robot hoover cleaning up dirt

Cleanliness sensor helps autonomous robots find dirty areas to clean

Image credit: Dreamstime

A sensor that lets autonomous robots assess an area’s cleanliness could help in the fight against Covid-19 in public spaces.

Developed by researchers from the Singapore University of Technology and Design (SUTD), the sensor works by pressing a white adhesive tape onto the floor and scanning for dirt particles in the tape.

By measuring the degree of dissimilarity between the photo of the tape before and after it was pressed, the team came up with a dirt score that can be assigned to the area. The sensor could also count the number of pixels corresponding to dirt on the photo of the tape, providing insight into the area’s dirt density.

“With this sensor that assigns a dirt score to an area using the touch-and-inspect analogy, what we need to do next is design the robot that could ‘touch’ a huge region,” explained first author Thejus Pathmakumar.

One strategy is to let the robot roam everywhere, checking every nook and cranny of the area, but it was determined that such a method is inefficient, as some regions may have higher concentrations of accumulated dirt, while others may not.

To make exploration smarter, the researchers programmed an algorithm that would encourage the robot to explore regions more likely to be dirty. Their dirt-probability-driven algorithm prompted the robot to notice changes in the floor’s visual patterns that may indicate dirt, after which the robot would be directed to navigate into the centre of the region.

To supplement this strategy, the team also used a frontier exploration algorithm so that the robot would prioritise unexplored areas.

“The frontier exploration algorithm is commonly used in applications like search and rescue,” Pathmakumar said.

“We modified this algorithm so that while the robot would still be motivated to move towards the frontier, if it sees a region with high dirt probability, it will go there first.”

Based on data from the touch-and-inspect method, the robots quantified an area’s cleanliness with a cleaning benchmark score between 0 and 100, with scores closer to 100 corresponding to cleaner surfaces.

The team tested their cleaning audit robots on a mix of indoor and semi-outdoor areas. Their tests showed lower benchmark scores for the latter, as areas with coarse textures made dust particles less susceptible to being lifted by the tape. Transitioning between different floor textures also prompted the robot to falsely detect dirt, suggesting future aspects for improvement.

In the future, the team hopes to take other factors into account, such as the microbial density in a given area or even being able to detect bad smells that imply an area is dirty.

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