When we mention “robotics” today, let’s not immediately picture those complex images that you’d find on a stock photos website. Instead, I’d like you to take into consideration a device that you’re likely to have at home – the genius that is the little robotic vacuum cleaner. That little disc-shaped wizard glides across your floor on its own, doing its chores without a complaint in the world. It has evolved tremendously from its inception in 1996, and from bumping into obstacles all along its path to learn its route and struggling to climb over mildly uneven surfaces, robot vacuums have come a long way. They now understand maps, obstacles and blocks, and work around them independently without one having to pick them up and place them back like an unstable toddler that’s just learning to walk.

All this is mainly because robots nowadays are equipped with analytical models that need to plan for uncertainty. They have the ability to think of countless scenarios and work out how to overcome them. When it comes to preparing for conceivable scenarios, it’s impossible to be fully prepared. But empowering these robots with an understanding of what is important, and how to prioritize helps them learn and make decisions as they go. 

And this is what designing for uncertainty is all about.

At ABB, we have Autonomous Mobile Robots (AMRs) which are designed to move and navigate independently in a given space using sensors and AI. These transport robots move loads autonomously in various industries – from automotive to logistics to consumer goods and other industrial processes. Earlier, they would follow a path they’d been taught to go on, and any reconfiguration of the path meant a reconfiguration of the robot as well. But it’s been learned over the years that change is inevitable, and in today’s tech world, uncertainty is built into the robot, giving it a decision making power.

What is uncertainty? It basically can be anything from an internal or external source that can break the fixed pattern of what the robot has learned. From changes in its usual environment to unplanned events, uncertainty is anything that causes the robot to evaluate factors that have come in its usual set pattern of decision making. It learns, evaluates its solution, and refines its abilities through this trial and error system. Remember our BASIC computer commands of IF, THEN, ELSE? It’s almost the same, but accentuated by the advances in technology like AI and Visual Slam.

Visual SLAM is a navigation technology that combines AI and 3D vision using off-the-shelf cameras. It allows AMRs to make intelligent decisions based on their surroundings, providing  higher accuracy and robustness even in challenging environments. It can help differentiate between fixed navigation references and moving objects and people that aren’t permanently a part of the map. This adds a whole new dimension of flexibility to tackle how uncertain situations can be worked with.

This is the era of resilience in tech – where robots aren’t just about precision, but also adaptability. The more human they become, so does their power to make decisions and find a way around a situation that they hadn’t been programmed for, increase. And I’m not just talking about AMRs and robot vacuums, but everything from self-driving cars to educational, service and medical robots. Planning for uncertainty is surely less straightforward, but an essential step to make the robots of the future more robust, efficient and imaginative.