Terrain classification using intelligent tire

Abstract

A wheeled ground robot was designed and built for better understanding of the challenges involved in utilization of accelerometerbased 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.

A Road Condition Classification Algorithm for a Tire Acceleration Sensor using an Artificial Neural Network

“…These acceleration sensor values were used as input for determining the road surface condition via ANN analysis after FFT processing. 16,125 In this paper, the relationship between the acceleration data measured through iTire and the road surface condition was modeled using MLP. Nine values obtained through pre-processing (at 0 Hz, and then at each 50 Hz interval from 100-500 Hz, where these data were summed) and a bias value were selected as input variables for the input layer.…”

“…Tires that collect such information through an internal sensor and relay data on the road surface conditions to the driver are called intelligent tires (iTires). Cars equipped with iTire technology can determine the road 2 of 8 condition on behalf of the driver, which may not be known by the driver, and ensure adequate steering and braking, thereby promoting safer driving [15,16].…”

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

“…In Selmanaj et al [60] used Self-Organizing Maps (SOM) to classify the road in baseline road, road with irregularities and road with hazard. Khaleghian and Taheri [34] applied fuzzy logic to recognize the surface type in grass, soil, concrete or asphalt. Fouad et al [21] used rough mereology theory to identify speed bumps.…”

“…Pitoňák and Filipovsky [52] aimed at evaluating the quality control and quality assurance in the civil engineering project handover process. [34], through the recognition of the surface composition type, controlled the vehicle speed.…”

Review on Friction and Wear Test Rigs: An Overview on the State of the Art in Tyre Tread Friction Evaluation

“…(a) The six-wheel small ground robot (left)[68]; (b) 3D representation of sample holder (adapted from[67]). …”