Terrain classification using intelligent tire

dc.contributor.authorKhaleghian, Seyedmeysamen
dc.contributor.authorTaheri, Saieden
dc.contributor.departmentMechanical Engineeringen
dc.contributor.departmentBiomedical Engineering and Mechanicsen
dc.date.accessioned2017-02-27T17:14:05Zen
dc.date.available2017-02-27T17:14:05Zen
dc.date.issued2017en
dc.description.abstractA wheeled ground robot was designed and built for better understanding of the challenges involved in utilization of accelerometer-based intelligent tires for mobility improvements. Since robot traction forces depend on the surface type and the friction associated with the tire-road interaction, the measured acceleration signals were used for terrain classification and surface characterization. To accomplish this, the robot was instrumented with appropriate sensors (a tri-axial accelerometer attached to the tire innerliner, a single axis accelerometer attached to the robot chassis and wheel speed sensors) and a data acquisition system. Wheel slip was measured accurately using encoders attached to driven and non-driven wheels. A fuzzy logic algorithm was developed and used for terrain classification. This algorithm uses the power of the acceleration signal and wheel slip ratio as inputs and classifies all different surfaces into four main categories; asphalt, concrete, grass, and sand. The performance of the algorithm was evaluated using experimental data and good agreements were observed between the surface types and estimated ones.en
dc.format.extent15 - 24 page(s)en
dc.identifier.issn0022-4898en
dc.identifier.orcidTaheri, S [0000-0001-7514-1690]en
dc.identifier.urihttp://hdl.handle.net/10919/75181en
dc.identifier.volume71en
dc.publisherPergamonen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleTerrain classification using intelligent tireen
dc.title.serialJournal of Terramechanicsen
dc.typeArticleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Mechanical Engineeringen

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Terrain Classification.pdf
Size:
2.71 MB
Format:
Adobe Portable Document Format
Description:
Accepted Version
License bundle
Now showing 1 - 1 of 1
Name:
VTUL_Distribution_License_2016_05_09.pdf
Size:
18.09 KB
Format:
Adobe Portable Document Format
Description: