nuances and changes to enhance the creative problem solving and strategic thinking of human experts.

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