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Calibration of omnidirectional wheeled mobile robots: method and experiments

Published online by Cambridge University Press:  11 April 2013

Yaser Maddahi ,
Ali Maddahi  and
Nariman Sepehri
Yaser Maddahi
Affiliation:
Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada, R3T 5N5
Ali Maddahi
Affiliation:
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Nariman Sepehri*
Affiliation:
Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada, R3T 5N5
*
*Corresponding author. E-mail: [email protected].
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Summary

Odometry errors, which occur during wheeled mobile robot movement, are inevitable as they originate from hard-to-avoid imperfections such as unequal wheels diameters, joints misalignment, backlash, slippage in encoder pulses, and much more. This paper extends the method, developed previously by the authors for calibration of differential mobile robots, to reduce positioning errors for the class of mobile robots having omnidirectional wheels. The method is built upon the easy to construct kinematic formulation of omnidirectional wheels, and is capable of compensating both systematic and non-systematic errors. The effectiveness of the method is experimentally investigated on a prototype three-wheeled omnidirectional mobile robot. The validations include tracking unseen trajectories, self-rotation, as well as travelling over surface irregularities. Results show that the method is very effective in improving position errors by at least 68%. Since the method is simple to implement and has no assumption on the sources of errors, it should be considered seriously as a tool for calibrating omnidirectional mobile having any number of wheels.

Keywords

Omnidirectional wheeled mobile robotsSystematic errorsNon-systematic errorsBenchmark testsError improvementPerformance analysis
Type
Articles
Information
Robotica , Volume 31 , Issue 6 , September 2013 , pp. 969 - 980
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Blumrich, J. F., Omnidirectional Vehicle. United States Patent, No. 3,789,947 (US Patent Office, Alexandria, VA, 1974).Google Scholar
2.Ilou, B. E., Wheels for a Course Stable Self-Propelling Vehicle Movable in Any Desired Direction on the Ground or Some Other Base, United States Patent, No. 3,876, 255, (US Patent Office, Alexandria, VA, 1975).Google Scholar
3.West, M. and Asada, H., “Design of ball wheel mechanisms for omnidirectional vehicles with full mobility and invariant kinematics,” J. Mech. Des. 119 (2), 153–161 (1997).CrossRefGoogle Scholar
4.Wada, M. and Mory, S., “Holonomic and Omnidirectional Vehicle with Conventional Tires,” In: Proceedings of IEEE International Conference on Robotics and Automation (April 22–28, 1996) pp. 3671–3676.Google Scholar
5.BIPM-JCGM, International Vocabulary of Metrology–Basic and General Concepts and Associated Terms (VIM), 3rd ed., JCGM 200:2008 (BIPM-JCGM, Cedex, France, 2008), pp. 16–34.Google Scholar
6.Maddahi, Y. and Maddahi, A., “Mobile Robots Experimental Analysis Based on Kinematics,” In: Proceedings of the International Conference on Simulation, Modeling and Optimization, Turkey (2004) pp. 1662–1667.Google Scholar
7.Jung-Hwan, K., Dong-Choon, H., Yong-Woo, J. and Eun-Soo, K., “Intelligent Mobile Robot System for Path Planning Using Stereo Camera-Based Geometry Information,” In: Proceeding of SPIE – International Society for Optical Engineering, Optical Metrology: vol. 6006, Boston, Massachusetts (2005) pp. 232–243.Google Scholar
8.Piaggio, M., Sgorbissa, A. and Zaccaria, R., “Navigation and localization for service mobile robots based on active beacons,” J. Syst. Sci. 27 (4), 71–83 (2001).Google Scholar
9.Bury, B. and Hope, J. C., “Autonomous Mobile Robot Navigation Using a Low-cost Fibre Optic Gyroscope,” In: Proceeding of the International Conference on Intelligent Autonomous Vehicles, Espoo, Finland (1995) pp. 39–43.Google Scholar
10.Kwon, W., Roh, K. S. and Sung, H. K., “Particle Filter-Based Heading Estimation Using Magnetic Compasses for Mobile Robot Navigation,” In: Proceedings of the International Conference on Robotics and Automation, Orlando, Florida (May 15–19, 2006) pp. 2705–2712.Google Scholar
11.Roy, N. and Thrun, S., “Online Self-Calibration for Mobile Robots,” In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, Detroit, Michigan (May 10–15, 1999) pp. 2292–2297.Google Scholar
12.Maddahi, Y., Sepehri, N., Maddahi, A. and Abdolmohammadi, M., “Calibration of wheeled mobile robots with differential drive mechanisms: An experimental approach,” J. Robot. 30 (6), 1029–1039 (2012).CrossRefGoogle Scholar
13.Borenstein, J. and Feng, L., “UMBmark: A Benchmark Test for Measuring Dead reckoning Errors in Mobile Robots,” In: Proceedings of the SPIE Conference on Mobile Robots, Newton, Massachusetts (1995) pp. 178–186.Google Scholar
14.Borenstein, J. and Feng, L., “UMBmark: A Method for Measuring, Comparing, and Correcting Dead-Reckoning Errors in Mobile Robots,” Technical Report (The University of Michigan UM-MEAM-94–22, 1994).Google Scholar
15.Borenstein, J. and Feng, L., “Measurement and correction of systematic odometry errors in mobile robots,” IEEE Trans. Intell. Robots Syst. 12 (6), 869–880 (1996).Google Scholar
16.Maddahi, Y., “Design and Laboratory Tests of Wheeled Mobile Robots,” In: International Conference on System Science and Simulation in Engineering, Tenerife, Spain (2005) pp. 186–191.Google Scholar
17.Han, K. H., Kin, H. K. and Lee, J. S., “The Sources of Position Errors of Omni-Directional Mobile Robot with Mecanum Wheel,” In: IEEE International Conference on Systems, Man and Cybernetics, Piscataway, New Jersey (2010) pp. 581–586.Google Scholar
18.Lee, J.-H and Hashimoto, H., “Controlling mobile robots in distributed intelligent sensor network,” IEEE Trans. Ind. Electron. 50 (5), 890–902 (2003).CrossRefGoogle Scholar
19.Szemes, P. T., Hashimoto, H. and Korondi, P., “Pedestrian-behavior-based mobile agent control in intelligent space,” IEEE Trans. Instrum. Meas. 54 (6), 2250–2257 (2005).CrossRefGoogle Scholar
20.Tsai, R., “A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE Trans. Robot. Automat. 3, 323–344 (1987).CrossRefGoogle Scholar
21.Chang, W.-C. and Chu, P.-R., “An Intelligent Space for Mobile Robot Navigation with On-line Calibrated Vision Sensors,” In: International Conference on Control, Automation, Robotics and Vision, Singapore (Dec 7–10, 2010) pp. 1452–1457.Google Scholar
22.Siegwart, R. and Nourbakhsh, I. R., Introduction to Autonomous Mobile Robots (MIT Press, Cambridge, Massachusetts, 2004).Google Scholar
23.Wuensch, K. L., Encyclopedia of Statistics in Behavioral Science (Wiley, Chichester, UK, 2005).Google Scholar