The Difference Between Autonomous Machines and Autonomous Processes

To fully operate without human intervention, autonomous vehicles need to be able to detect other vehicles and traffic signs as well as road conditions.

The automotive industry is betting heavily on the future of autonomous driving. Waymo and Tesla are two examples of how intelligent cars use artificial intelligence to make decisions autonomously in a complex environment.

The one thing that is very different from autonomous driving on the streets versus autonomous driving in industrial environments is that a car always has a clear and simple goal or mission. It needs to drive from A to B.

When we use autonomous robots in industrial environments, we no longer have just one goal — we have multiple and sometimes conflicting goals. And we need to continue meeting those goals for an indefinite amount of time. That adds a new level of complexity and requires a whole new approach.

In this case, Quantillion uses a combination of predictive AI and deterministic calculations to optimise operations and achieve multiple goals, continuously. With this approach we can work a mixed traffic fleet with both automated and autonomous machines.

By creating multiple work orders in advance, we can optimise and distribute tasks to multiple machines and keep track of overall statuses of different processes. Optimisation means you can handle more tasks with less equipment and respond to process blockages without human involvement.

These Autonomous Decision Systems allow factories to focus on day-to-day issues and no longer need to worry processes will stop when something goes wrong.