A fast high-precision six-degree-of-freedom relative position sensor

Lasers are commonly used in high-precision measurement and profiling systems. Some laser measurement systems are based on interferometry principles, and others are based on active triangulation, depending on requirements of the application. This paper describes an active triangulation laser measurement system for a specific application wherein the relative position of two fixed, rigid mechanical components is to be measured dynamically with high precision in six degrees of freedom (DOF). Potential applications include optical systems with feedback to control for mechanical vibration, such as target acquisition devices with multiple focal planes. The method uses an array of several laser emitters mounted on one component. The lasers are directed at a reflective surface on the second component. The reflective surface consists of a piecewise-planar pattern such as a pyramid, or more generally a curved reflective surface such as a hyperbolic paraboloid. The reflected spots are sensed at 2-dimensional photodiode arrays on the emitter component. Changes in the relative position of the emitter component and reflective surface will shift the location of the reflected spots within photodiode arrays. Relative motion in any degree of freedom produces independent shifts in the reflected spot locations, allowing full six-DOF relative position determination between the two component positions. Response time of the sensor is limited by the read-out rate of the photodiode arrays. Algorithms are given for position determination with limits on uncertainty and sensitivity, based on laser and spot-sensor characteristics, and assuming regular surfaces. Additional uncertainty analysis is achievable for surface irregularities based on calibration data.

[1]  J P Hessling,et al.  A novel method of estimating dynamic measurement errors , 2006 .

[2]  Tilo Pfeifer,et al.  Optical Methods for Dimensional Metrology in Production Engineering , 2002 .

[3]  Yuri Chugui,et al.  3D optical measuring technologies for industrial applications , 2011, Optical Metrology.

[4]  Evgeny V. Sysoev,et al.  3D Optical Measuring Systems and Laser Technologies for Scientific and Industrial Applications , 2013 .

[5]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[6]  Shu-Li Sun,et al.  Multi-sensor optimal information fusion Kalman filter , 2004, Autom..

[7]  Gary B. Hughes,et al.  Local phase control for a planar array of fiber laser amplifiers , 2015, SPIE Optical Engineering + Applications.

[8]  J P Hessling,et al.  A novel method of evaluating dynamic measurement uncertainty utilizing digital filters , 2009 .

[9]  Gary B. Hughes,et al.  Toward directed energy planetary defense , 2014 .

[10]  Gary B. Hughes Algorithms for sensor chip alignment to blind datums , 2006, J. Electronic Imaging.

[11]  O. M. Vasilevskyi,et al.  An approach to the evaluation of dynamic uncertainty in measurement using non-statistical techniques , 2014 .

[12]  Xiangqian Jiang,et al.  Multisensor data fusion in dimensional metrology , 2009 .

[13]  J P Hessling,et al.  Dynamic metrology—an approach to dynamic evaluation of linear time-invariant measurement systems , 2008 .

[14]  O. M. Vasilevskyi A frequency method for dynamic uncertainty evaluation of measurement during modes of dynamic operation , 2015 .

[15]  A. Amthor,et al.  Bayes filter for dynamic coordinate measurements – Accuracy improvment, data fusion and measurement uncertainty evaluation , 2013 .

[16]  Juho Kannala,et al.  A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Y. Bar-Shalom,et al.  A generalized S-D assignment algorithm for multisensor-multitarget state estimation , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Jean-François Fontaine,et al.  Systematic error correction of a 3D laser scanning measurement device , 2011 .

[19]  Shu-li Sun,et al.  Multi-sensor optimal information fusion Kalman filters with applications , 2004 .

[20]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[22]  Stephane Ruel,et al.  TriDAR: A HYBRID SENSOR FOR EXPLOITING THE COMPLEMENTARY NATURE OF TRIANGULATION AND LIDAR TECHNOLOGIES , 2005 .

[23]  Ning Xiong,et al.  Multi-sensor management for information fusion: issues and approaches , 2002, Inf. Fusion.

[24]  Qingchang Tan,et al.  A Flexible Calibration Method Using the Planar Target with a Square Pattern for Line Structured Light Vision System , 2014, PloS one.

[25]  Clemens Elster,et al.  Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system , 2008 .

[26]  Yu. V. Chugui Three-dimensional optoelectronic measurement systems and laser technologies for scientific and industrial applications , 2015 .

[27]  Yuri V. Chugui,et al.  Optical electronic measuring systems and laser technologies for scientific and industrial applications , 2006, International Symposium on Instrumentation and Control Technology.

[28]  Nadir Murru,et al.  An algorithm for concurrent estimation of time-varying quantities , 2012 .

[29]  J. Hessling Propagation of dynamic measurement uncertainty , 2011 .

[30]  Marco Gaiani,et al.  Evaluating the performance of close-range 3D active vision systems for industrial design applications , 2004 .

[31]  Marc Rioux,et al.  Optimized position sensors for flying-spot active triangulation systems , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[32]  C. English,et al.  Real-Time Dynamic Pose Estimation Systems in Space : Lessons Learned for System Design and Performance Evaluation , 2011 .