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  /  Latest News   /  Your Everyday WiFi Can Actually Make Robots Friendlier Family Members

Your Everyday WiFi Can Actually Make Robots Friendlier Family Members

WiFi can actually make robots our friendlier family by helping them navigate better indoors.

Engineers from the University of California, San Diego, have created a low-cost, low-power device to assist robots in properly mapping their path indoors, even in low-light conditions and in the absence of recognizable landmarks or features. The use of basic WiFi can actually make robots our friendlier family members. They will use sensors that depend on WiFi signals to assist the robot as it traces its path to make up the technology. It’s a fresh take on robot navigation indoors. Optical light sensors, such as cameras and LiDARs, are used in the majority of systems as household robots in the future. The so-called “WiFi sensors” in this case use radio frequency signals rather than light or visual cues, allowing them to operate in settings where cameras and LiDARs struggle, such as low light, shifting light, and repetitive surroundings like lengthy hallways and warehouses. This is how humans can make up the actual use of robots in real life.

This impact of the Internet on our life could also provide a cost-effective alternative to expensive and power-hungry LiDARs, according to a group of researchers from UC San Diego’s Wireless Communication Sensing and Networking Group. Wireless signals surround us practically everywhere we go and that’s how WiFi can make robots our friends. The beauty of this research is that we can use these common signals to accomplish indoor localization of household robots in the future and practice mapping with robots. Building a new kind of sensing modality using WiFi that fills in the gaps in light-based sensors will enable robots to navigate in scenarios where they can’t currently.

The researchers used off-the-shelf gear to create their prototype system. The WiFi sensors, which are created using commercially available WiFi transceivers, are attached to a robot in the system. These devices broadcast and receive wireless signals to and from nearby WiFi access points. These robots are unique in that they employ constant back-and-forth communication with WiFi access points to map their own location and movement direction.

This two-way communication is already happening all the time between mobile devices like your phone and WiFi access points just it’s not informing you where you are. In an unknown environment, our technology uses that communication to accomplish localization and mapping. The WiFi sensors are initially unaware of the robot’s location as well as the location of any WiFi access points in the area. It’s like playing Marco Polo: the sensors cry out to the access points and listen for their responses as the robot progresses, utilizing them as landmarks. The key is that each incoming and outgoing wireless signal has its own unique physical information an angle of arrival and direct path length to (or from) an access point that can be used to determine where the robot and access points are with respect to one another. It also helps to extract data thanks to algorithms created by the team. As the request and answer cycle continues, the sensors gather more data and may pinpoint the robot’s location.

The researchers put their technique to the test on a floor of a skyscraper. They set up many access points across the facility and outfitted a robot with WiFi sensors, as well as a camera and a LiDAR to make comparison measurements. The researchers programmed their robot to cruise around the floor multiple times, turning corners, narrowing passages, and traveling through both bright and dimly illuminated areas. In these experiments, the WiFi sensor’s accuracy of localization and mapping was comparable to that of commercial cameras and LiDAR sensors.

Robust and reliable sensing in visually demanding conditions using WiFi signals, which are practically free. In these circumstances, WiFi sensing might potentially replace pricey LiDARs and augment other low-cost sensors like webcams. That is what the team is now investigating. To develop a more complete, yet inexpensive, mapping method, the researchers will combine WiFi sensors (which provide accuracy and dependability) with cameras (which provide visual and contextual information about the area).