Researchers from Russia’s Skolkovo Institute of Science and Technology have developed a radio frequency identification (RFID) tag-reading robot for obtaining insights into consumer behavior and for making predictions about demand distribution in retail settings.

The robot, dubbed Michelle, is an autonomous stocktaking robot at RFID-equipped stores that reads RFID tags with an accuracy of 0.3 m, while simultaneously observing customers, noting those locations throughout the store they find most appealing.

Source: Skolkovo Institute of Science and Technology

According to Michelle’s developers, the data gathered by the autonomous RFID-reading robot can be used to construct models for making predictions about consumer demand as well as offering tips to retailers about where to place products to drive up sales and, consequently, profits.

"At Skoltech, we have developed Michelle, an RFID-capable autonomous robot for Decathlon stores, that helps considerably reduce the number of RFID-reading errors caused by the human factor and make the inventory faster and cheaper. We came up with an idea to benefit from big data collected over 10 months of the robot's operation at the store. Specifically, we wanted to estimate the tag density change over the entire purchase area. First, we proposed a probabilistic model for estimating tag locations with an accuracy of 0.3 m and then constructed the hit map of the tag density dynamics which clearly shows the maximum and minimum purchase quantity areas. These findings are very important for retailers in relocating the goods in order to maximize sales profits and predict seasonal demand profiles," Skoltech Professor Dzmitry Tsetserukou explained.

The research appears in the Proceedings of the International Conference on Control, Automation, Robotics and Vision (ICARCV).