Pregled bibliografske jedinice broj: 1160485
Inverse Kinematics as Low-Rank Euclidean Distance Matrix Completion
Inverse Kinematics as Low-Rank Euclidean Distance Matrix Completion // Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’20), Workshop on Bringing Geometric Methods to Robot Learning, Optimization and Control
Las Vegas (NV), Sjedinjene Američke Države, 2020. 09196704, 2 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), ostalo)
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Naslov
Inverse Kinematics as Low-Rank Euclidean Distance
Matrix Completion
Autori
Marić, Filip ; Giamou, Matthew ; Petrović, Ivan ; Kelly, Jonathan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo
Izvornik
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’20), Workshop on Bringing Geometric Methods to Robot Learning, Optimization and Control
/ - , 2020
Skup
IROS 2020 Workshop on Bringing Geometric Methods to Robot Learning, Optimization and Control (IROS’20)
Mjesto i datum
Las Vegas (NV), Sjedinjene Američke Države, 29.10.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Inverse Kinematics, Distance Geometry
Sažetak
The majority of inverse kinematics (IK) algorithms search for solutions in a configuration space defined by joint angles. However, the kinematics of many robots can also be described in terms of distances between rigidly-attached points, which collectively form a Euclidean distance matrix. This alternative geometric description of the kinematics reveals an elegant equivalence between IK and the problem of low-rank matrix completion. We use this connection to implement a novel Riemannian optimization-based solution to IK for various articulated robots with symmetric joint angle constraints.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
