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Learning and Prediction of Slip from Visual InformationThis paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.
Document ID
20080024232
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Angelova, Anelia (California Inst. of Tech. Pasadena, CA, United States)
Matthies, Larry (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Helmick, Daniel (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Perona, Pietro (California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 24, 2013
Publication Date
March 1, 2007
Subject Category
Geophysics
Distribution Limits
Public
Copyright
Other
Keywords
rover
navigation
rough terrain
imaging systems
slip prediction