Robotics and RFID to Manage Your Wardrobe

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ThreadRobe, a Virginia-based startup, intends to automate the way in which people store and manage their clothes. Its new automated armoire—which employs passive ultrahigh-frequency (UHF) RFID technology, as well as a mobile app—is being sold directly to consumers. With the technology in place, users can deposit clean clothes into a bin, and the armoire will then identify each item via RFID, hang it up and provide it to the user—freshly steamed—when requested. The app enables users to view what is hanging in the armoire, and to make requests. This, according to the company, helps them with buying, packing and strategizing their wardrobe for the coming days or weeks.


"We've been talking to consumers for over a year to understand what's most important to them," says Kristy Schultz, ThreadRobe's marketing VP. That effort, she says, determined that "the biggest pain point is folding and putting away clothes." So the ThreadRobe system is intended to take that task from the user. Once clean clothes are throw into the bin, the armoire's built-in robotics pick up and hang each item, then automatically steam the garment before providing it to the individual when required.


However, identifying each garment required technology such as RFID. First, users must apply an RFID tag to every clothing item. ThreadRobe has tested a variety of tags from multiple suppliers, but has not yet settled on a specific RFID tag make or model, the company reports. Tagged clothing can include socks, underwear, swimwear, shirts, slacks, jeans and suits.


The individual uses an app on his or her smartphone to input information regarding each item, including taking a picture of it so the system knows its color. The photo is also used to identify the item in the app. That process "takes about 30 seconds per garment," says Matt Powell, ThreadRobe's founder and CEO. The user can then hold the tagged garment within read range of an RFID reader antenna (mounted on the armoire's interior but designed to read on the exterior), so that the tag ID can be linked to that clothing. ThreadRobe employs a variety of UHF RFID readers and antennas.


When a load of tagged clothing is placed inside the armoire bin—about 30 items could be loaded into it at a time—the device proceeds to lower a robotic arm with a rubberized hook to pick up each garment. As it does so, it carries the tag near the second built-in RFID antenna, mounted to read in the armoire's interior, so that the item can be identified. The wardrobe software selects a storage location appropriate in size for that item and the hook moves to that location. The clothing then simply remains on that hook until needed.


To request a particular item, a user can utilize the ThreadRobe app. The app-based data is forwarded to the armoire, which releases the clothing attached to that hook (which has been linked to its RFID number) at the selected time and steams it at a preset level before releasing the door so that the user can retrieve it. The garment is not folded; because it is steamed, however, it should be wrinkle-free and ready to wear.


"The other aspect to the system is the digital closet," Powell says. The app stores data regarding what items a user has input into the system when they were tagged, as well as what is hanging in the armoire (anything not in the armoire could be presumed as either being worn or in the laundry). The user inputs enough detail about each item that the ThreadRobe app knows how it is worn—such as for business or at the gym—and maintains a record of when and how often the user retrieves it from the armoire.


Users can employ the app to select outfits for themselves or their children for the week, or to pack for a trip without being at home. They could select garments from the app, or mix and match items and determine which ones go together, and then select that clothing to be provided at the time they wish to pack. The app could also be used during shopping, to ascertain which items in a user's wardrobe might match a garment he or she plans to buy.


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Last update: Apr 26, 2017

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