Nonparametric Second-order Theory of Error Propagation on Motion Groups

被引:95
作者
Wang, Yunfeng [1 ]
Chirikjian, Gregory S. [2 ]
机构
[1] Kean Coll New Jersey, Dept Mech Engn, Ewing, NJ 08628 USA
[2] Johns Hopkins Univ, Dept Mech Engn, Baltimore, MD 21218 USA
关键词
recursive error propagation; Euclidean group; spatial uncertainty;
D O I
10.1177/0278364908097583
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Error propagation on the Euclidean motion group arises in a number of areas such as in dead reckoning errors in mobile robot navigation and joint errors that accumulate from the base to the distal end of kinematic chains such as manipulators and biological macromolecules. We address error propagation in rigid-body poses in a coordinate-free way. In this paper we show how errors propagated by convolution on the Euclidean motion group, SE(3), can be approximated to second order using the theory of Lie algebras and Lie groups. We then show how errors that are small (but not so small that linearization is valid) can be propagated by a recursive formula derived here. This formula takes into account errors to second order, whereas prior efforts only considered the first-order case. Our formulation is non-parametric in the sense that it will work for probability density functions of any form (not only Gaussians). Numerical tests demonstrate the accuracy of this second-order theory in the context of a manipulator arm and a flexible needle with bevel tip.
引用
收藏
页码:1258 / 1273
页数:16
相关论文
共 23 条
[1]  
Alterovitz R., 2007, ROBOTICS SCI SYSTEMS
[2]  
Anderson T. W., 2005, INTRO MULTIVARIATE S
[3]  
[Anonymous], 2005, Probabilistic Robotics(IntelligentRobotics and Autonomous Agents)
[4]  
Baker HF, 1904, P LOND MATH SOC, V1, P247
[5]  
Campbell J.E., 1897, Proc. London Math. Soc., V29, P14
[6]  
Chirikjian G.S., 2001, ENG APPL NONCOMMUTAT
[7]  
CHIRIKJIAN GS, 2009, STOCHASTIC IN PRESS
[8]  
Craig J.J., 2005, INTRO ROBOTICS MECH, V3
[9]  
Hausdorff F., 1906, BER VERH KGL SA CHS, V58, P19
[10]   An algorithmic introduction to numerical simulation of stochastic differential equations [J].
Higham, DJ .
SIAM REVIEW, 2001, 43 (03) :525-546