Motion Polynomials Admitting a Factorization with Linear Factors
September 06, 2022 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zijia Li, Hans-Peter SchrΓΆcker, Mikhail Skopenkov, Daniel F. Scharler
arXiv ID
2209.02306
Category
math.RA
Cross-listed
cs.RO
Citations
2
Last Checked
3 months ago
Abstract
Motion polynomials (polynomials over the dual quaternions with nonzero real norm) describe rational motions. We present a necessary and sufficient condition for reduced bounded motion polynomials to admit factorizations into monic linear factors, and we give an algorithm to compute them. We can use those linear factors to construct mechanisms because the factorization corresponds to the decomposition of the rational motion into simple rotations or translations. Bounded motion polynomials always admit a factorization into linear factors after multiplying with a suitable real or quaternion polynomial. Our criterion for factorizability allows us to improve on earlier algorithms to compute a suitable real or quaternion polynomial co-factor.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β math.RA
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Good Integers and Applications in Coding Theory
R.I.P.
π»
Ghosted
Generalized iterated-sums signatures
R.I.P.
π»
Ghosted
Tropical time series, iterated-sums signatures and quasisymmetric functions
R.I.P.
π»
Ghosted
Wajsberg algebras arising from binary block codes
R.I.P.
π»
Ghosted
Constacyclic and Quasi-Twisted Hermitian Self-Dual Codes over Finite Fields
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