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.
IEEE Transactions on Robotics
Vibration measurement,Safety,Rough surfaces,Surface roughness,Robustness,Extraterrestrial measurements,Accelerometers,Linear discriminant analysis,Laboratories,Testing
Offline learning,Computer vision,Accelerometer,Remote sensing,Terrain,Artificial intelligence,Linear discriminant analysis,Vibration,Engineering,Contextual image classification,Mobile robot,Robotics