Predictive terrain contour mapping for a legged robot

Most legged robots have to negotiate unknown environments with little or no descriptive terrain data as autonomous terrain mapping facilities for legged robots are limited. A predictive terrain contour mapping strategy is proposed which considers the use of feedforward neural networks to predict terrain contours in unstructured environments based on sample data extracted from the walking surface during the locomotion of Robug III-an eight legged, pneumatically powered walking and climbing robot. In simulation, it is shown that the prediction performance is very acceptable; practical tests are conducted on a prototype robot leg and the results are compared with those obtained in simulation.