RF-compass: Robot object manipulation using RFIDs
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Katabi_RF-compass.pdf
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2.61 MB
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Author(s)
Wang, Jue
Katabi, Dina
Adib, Fadel M.
Knepper, Ross A.
Rus, Daniela L.
Date Issued
September 2013
Journal
Proceedings of the 19th annual international conference on Mobile computing & networking (MobiCom '13)
Publisher
Association for Computing Machinery (ACM)
Citation
ue Wang, Fadel Adib, Ross Knepper, Dina Katabi, and Daniela Rus. 2013. RF-compass: robot object manipulation using RFIDs. In Proceedings of the 19th annual international conference on Mobile computing & networking (MobiCom '13). ACM, New York, NY, USA, 3-14.
Version
Author's final manuscript
Abstract
Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches).
This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compass's key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robot's consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1145/2500423.2500451
