Low-dimensional projections for SyCLoP

This paper presents an extension to SyCLoP, a multilayered motion planning framework that has been shown to successfully solve high-dimensional problems with differential constraints. SyCLoP combines traditional sampling-based planning with a high-level decomposition of the workspace through which it attempts to guide a low-level tree of motions. We investigate a generalization of SyCLoP in which the high-level decomposition is defined over a given low-dimensional projected subspace of the state space. We begin with a manually-chosen projection to demonstrate that projections other than the workspace can potentially work well. We then evaluate SyCLoP´s performance with random projections and projections determined from linear dimensionality reduction over elements of the state space, for which the results are mixed. As we will see, finding a useful projection is a difficult problem, and we conclude this paper by discussing the merits and drawbacks of various types of projections.