Optimized Synthesis of Snapping Fixtures
September 12, 2019 Β· Declared Dead Β· π Workshop on the Algorithmic Foundations of Robotics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tom Tsabar, Efi Fogel, Dan Halperin
arXiv ID
1909.05953
Category
cs.CG: Computational Geometry
Cross-listed
cs.RO
Citations
2
Venue
Workshop on the Algorithmic Foundations of Robotics
Last Checked
3 months ago
Abstract
Fixtures for constraining the movement of parts have been extensively investigated in robotics, since they are essential for using robots in automated manufacturing. This paper deals with the design and optimized synthesis of a special type of fixtures, which we call \emph{snapping fixtures}. Given a polyhedral workpiece $P$ with $n$ vertices and of constant genus, which we need to hold, a snapping fixture is a semi-rigid polyhedron $G$, made of a palm and several fingers, such that when $P$ and $G$ are well separated, we can push $P$ toward $G$, slightly bending the fingers of $G$ on the way (exploiting its mild flexibility), and obtain a configuration, where $G$ is back in its original shape and $P$ and $G$ are inseparable as rigid bodies. We prove the minimal closure conditions under which such fixtures can hold parts, using Helly's theorem. We then introduce an algorithm running in $O(n^3)$ time that produces a snapping fixture, minimizing the number of fingers and optimizing additional objectives, if a snapping fixture exists. We also provide an efficient and robust implementation of a simpler version of the algorithm, which produces the fixture model to be 3D printed and runs in $O(n^4)$ time. We describe two applications with different optimization criteria: Fixtures to hold add-ons for drones, where we aim to make the fixture as lightweight as possible, and small-scale fixtures to hold precious stones in jewelry, where we aim to maximize the exposure of the stones, namely minimize the obscuring of the workpiece by the fixture.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computational Geometry
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Dynamic Planar Convex Hull
R.I.P.
π»
Ghosted
TEMPO: Feature-Endowed TeichmΓΌller Extremal Mappings of Point Clouds
R.I.P.
π»
Ghosted
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization
R.I.P.
π»
Ghosted
Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal
R.I.P.
π»
Ghosted
Momen(e)t: Flavor the Moments in Learning to Classify Shapes
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted