Abstract
This study presents a framework for assessing the navigation speed of unmanned robots by combining information extracted from the 3D world model of natural terrain with regional traversability based on the fuzzy technique. The proposed method divides the world model into several patches, extracts the slope and roughness of each terrain patch along four heading directions, and then uses them to evaluate the level of difficulty associated with the traversal. The slope is estimated through curved surface fitting, and roughness is obtained using fractal-based analysis together with another two RMS metrics. As navigation systems can cope with the imprecision and uncertainty of input data, we modify the Seraji’s fuzzy-based measure to assess the traversability and navigation speed of each patch for path planning. The proposed method is tested on both fractal and real terrain to verify its effectiveness.
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