How to Cut Corners and Get Bounded Convex Curvature
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We describe an algorithm for solving an important geometric problem arising in computer-aided manufacturing. When cutting away a region from a solid piece of material—such as steel, wood, ceramics, or plastic—using a rough tool in a milling machine, sharp convex corners of the region cannot be done properly, but have to be left for finer tools that are more expensive to use. We want to determine a toolpath that maximizes the use of the rough tool. In order to formulate the problem in mathematical terms, we introduce the notion of bounded convex curvature. A region of points in the plane Q has bounded convex curvature if for any point x∈ ∂Q, there is a unit disk U and ε> 0 such that x∈ ∂U and all points in U within distance ε from x are in Q. This translates to saying that as we traverse the boundary ∂Q with the interior of Q on the left side, then ∂Q turns to the left with curvature at most 1. There is no bound on the curvature where ∂Q turns to the right. Given a region of points P in the plane, we are now interested in computing the maximum subset Q⊆ P of bounded convex curvature. The difference in the requirement to left- and right-curvature is a natural consequence of different conditions when machining convex and concave areas of Q. We devise an algorithm to compute the unique maximum such set Q, when the boundary of P consists of n line segments and circular arcs of arbitrary radii. In the general case where P may have holes, the algorithm runs in time O(n2) and uses O(n) space. If P is simply-connected, we describe a faster O(nlog n) time algorithm.
Originalsprog | Engelsk |
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Tidsskrift | Discrete and Computational Geometry |
Vol/bind | 69 |
Sider (fra-til) | 1195–1231, |
ISSN | 0179-5376 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
A preliminary version of this paper was presented at SoCG 2016 []. The authors are part of BARC, Basic Algorithms Research Copenhagen, supported by the VILLUM Foundation grant 16582. Mikkel Abrahamsen is supported by Starting Grant 1054-00032B from the Independent Research Fund Denmark under the Sapere Aude research career programme.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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