Vision-guided feedback control of a mobile robot with compressive measurements and side information

In this work, we present feedback control laws for vision guided navigation of a mobile robot. The robot is modeled as a cart that can move along a straight line, and has two vision sensors onboard. The primary vision sensor is a high resolution single-pixel camera (SPC), based on principles of compressive sensing, for capturing images. Additionally, there is a low-resolution sensor that provides coarse measurements. In this work, we consider a simple scenario in which the target is modeled as a straight line segment on a plane. The main contribution of this work is the formulation of control laws directly from the compressed measurements, obtained from the SPC. Therefore, the reconstruction of the target image is sidestepped, leading to a reduction in the amount of data acquired for control.

[1]  Richard G. Baraniuk,et al.  An Architecture for Compressive Imaging , 2006, 2006 International Conference on Image Processing.

[2]  T. Başar,et al.  Solutions to a class of minimax decision problems arising in communication systems , 1984, The 23rd IEEE Conference on Decision and Control.

[3]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[4]  Wei Dai,et al.  Observability of a Linear System Under Sparsity Constraints , 2012, IEEE Transactions on Automatic Control.

[5]  S. Shankar Sastry,et al.  Vision guided navigation for a nonholonomic mobile robot , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[6]  Tamer Basar,et al.  Sparsity based feedback design: A new paradigm in opportunistic sensing , 2011, Proceedings of the 2011 American Control Conference.

[7]  P. Sen,et al.  Compressed mapping of communication signal strength , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[8]  M. Wakin,et al.  Exact topology identification of large-scale interconnected dynamical systems from compressive observations , 2011, Proceedings of the 2011 American Control Conference.

[9]  S. Shankar Sastry,et al.  An Invitation to 3-D Vision , 2004 .

[10]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[11]  David V. Anderson,et al.  Compressive Sensing on a CMOS Separable-Transform Image Sensor , 2010, Proceedings of the IEEE.

[12]  Daniel E. Quevedo,et al.  Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit , 2013, ArXiv.

[13]  Daniel E. Quevedo,et al.  Sparse Packetized Predictive Control for Networked Control Over Erasure Channels , 2013, IEEE Transactions on Automatic Control.

[14]  Avinash C. Kak,et al.  Vision for Mobile Robot Navigation: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  J. Romberg,et al.  Imaging via Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[16]  Bo Song,et al.  Compressive feedback based non-vector space control , 2012, 2012 American Control Conference (ACC).

[17]  Daniel E. Quevedo,et al.  Compressive sampling for networked feedback control , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Tamer Basar,et al.  A complete characterization of minimax and maximin encoder- decoder policies for communication channels with incomplete statistical description , 1985, IEEE Trans. Inf. Theory.

[19]  Tamer Basar,et al.  The Gaussian test channel with an intelligent jammer , 1983, IEEE Trans. Inf. Theory.

[20]  S. Shankar Sastry,et al.  An Invitation to 3-D Vision: From Images to Geometric Models , 2003 .

[21]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[22]  H. Opower.  Multiple view geometry in computer vision , 2002 .

[23]  Amit K. Roy-Chowdhury,et al.  Collaborative Sensing in a Distributed PTZ Camera Network , 2012, IEEE Transactions on Image Processing.

[24]  Francisco Bonin-Font,et al.  Visual Navigation for Mobile Robots: A Survey , 2008, J. Intell. Robotic Syst..

[25]  Volkan Cevher,et al.  Compressed sensing for multi-view tracking and 3-D voxel reconstruction , 2008, 2008 15th IEEE International Conference on Image Processing.

[26]  Lu Gan.  Block Compressed Sensing of Natural Images , 2007, 2007 15th International Conference on Digital Signal Processing.

[27]  Daniel E. Quevedo,et al.  Packetized predictive control for rate-limited networks via sparse representation , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[28]  Richard G. Baraniuk,et al.  Democracy in Action: Quantization, Saturation, and Compressive Sensing , 2011 .

[29]  T. Başar,et al.  Solutions to a class of minimax decision problems arising in communication systems , 1986 .

[30]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[31]  Yasamin Mostofi,et al.  Compressive cooperative sensing and mapping in mobile networks , 2009, 2009 American Control Conference.

[32]  Rama Chellappa,et al.  Compressive Acquisition of Dynamic Scenes , 2010, ECCV.

[33]  Volkan Cevher,et al.  Compressive Sensing for Background Subtraction , 2008, ECCV.

[34]  Tyrone L. Vincent,et al.  On the observability of linear systems from random, compressive measurements , 2010, 49th IEEE Conference on Decision and Control (CDC).