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Calibration of wheeled mobile robots with differential drive mechanisms: an experimental approach

Published online by Cambridge University Press:  12 January 2012

Y. Maddahi ,
N. Sepehri ,
A. Maddahi  and
M. Abdolmohammadi
Y. Maddahi
Affiliation:
Department of Mechanical and Manufacturing Engineering, University of Manitoba, Winnipeg, Canada
N. Sepehri*
Affiliation:
Department of Mechanical and Manufacturing Engineering, University of Manitoba, Winnipeg, Canada
A. Maddahi
Affiliation:
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
M. Abdolmohammadi
Affiliation:
School of Manufacturing Engineering, Science and Research Division, Islamic Azad University, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

Exact knowledge of the position and proper calibration of robots that move by wheels form an important foundation in mobile robot applications. In this context, a variety of sensory systems and techniques have been developed for accurate positioning of differential drive mobile robots. This paper, first, provides a brief overview of mobile robots positioning techniques and then, presents a new benchmark method capable of calibrating mobile robots with differential drive mechanisms to correct systematic errors. The proposed method is compared with the commonly used University of Michigan Benchmark (UMBmark) odometry method. Two sets of comparisons are conducted on six prototyped robots with differential drives. The first set of tests establishes the workability and accuracy that can be achieved with the new method and compares them with the ones obtained from the UMBmark technique. The second experiment compares the performance of a mobile robot, calibrated with either the UMBmark or the new method, for an unseen path. It is demonstrated that the proposed method of calibration is simple to implement, and leads to accuracy comparable to the UMBmark method. Specifically, while the error corrections in both methods are within ±5% of each other, the proposed method requires single straight line motion for calibration, which is believed to be simpler and less timely to implement than the square path motion required by the UMBmark technique. The method should therefore be considered seriously as a new tool when calibrating differential drive mobile robots.

Keywords

Differential drive mobile robotsSystematic errorBenchmark testsError improvementPerformance analysisStatistical results
Type
Articles
Information
Robotica , Volume 30 , Issue 6 , October 2012 , pp. 1029 - 1039
Copyright
Copyright © Cambridge University Press 2012

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References

1.JCGM, “International Vocabulary of Metrology—Basic and General Concepts and Associated Terms,” 3rd ed., JCGM 200, 16–34 (2008).Google Scholar
2.de Wit, C., “Trends in Mobile Robot and Vehicle Control,” In: Lecture Notes in Control and Information Sciences (Springer, Berlin, 1998), vol. 230, pp. 151–175.Google Scholar
3.Maddahi, Y. and Maddahi, A., “Mobile robots experimental analysis based on kinematics,” WSEAS Trans. Circuits Syst. 3, 1662–1667 (2004).Google Scholar
4.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,” Proceedings of the SPIE—The International Society for Optical Engineering, Boston, USA (Oct. 23, 2005) vol. 6006, pp. 232–243.Google Scholar
5.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
6.Bury, B. and Hope, J. C., “Autonomous Mobile Robot Navigation Using a Low-Cost Fibre Optic Gyroscope,” Proceedings of the International Conference on Intelligent Autonomous Vehicles, Espoo, Finland (June 12–14, 1995) pp. 39–43.Google Scholar
7.Kwon, W., Roh, K. S. and Sung, H. K., “Particle Filter-Based Heading Estimation Using Magnetic Compasses for Mobile Robot Navigation,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida, USA (May 15–19, 2006) pp. 2705–2712.Google Scholar
8.Borenstein, J., “Experimental results from internal odometry error correction with the omnimate mobile robot,” IEEE Trans. Robot. Autom. 14 (6), 963–969 (1998).CrossRefGoogle Scholar
9.Antonelli, G., Chiaverini, S. and Fusco, G., “A calibration method for odometry of mobile robots based on the least-square technique: Theory and experimental validation,” IEEE Trans. Robot. 21 (5), 994–1004 (2005).CrossRefGoogle Scholar
10.Martinelli, A. and Siegwart, R., “Estimating the Odometry Error of a Mobile Robot During Navigation,” Proceedings of the European Conference on Mobile Robots, Warsaw, Poland (Sep. 4–6, 2003).Google Scholar
11.Roy, N. and Thrun, S., “Online Self-Calibration for Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI, USA (May 10–15, 1999) vol. 3, pp. 2292–2297.Google Scholar
12.Rahok, S. A. and Koichi, O., “Odometry Correction with Localization Based on Landmarkless Magnetic Map for Navigation System of Indoor Mobile Robot,” Proceedings of the IEEE International Conference on Autonomous Robots and Agents, Willington, New Zealand (Feb. 10–12, 2009) pp. 572–577.Google Scholar
13.Song, X. and Wang, Y., “A Novel Model-Based Method for Odometry Calculation of All-Terrain Mobile Robots,” Proceedings of the IEEE World Congress on Intelligent Control and Automation, Chongqing, China (June 25–27, 2008) pp. 581–586.Google Scholar
14.Nistér, D., Naroditsky, O. and Bergen, J., “Visual odometry for ground vehicle applications,” J. Field Robot. 23 (1), 3–20 (2006).CrossRefGoogle Scholar
15.Marantos, P., Koveos, Y., Stergiopoulos, L., Panousopoulou, A. and Tzes, A., “Mobile Robot Odometry Relying on Data Fusion from RF and Ultrasound Measurements in a Wireless Sensor Framework,” Proceedings of the IEEE Conference on Control and Automation, Ajaccio, France (June 25–27, 2008) pp. 523–528.Google Scholar
16.Papadopoulos, E. and Misailidis, M., “On Differential Drive Robot Odometry with Application to Path Planning,” Proceedings of the European Control Conference, Kos, Greece (Jul. 2–5, 2007) pp. 5492–5499.Google Scholar
17.Chenavier, F. and Crowley, J. L., “Position Estimation for a Mobile Robot Using Vision and Odometry,” Proceedings of the IEEE International Conference on Robotics and Automation, Nice, France (May 12–14, 1992) pp. 2588–2593.Google Scholar
18.Borenstein, J. and Feng, L., “UMBmark: A Benchmark Test for Measuring Dead Reckoning Errors in Mobile Robots,” Proceedings of the SPIE Conference on Mobile Robots, Philadelphia, PA, USA (Oct. 23–24, 1995) pp. 113–124.Google Scholar
19.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
20.Borenstein, J. and Feng, L., “Measurement and correction of systematic odometry errors in mobile robots,” IEEE Trans. Intell. Robot. Syst. 12 (6), 869–880 (1996).Google Scholar
21.Maddahi, Y., Seddigh, M., Mohammad Pour, M. and Maleki, M., “Simulation study and laboratory results of two-wheeled mobile robot,” WSEAS Trans. Syst. 3, 2807–2812 (2004).Google Scholar
22.Maddahi, Y. and Maddahi, A., “YMBM: New method for errors measurements in wheeled mobile robots,” WSEAS Trans. Syst. 5, 552–557 (2006).Google Scholar
23.Maddahi, Y., “Design and Laboratory Tests of Wheeled Mobile Robots,” Proceedings of the International Conference on System Science and Simulation in Engineering, Tenerife, Spain (Dec. 16–18, 2005) pp. 186–191.Google Scholar
24.Denavit, J. and Hartenberg, R. S., “A kinematic notation for lower-pair mechanisms based on matrices,” ASME J. Appl. Mech. 23, 215–221 (1955).CrossRefGoogle Scholar
25.Siegwart, R. and Nourbakhsh, I. R., Introduction to Autonomous Mobile Robots (Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, 2004) pp. 48–64.Google Scholar
26.Wuensch, K. L., Encyclopedia of Statistics in Behavioral Science (Wiley, Chichester, UK, 2005).Google Scholar
27.Brown, J. D., “Statistics corner: Questions and answers about language testing statistics: Skewness and kurtosis,” Shiken: JALT Test. Eval. SIG Newsl. 1 (1), 20–23 (1997).Google Scholar
28.Abramowitz, M. and Stegun, I. A., eds., Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables (Dover, New York, 1972).Google Scholar
29.Aggarwal, Y. P., Statistical Methods: Concepts, Application and Computation, 3rd ed. (Sterling Publishers, New Delhi, 2008).Google Scholar