New asymptotic blends and Taylor extrapolations improve two families of distance-to-boundary estimators, demonstrated on 2D images.
Walk on Spheres for PDE-based Path Planning
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abstract
In this paper, we investigate the Walk on Spheres algorithm (WoS) for motion planning in robotics. WoS is a Monte Carlo method to solve the Dirichlet problem developed in the 50s by Muller and has recently been repopularized by Sawhney and Crane, who showed its applicability for geometry processing in volumetric domains. This paper provides a first study into the applicability of WoS for robot motion planning in configuration spaces, with potential fields defined as the solution of screened Poisson equations. The experiments in this paper empirically indicate the method's trivial parallelization, its dimension-independent convergence characteristic of $O(1/N)$ in the number of walks, and a validation experiment on the RR platform.
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Accuracy Improvements for Convolutional and Differential Distance Function Approximations
New asymptotic blends and Taylor extrapolations improve two families of distance-to-boundary estimators, demonstrated on 2D images.