h-NESDF: An Infinitely Differentiable Signed Distance Between Two Sets with Robotic Applications
h-NESDF: An Infinitely Differentiable Signed Distance Between Two Sets with Robotic Applications
This paper introduces h-NESDF, a normal Euclidean signed distance function that is infinitely differentiable for any smoothing parameter h > 0 and reduces to the non-smooth NESDF as h approaches zero. The method supports GPU parallelization and is evaluated through extensive simulations for motion planning tasks involving general, possibly non-convex sets.
The authors propose a mathematically smoothed distance metric for robotic motion planning, validated via simulation; no deployment or hardware results are reported.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2611.03944v1 Announce Type: new Abstract: Computing distances between sets is important in many robotic applications, such as motion planning and control. In many applications, derivatives of the distance are needed as well. It is well known that the Euclidean distance is not always differentiable, which create issues to algorithms that need a derivative. In this paper, we introduce NESDF, the Normal Euclidean
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Source：arXiv Robotics — research abstracts · arxiv.org