Self-indexed motion planning

Motion planning is a central problem for robotics. A practical way to address it is building a graph-based representation (a roadmap) capturing the connectivity of the configuration space. The Probabilistic Road Map (PRM) is perhaps the most widely used method by the robotics community based on that idea. A key sub-problem for discovering and maintaining a collision-free path in the PRM is inserting new sample points and connecting them with the $k$-nearest neighbors in the previous set. Instead of following the usual solution of indexing the points and then building the PRM with successive $k$-NN queries, we propose an approximation of the $k$-Nearest Neighbors Graph using the PRM as a self-index. The motivation for this construction comes from the Approximate Proximity Graph (APG), which is an index for searching proximal objects in a metric space. Using this approach the estimation of the $k$-NN is improved while simultaneously reducing the total time and space needed to compute a PRM. We present simulations for high-dimensional configuration spaces with and without obstacles, showing significant improvement over the standard techniques used by the robotics community.     

A remark concerning divergence accuracy order for H(div)-conforming finite element flux approximations

The construction of finite element approximations in $\mathbf{H}(\text{div},\Omega )$ usually requires the Piola transformation to map vector polynomials from a master element to vector fields in the elements of a partition of the region $\Omega$. It is known that degradation may occur in convergence order if non affine geometric mappings are used. On this point, we revisit a general procedure for the improvement of two-dimensional flux approximations discussed in a recent paper of this journal (Comput. Math. Appl. 74 (2017) 3283–3295). The starting point is an approximation scheme, which is known to provide $L^2$-errors with accuracy of order $k+1$ for sufficiently smooth flux functions, and of order $r+1$ for flux divergence. An example is $R{T}_k$ spaces on quadrilateral meshes, where $r=k$ or $k?1$ if linear or bilinear geometric isomorphisms are applied. Furthermore, the original space is required to be expressed by a factorization in terms of edge and internal shape flux functions. The goal is to define a hierarchy of enriched flux approximations to reach arbitrary higher orders of divergence accuracy $r+n+1$ as desired, for any $n\ge 1$. The enriched versions are defined by adding higher degree internal shape functions of the original family of spaces at level $k+n$, while keeping the original border fluxes at level $k$. The case $n=1$ has been discussed in the mentioned publication for two particular examples. General stronger enrichment $n>1$ shall be analyzed and applied to Darcy’s flow simulations, the global condensed systems to be solved having same dimension and structure of the original scheme.     

Two-grid method for the two-dimensional time-dependent Schrödinger equation by the finite element method

In this paper, we construct a backward Euler full-discrete two-grid finite element scheme for the two-dimensional time-dependent Schrödinger equation. With this method, the solution of the original problem on the fine grid is reduced to the solution of same problem on a much coarser grid together with the solution of two Poisson equations on the same fine grid. We analyze the error estimate of the standard finite element solution and the two-grid solution in the $H^1$ norm. It is shown that the two-grid algorithm can achieve asymptotically optimal approximation as long as the mesh sizes satisfy $H=O\left(h^{\frac{k}{k+1}}\right)$. Finally, a numerical experiment indicates that our two-grid algorithm is more efficient than the standard finite element method.