TY - GEN
T1 - Unbiased, scalable sampling of closed kinematic chains
AU - Zhang, Yajia
AU - Hauser, Kris
AU - Luo, Jingru
PY - 2013/11/14
Y1 - 2013/11/14
N2 - This paper presents a Monte Carlo technique for sampling configurations of a kinematic chain according to a specified probability density while accounting for loop closure constraints. A key contribution is a method for sampling sub-loops in unbiased fashion using analytical inverse kinematics techniques. Sub-loops are then iterated across the chain to produce samples for the entire chain. The method is demonstrated to scale well to high-dimensional chains (>200DOFs) and is applied to flexible 2D chains, protein molecules, and robots with multiple closed-chains.
AB - This paper presents a Monte Carlo technique for sampling configurations of a kinematic chain according to a specified probability density while accounting for loop closure constraints. A key contribution is a method for sampling sub-loops in unbiased fashion using analytical inverse kinematics techniques. Sub-loops are then iterated across the chain to produce samples for the entire chain. The method is demonstrated to scale well to high-dimensional chains (>200DOFs) and is applied to flexible 2D chains, protein molecules, and robots with multiple closed-chains.
UR - http://www.scopus.com/inward/record.url?scp=84887294594&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887294594&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2013.6630911
DO - 10.1109/ICRA.2013.6630911
M3 - Conference contribution
AN - SCOPUS:84887294594
SN - 9781467356411
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 2459
EP - 2464
BT - 2013 IEEE International Conference on Robotics and Automation, ICRA 2013
T2 - 2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Y2 - 6 May 2013 through 10 May 2013
ER -