Title:
RF vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation

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dc.contributor.author Deyle, Travis en_US
dc.contributor.author Nguyen, Hai en_US
dc.contributor.author Reynolds, Matt S. en_US
dc.contributor.author Kemp, Charles C. en_US
dc.contributor.corporatename Georgia Institute of Technology. Healthcare Robotics Lab en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines en_US
dc.date.accessioned 2011-03-11T15:18:55Z
dc.date.available 2011-03-11T15:18:55Z
dc.date.issued 2009-10
dc.description ©2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. en_US
dc.description Presented at IROS 2009, the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 10-15 Oct. 2009, St. Louis, MO. en_US
dc.description DOI: 10.1109/IROS.2009.5354047 en_US
dc.description.abstract In this work we present a set of integrated methods that enable an RFID-enabled mobile manipulator to approach and grasp an object to which a self-adhesive passive (battery-free) UHF RFID tag has been affixed. Our primary contribution is a new mode of perception that produces images of the spatial distribution of received signal strength indication (RSSI) for each of the tagged objects in an environment. The intensity of each pixel in the 'RSSI image' is the measured RF signal strength for a particular tag in the corresponding direction. We construct these RSSI images by panning and tilting an RFID reader antenna while measuring the RSSI value at each bearing. Additionally, we present a framework for estimating a tagged object's 3D location using fused ID-specific features derived from an RSSI image, a camera image, and a laser range finder scan. We evaluate these methods using a robot with actuated, long-range RFID antennas and finger-mounted short-range antennas. The robot first scans its environment to discover which tagged objects are within range, creates a user interface, orients toward the user-selected object using RF signal strength, estimates the 3D location of the object using an RSSI image with sensor fusion, approaches and grasps the object, and uses its finger-mounted antennas to confirm that the desired object has been grasped. In our tests, the sensor fusion system with an RSSI image correctly located the requested object in 17 out of 18 trials (94.4%), an 11.1% improvement over the system's performance when not using an RSSI image. The robot correctly oriented to the requested object in 8 out of 9 trials (88.9%), and in 3 out of 3 trials the entire system successfully grasped the object selected by the user. en_US
dc.identifier.citation Deyle, T.; Hai Nguyen; Reynolds, M.; Kemp, C.C., "RF vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation," 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, ( IROS 2009) 5553-5560 en_US
dc.identifier.isbn 978-1-4244-3803-7
dc.identifier.uri http://hdl.handle.net/1853/37360
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers en_US
dc.subject Antennas en_US
dc.subject Attitude control en_US
dc.subject Control engineering computing en_US
dc.subject Manipulators en_US
dc.subject Mobile robots en_US
dc.subject Radiofrequency identification en_US
dc.subject Robot vision en_US
dc.subject Sensor fusion en_US
dc.subject User interfaces en_US
dc.title RF vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation en_US
dc.type Text
dc.type.genre Proceedings
dc.type.genre Post-print
dspace.entity.type Publication
local.contributor.author Kemp, Charles C.
local.contributor.corporatename Healthcare Robotics Lab
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
relation.isAuthorOfPublication e4f743b9-0557-4889-a16e-00afe0715f4c
relation.isOrgUnitOfPublication c6394b0e-6e8b-42dc-aeed-0e22560bd6f1
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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