Type of Document	Thesis
Author	Sadhukhan, Debangshu
URN	etd-04092004-171647
Title	Autonomous Ground Vehicle Terrain Classification Using Internal Sensors
Degree	Master of Science
Department	Mechanical Engineering, Department of
Advisory Committee	Advisor Name Title Carl A Moore Committee Chair Emmanuel Collins Committee Member Rodney Roberts Committee Member
Keywords	Terrain Signatures Probabilistic Neural Network Pattern Classification FFT
Date of Defense	2004-03-16
Availability	unrestricted
Abstract 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.
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