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Path Planning with Divergence-Based Distance Functions

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons180767

Chen,  Renjie
Computer Graphics, MPI for Informatics, Max Planck Society;

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arXiv:1708.02845.pdf
(Preprint), 6MB

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Citation

Chen, R., Gotsman, C., & Hormann, K. (2017). Path Planning with Divergence-Based Distance Functions. Retrieved from http://arxiv.org/abs/1708.02845.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-063A-8
Abstract
Distance functions between points in a domain are sometimes used to automatically plan a gradient-descent path towards a given target point in the domain, avoiding obstacles that may be present. A key requirement from such distance functions is the absence of spurious local minima, which may foil such an approach, and this has led to the common use of harmonic potential functions. Based on the planar Laplace operator, the potential function guarantees the absence of spurious minima, but is well known to be slow to numerically compute and prone to numerical precision issues. To alleviate the first of these problems, we propose a family of novel divergence distances. These are based on f-divergence of the Poisson kernel of the domain. We define the divergence distances and compare them to the harmonic potential function and other related distance functions. Our first result is theoretical: We show that the family of divergence distances are equivalent to the harmonic potential function on simply-connected domains, namely generate paths which are identical to those generated by the potential function. The proof is based on the concept of conformal invariance. Our other results are more practical and relate to two special cases of divergence distances, one based on the Kullback-Leibler divergence and one based on the total variation divergence. We show that using divergence distances instead of the potential function and other distances has a significant computational advantage, as, following a pre-processing stage, they may be computed up to an order of magnitude faster than the others when taking advantage of certain sparsity properties of the Poisson kernel. Furthermore, the computation is "embarrassingly parallel", so may be implemented on a GPU with up to three orders of magnitude speedup.