An autonomous robot that operates in huge facilities and keeps track of valuable, portable equipment that is constantly on
the move. The robot positions itself using bluetooth, wifi, or RFID. With built-in Artificial Intelligence, Schneider learns
and automatically improves accuracy as it cruises through a space. Its success as an automated Inventory Management System
saved hundreds of thousands of dollars for our client that would have otherwise used staff to manually manage this equipment.
Our client is one of the largest commercial real estate companies in the world. They employ 70,000 people across more than
280 corporate offices, and have revenues of over $6 billion a year while managing more than 4 billion square feet of real
The client wanted to develop a proof of concept which would automate inventory management. This could be utilized in their
own offices with thousands of pieces of equipment, or productized as a service to their large customers for their spaces.
For example, a hospital has lots of large pieces of portable equipment that are constantly moving around. It pays big returns
in both healthcare and dollars to know where each piece of equipment was last seen and if it has gone missing from the facility.
To solve this problem, Codelitt built the brains, or AI, behind a custom autonomous robot, that we
affectionately named Schneider. In order to satisfy the very specific customer needs, we had to create our own
indoor positioning system (IPS), which is more accurate than anything on the market. Indoor positioning is like
GPS, but specifically for indoor applications. GPS can’t reliably be used indoors, because often there is no
signal, or just an inaccurate signal. Part of what makes this such a difficult problem to solve is that you
are dealing with very small spaces that require very high accuracy. In Google Maps, for example, GPS accuracy
of 5 feet is not a problem. If the GPS shows you 5 feet to the south of your actual position, then the map will
still show you on the road. However, if you’re indoors, 5 feet could mean the difference between being in an
entirely different room on the other side of the wall. To get accurate results for indoor positions, you have
to rely on data from sources such as low energy bluetooth, wifi, or RFID. These are technologies that are not
specifically designed for location. We utilized a fingerprint, triangulation, and machine learning solution that
enabled us to track the indoor position of the robot using these signals with very high accuracy. The more data
that is collected from the bot roving, the better the machine learning algorithms work and the higher accuracy
First, we adapted a self-mapping system. A self-mapping system utilizes LIDAR (lasers) and an artificial intelligence that
senses walls, furniture, and other fixtures in a space, and then builds a point cloud. Schneider roams around our test space
building this point cloud which it then turns into a 2D map of the space. Part of this self-mapping system is object avoidance,
even for temporary objects like a chair that has been moved, or a person walking in front of the robot. We then outfitted our
inventory with low energy bluetooth devices stuck on them. We built a mobile application for an Android device which has a
bluetooth sensor built in and senses the bluetooth fingerprints from these inventory items. The moment the signal is strongest,
the device sends a signal to a central server which then communicates with Schneider to retrieve its location on the map.
The real position is then translated into pixels on a map and the inventory item appears in it’s current location on a web
accessible map. The map allows the end user to be able to monitor current positions, past position, and if an item was not
detected during a scan. These navigations can be scheduled, or done manually.
The final deliver of Schneider was an incredible success. Our client was very pleased with the result, and is hoping to roll
out a pilot in select locations. The automated inventory management system could provide hundreds of thousands of dollars in
savings for a large facility with expensive equipment, as well as savings on the human capital that would be needed to track
End-to-end commercial real estate transaction management.
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