Year: 2004
Type: dissertation
Abstract: Autonomous mobility in rough terrain is becoming increasingly important for planetary exploration rovers.Increased knowledge of local terrain properties is critical to ensure a rover's safety, especially when driving on slopes or rough surfaces.This thesis presents two methods for using on-board sensors to identify local terrain conditions.The first method visually measures sinkage of a rover wheel into deformable terrain, based on a single color or grayscale image from a camera with a view of the wheelterrain interface.Grayscale intensity is computed along the rim of the wheel, and the wheel-terrain interface is identified as the location with maximum change in intensity.The algorithm has been shown experimentally to give accurate results in identifying the terrain characteristics under a wide range of conditions.The second method classifies terrain based on vibrations induced in the rover structure by rover-terrain interaction during driving.Vibrations are measured using an accelerometer on the rover structure.The method uses a supervised learning approach to train a classifier to recognize terrain based on representative vibration signals during an off-line learning phase.Real-time terrain classification uses linear discriminant analysis in the frequency domain to identify gross terrain classes such as sand, gravel, or clay.The algorithm is experimentally validated on a laboratory testbed and on a rover in outdoor conditions.Results demonstrate the robustness of the algorithm on both systems.
Author: Christopher Allen Brooks
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1583173390