We’re familiar with the iRobot Roomba, but how is Artificial Intelligence improving the technology to expand to include industrial cleaning equipment – or is it?
For anyone who has owned a Roomba or any other brand of robot vacuum cleaner, you know that, thus far, it’s been an acceptable stop-gap when managing household floors between proper vacuums. But, not quite up to par for taking over your human-powered vacuum routine.
Between sucking up socks and getting caught up in power cords that can render it incapacitated, it’s been a great idea in theory and simply OK in practice.
But that may be not only changing but improving enough to include the demands of industrial cleaning needs as well as that of the home.
The commercial cleaning industry is a profitable and sustainable one that shows no sign of diminishing. As long as people want and need to work, shop, learn, and more in a clean environment and as long as there are rules and regulations that compel employers to ensure they keep a meticulously clean facility, there will be a healthy market for industrial cleaning.
And, according to the Bureau of Labor Statistics in the United States, there are 2,384,600 building janitors and cleaners representing an annual spend of nearly $60 billion. When it comes to the future of these jobs, “the employment of janitors and building cleaners is projected to grow 10 percent from 2016 to 2026, faster than the average for all occupations.”
With the demand only projected to increase and employment in the sector seeming secure, there is still room for Artificial Intelligence (AI) to innovate the industry and bring increased efficiency and cost-savings into the marketplace.
Artificial Intelligence to clean – how does it work?
For any device, whether for home or commercial use, how does a computerized cleaner know where it’s been and where it hasn’t? How does it know when it’s finished? Why do some seem to clean in an orderly way, while others follow a meandering and indirect path?
It’s actually not that complicated. There are basically just two ways a robot vacuum finds its way around a room, a home, or a workspace.
To clean a room effectively, the device needs to move freely through the space while staying out of trouble! Of course, they aren’t able to view the world as we do, even if there’s a camera on board.
The first iteration in AI cleaning equipment, and in common and lower end models, the device has relied on various sensors to detect obstacles and other hazards (including sudden vertical drops), measure how far they’ve traveled, and also discover the new areas it has yet to cover. These sensors trigger programmed behaviors that determine how the robot responds. Just which sensors a robot vacuum uses and how they work can vary by manufacturer and model, but these are common to all: obstacle sensors, cliff sensors, wall sensors, and wheel sensors.
At one time, sensor navigation was the way all robot vacuums worked. Today it’s mostly limited to manufacturer’s lower-end models, because though it’s effective, it’s not particularly efficient. Because these robot vacuums react to sensory input, they tend to grope their way through a room, vacuuming in haphazard paths. In order to get complete coverage and clean every area at least once they’ll take multiple passes over a room in whatever time their battery life allows.
Mapping Technology, Data, and AI
Newer, higher-end robot vacuums and cleaning equipment include self-navigation systems that use mapping technology. Each manufacturer implements its own particular spin on mapping, but each of them is currently constructed around these two methods: an onboard digital camera and a a laser range finder.
The onboard digital camera takes pictures of walls, ceilings, doorways, furniture, and other obstacles and landmarks. The laser range finder (also called LIDAR for Light Detection and Ranging) measures the distance to objects in the machine’s path.
In either case, the robot vacuum uses the data it collects in combination with information from its other sensors to gradually build a map of the room during its initial cleaning.
Mapping delivers significant advantages. Equipped with a floor plan, the robot cleaner can plot the most efficient route through the room, which is why mapping models seem to move in more orderly straight lines than their non-mapping counterparts.
Mapping also allows the robot vac to localize itself within the map, which informs it where it’s been and where it yet needs to go. And if the vacuum’s battery runs low part way through the job, it can return to its dock to recharge, and then pick up where it left off. The result is a quicker, more thorough and even cleaning.
Current iterations of industrial machines incorporate routing algorithms, 4G LTE connectivity, sensors, and cameras. Look for GPS tracking and mobile data transmission to provide data as well as send text notifications to its users.
Regardless of the technology, right now, no robot cleaner or vacuum will navigate flawlessly all the time.
When incorporated into the industrial cleaning sector, this technology will have the power to transform the cleaning process. However, despite the consistent advancements in Artificial Intelligence in cleaning and commercial cleaning, when all’s said and done, the robot still needs a custodian – someone trained in the technology – on hand to manage the tasks like recharging, maintaining, and repairing the machine and replenishing its tank.