Terrain Estimation using Internal Sensors

D. Sadhukhan, C. Moore, and E. Collins (USA)


Mobile robot, terrain identification, neural network, pattern classification, pattern recognition, and XUV.


The US Army designed Experimental Unmanned Vehicle (XUV) [1], shown in Figure 1, is a semi-autonomous unmanned ground vehicle (UGV) that uses high fidelity sensors for reconnaissance, surveillance, and target acquisition. One of the goals of XUV research is to develop autonomous mobility that enables the vehicle to maneuver over rugged terrain as part of a mixed manned and unmanned vehicle group. As part of this goal, the XUV must be able to autonomously navigate over different terrains at high speeds. 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 to categorize the traversability of the terrain. Others have used classical terramechanics equations to identify key terrain parameters. This paper presents a novel algorithm1 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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