Learning and prediction of slip from visual information: Research Articles

Authors:
Anelia Angelova;Larry Matthies;Daniel Helmick;Pietro Perona
Affiliations:
Department of Computer Science California Institute of Technology Pasadena, California 91125;Jet Propulsion Laboratory California Institute of Technology Pasadena, California 91109;Jet Propulsion Laboratory California Institute of Technology Pasadena, California 91109;Department of Electrical Engineering California Institute of Technology Pasadena, California 91125
Venue:
Journal of Field Robotics - Special Issue on Space Robotics
Year:
2007

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Abstract

This 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. © 2006 Wiley Periodicals, Inc.