Multiresolution rough terrain motion planning

We describe a new approach to the problem of motion planning for mobile robots on natural, nonhomogenous terrain. Our approach computes a multiresolution representation of the terrain using wavelets, and hierarchically plans the path through sections which are well approximated on coarser levels and relatively smooth. Unlike most methods, the hierarchical approximation errors are used explicitly in a cost function to distinguish preferred terrain sections. The error is computed using the corresponding wavelet coefficients. The path planning algorithm uses a new nonscalar path cost measure based on the sorted terrain costs along the path. This measure can be incorporated into standard global path search algorithms and yields intuitively good paths. Additional constraints for specific robots can be integrated into this approach for efficient hierarchical motion planning on rough terrain. We present experimental results for real terrain data