Terrain identification methods for planetary exploration rovers
Author(s)Brooks, Christopher Allen, 1978-
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
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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 wheel- terrain 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.
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (leaves 77-82).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology