Dynamic modeling and control of nonholonomic mobile robot with lateral slip
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Robot Navigation in Multi-terrain Outdoor Environments
International Journal of Robotics Research
Online speed adaptation using supervised learning for high-speed, off-road autonomous driving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Vibration-based terrain classification for electric powered wheelchairs
Telehealth/AT '08 Proceedings of the IASTED International Conference on Telehealth/Assistive Technologies
Self-supervised terrain classification for planetary surface exploration rovers
Journal of Field Robotics
Shock Reduction for Autonomous Navigation on Rough Terrain: A Difference of Normals Approach
Proceedings of Conference on Advances In Robotics
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Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover's safety on slopes and uneven surfaces. Visual features are often used to classify terrain; however, vision can be sensitive to lighting variations and other effects. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. This sensing mode is robust to lighting variations. Vibrations are measured using an accelerometer mounted on the rover structure. The classifier is trained using labeled vibration data during an offline learning phase. Linear discriminant analysis is used for online identification of terrain classes, such as sand, gravel, or clay. This approach has been experimentally validated on a laboratory testbed and on a four-wheeled rover in outdoor conditions.