Online motion planning over uneven terrain with walking primitives and regression

Abstract

This paper introduces an online motion planning algorithm and a motion generation methodology for underactuated dynamic planar walking on uneven terrain. The key idea is to utilize a database of Motion Primitives and use them as training examples in a regression methodology, which is utilized when there is no match between the terrain variation and the Motion Primitives in the database. Among the key features which enable the algorithm to be suitable for real-time purposes is the proposed best first graph search approach and the small inference time of the regression methodology, which in this paper is the Gaussian Process.