Terrain classification using intelligent tire. (June 2017)

• Record Type: Journal Article
• Authors: Khaleghian, Seyedmeysam; Taheri, Saied
• Is Part Of: Journal of terramechanics. Volume 71(2017:Jun.)
• Page Start: 15
• Page End: 24
• Publication Date: 2017-06
• Subjects: Wheeled ground robot -- Intelligent tire -- Terrain classification -- Fuzzy logic algorithm; Trafficability -- Periodicals; Praticabilité (Routes) -- Périodiques; Trafficability; Periodicals; 629.222
• DOI: 10.1016/j.jterra.2017.01.005
• Languages: English
• ISSNs: 0022-4898

Abstract

Highlights: This study presents a terrain classification algorithm using intelligent tire. A six-wheel ground robot was designed and built for this purpose. Fuzzy logic algorithm was used to classify all surfaces into four known surfaces. The proposed algorithm was tested and validated using experimental data. Good agreements were observed between surfaces types and estimated ones. Abstract: A 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.