Yu Zihan, Ge Qiaode Jeffrey, Langer Mark P, Arbab Mona
Department of Mechanical Engineering, Stony Brook University, Stony Brook, NY 11794.
Department of Radiation Oncology, Indiana University, Indianapolis, IN 46202.
J Mech Robot. 2024 Aug;16(8). doi: 10.1115/1.4064281. Epub 2024 Jan 12.
This paper studies the statistical concept of confidence region for a set of uncertain planar displacements with a certain level of confidence or probabilities. Three different representations of planar displacements are compared in this context and it is shown that the most commonly used representation based on the coordinates of the moving frame is the least effective. The other two methods, namely the exponential coordinates and planar quaternions, are equally effective in capturing the group structure of SE(2). However, the former relies on the exponential map to parameterize an element of SE(2), while the latter uses a quadratic map, which is often more advantageous computationally. This paper focus on the use of planar quaternions to develop a method for computing the confidence region for a given set of uncertain planar displacements. Principal component analysis (PCA) is another tool used in our study to capture the dominant direction of movements. To demonstrate the effectiveness of our approach, we compare it to an existing method called rotational and translational confidence limit (RTCL). Our examples show that the planar quaternion formulation leads to a swept volume that is more compact and more effective than the RTCL method, especially in cases when off-axis rotation is present.
本文研究了具有一定置信度或概率的一组不确定平面位移的置信区域的统计概念。在此背景下,比较了平面位移的三种不同表示形式,结果表明,基于移动坐标系坐标的最常用表示形式效率最低。另外两种方法,即指数坐标和平面四元数,在捕捉SE(2)的群结构方面同样有效。然而,前者依赖指数映射来参数化SE(2)的一个元素,而后者使用二次映射,这在计算上通常更具优势。本文重点研究使用平面四元数来开发一种计算给定不确定平面位移集的置信区域的方法。主成分分析(PCA)是我们研究中用于捕捉运动主导方向的另一种工具。为了证明我们方法的有效性,我们将其与一种称为旋转和平移置信极限(RTCL)的现有方法进行了比较。我们的例子表明,平面四元数公式产生的扫掠体积比RTCL方法更紧凑、更有效,特别是在存在离轴旋转的情况下。