TY - GEN
T1 - Physically balancing multi-articulated objects
AU - Baek, Nakhoon
AU - Yoo, Kwan Hee
PY - 2011
Y1 - 2011
N2 - In many fields of computer science and other engineering areas, we often need to balance multi-articulated structures. In this paper, we formalize this kind of balancing problem from a more physical and theoretical point of view. Through describing details of all the solution steps, we finally represent a set of algorithms to automatically balance multi-articulated objects with tree topologies. Given the geometric configurations and masses at the leaf nodes of target multi-articulated objects, our algorithms achieve their balanced state through adjusting the mass of each node. To minimize the mass changes from the initial configuration, we use constraints of minimizing the norms of the mass differences between the initial masses and the final balanced masses. Actually, we use three different metrics, l 1, l 2 and l ∞ norms. These norms show slightly different behaviors in the minimization process, and users can select one of them according to their preferences and application purposes. We show all the details of algorithms, their time complexity analyses, and experimental results.
AB - In many fields of computer science and other engineering areas, we often need to balance multi-articulated structures. In this paper, we formalize this kind of balancing problem from a more physical and theoretical point of view. Through describing details of all the solution steps, we finally represent a set of algorithms to automatically balance multi-articulated objects with tree topologies. Given the geometric configurations and masses at the leaf nodes of target multi-articulated objects, our algorithms achieve their balanced state through adjusting the mass of each node. To minimize the mass changes from the initial configuration, we use constraints of minimizing the norms of the mass differences between the initial masses and the final balanced masses. Actually, we use three different metrics, l 1, l 2 and l ∞ norms. These norms show slightly different behaviors in the minimization process, and users can select one of them according to their preferences and application purposes. We show all the details of algorithms, their time complexity analyses, and experimental results.
KW - balancing
KW - minimization
KW - tree-topology
UR - http://www.scopus.com/inward/record.url?scp=84055221828&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-27204-2_22
DO - 10.1007/978-3-642-27204-2_22
M3 - Conference contribution
AN - SCOPUS:84055221828
SN - 9783642272035
T3 - Communications in Computer and Information Science
SP - 185
EP - 190
BT - Multimedia, Computer Graphics and Broadcasting - Int. Conf. MulGraB 2011, Held as Part of the Future Generation Information Technology Conf. FGIT 2011, in Conjunction with GDC 2011, Proc.
T2 - 2011 International Conference on Multimedia, Computer Graphics and Broadcasting, MulGraB 2011, Held as Part of the 3rd International Mega-Conference on Future-Generation Information Technology, FGIT 2011, in Conjunction with GDC 2011
Y2 - 8 December 2011 through 10 December 2011
ER -