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Sadhukhan, D. (2004). Autonomous Ground Vehicle Terrain Classification Using Internal Sensors. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-2115
The semi-autonomous vehicle known as the Experimental Unmanned Vehicle (XUV)was designed by the US Army to autonomously navigate over different types of terrain. The performance of autonomous navigation improves when the vehicle's control system takes into account the type of terrain on which the vehicle is traveling. For example, if the ground is covered with snow a reduction of acceleration is necessary to avoid wheel slip.Previous researchers have developed algorithms based on vision and digital signal processing (DSP) to categorize the traversability of the terrain. Others have used classical terramechanics equations to identify the key terrain parameters. This thesis presents a novel algorithm that uses the vehicle's internal sensors to qualitatively categorize the terrain type in real-time. The algorithm was successful in identifying gravel, packed dirt, and grass.
A Thesis Submitted to the Department of Mechanical Engineering in Partial Fulfillment of the Requirements for the Degree of Master of Science.
Bibliography Note
Includes bibliographical references.
Advisory Committee
Carl Moore, Professor Directing Thesis; Emmanuel Collins, Committee Member; Rodney Roberts, Committee Member.
Publisher
Florida State University
Identifier
FSU_migr_etd-2115
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Sadhukhan, D. (2004). Autonomous Ground Vehicle Terrain Classification Using Internal Sensors. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-2115