Predicting terrain contours using a feed-forward neural network

Stephen Erwin-Wright, David Sanders, Sheng Chen

    Research output: Contribution to journalArticlepeer-review


    Wheeled or tracked vehicles cannot move easily over much of the land surface of the earth. This paper describes research work to create walking machines that are able to travel when the terrain makes wheeled or tracked vehicles ineffective. These legged walking vehicles must be able to negotiate unknown environments with little or no knowledge of the terrain. A predictive terrain contour mapping strategy is proposed that uses a feed-forward neural network trained using a back-propagation algorithm to predict contours based on leg positions and orientations. The strategy is tested using the abilities of a tele-operated eight-legged robot named ‘‘Robug IV’’. Predicted performance is an improvement on previous implementations and a summarised comparison of the results for the four terrains is provided.
    Original languageEnglish
    Pages (from-to)465-472
    JournalEngineering Applications of Artificial Intelligence
    Issue number5-6
    Publication statusPublished - 2003


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