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A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing

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Numerical Methods and Applications (NMA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2542))

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Abstract

This work presents a comparison of decision making criteria and optimization methods for active sensing in robotics. Active sensing incorporates the following aspects: (i ) where to position sensors, and (ii ) how to make decisions for next actions, in order to maximize information gain and minimize costs. We concentrate on the second aspect: “Where should the robot move at the next time step?”. Pros and cons of the most often used statistical decision making strategies are discussed. Simulation results from a new multisine approach for active sensing of a nonholonomic mobile robot are given.

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References

  1. Geeter, J. D., De Schutter, J., Bruyninckx, H., Brussel, H. V., and Decrton, M.: Tolerance-Weighted L-optimal Experiment Design: a New Approach to Task-Directed Sensing. Advanced Robotics, Vol. 13, no.4 (1999) 401–416.

    Article  Google Scholar 

  2. Roy, N., Burgard, W., Fox, D., and Thrun, S.: Coastal Navigation-Mobile Robot Navigation with Uncertainty in Dynamic Environments. Proc. of ICRA (1999)

    Google Scholar 

  3. Cassandra, A., Kaelbling, L., and Kurien, J.: Acting under Uncertainty: Discrete Bayesian Models for Mobile Robot Navigation. Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (1996)

    Google Scholar 

  4. DeSouza, G. N. and Kak, A.: Vision for Mobile Robot Navigation: A Survey. IEEE Trans. on Pattern Analysis and Machine Intel., Vol. 24, no. 2 (2002) 237–267.

    Article  Google Scholar 

  5. Denzler, J. and Brown, C.: Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, (2002) 145–157.

    Article  Google Scholar 

  6. Sutton, R. and Barto, A.: Reinforcement Learning, An introduction. MIT (1998)

    Google Scholar 

  7. Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/non-Gaussian Bayesian Tracking. IEEE Trans. on Signal Proc., Vol. 50, no. 2 (2002) 174–188.

    Google Scholar 

  8. Bar-Shalom, Y. and Li, X.: Estimation and Tracking: Principles, Techniques and Software. Artech House (1993)

    Google Scholar 

  9. Laumond, J.-P.: Robot Motion Planning and Control. Guidelines in Nonholonomic Motion Planning for Mobile Robots, by Laumond, J.-P, Sekhavat, S., and Lamiraux, F., available at http://www.laas.fr/∼jpl/book.html: Springer-Verlag (1998)

  10. Brock, O. and Khatib, O.: Elastic Strips: A Framework for Integrated Planning and Execution. Proc. of 1999 Int. Symp. of Experim. Robotics, (1999) 245–254.

    Google Scholar 

  11. Fisher, R.: On the Mathematical Foundations of Theoretical Statistics. Phylosophical Trans. of the Royal Society of London, Series A, Vol. 222 (1922) 309–368.

    Article  Google Scholar 

  12. Fedorov, V.: Theory of Optimal Experiments. Academic press, New York ed. (1972)

    Google Scholar 

  13. Shannon, C.: A Mathematical Theory of Communication, I and II. The Bell System Technical Journal, Vol. 27 (1948) 379–423 and 623-656.

    MathSciNet  MATH  Google Scholar 

  14. Kullback, S.: On Information and Sufficiency. Annals of mathematical Statistics, Vol. 22 (1951) 79–86.

    Article  MathSciNet  MATH  Google Scholar 

  15. Mihaylova, L., De Schutter, J., and Bruyninckx, H.: A Multisine Approach for Trajectory Optimization Based on Information Gain. Proc. of IROS Conf. (2002)

    Google Scholar 

  16. Julier, S., Uhlman, J., and Durrant-Whyte, H.: A New Method for the Transformation of Means and Covariances in Filters and Estimators. IEEE Trans. on AC, Vol. 45, no. 3 (2000) 477–482.

    MATH  Google Scholar 

  17. Swevers, J., Ganseman, C., Tukel, D., De Schutter, J., and Brussel, H. V.: Optimal Robot Excitation and Identification. IEEE Trans. on AC, Vol. 13, no. 5 (1997) 730–740.

    Google Scholar 

  18. Bellman, R.: Dynamic Programming. New Jersey: Princeton Univ. Press (1957)

    Google Scholar 

  19. Howard, R. A.: Dynamic Programming and Markov Processes. MIT Press (1960)

    Google Scholar 

  20. Schweitzer, P. and Seidmann, A.: Generalized Polynomial Approximations in Markovian Decision Processes. J. of MA and Appl., Vol. 110 (1985) 568–582.

    MATH  MathSciNet  Google Scholar 

  21. Boutilier, C., Dean, T., and Hanks, S.: Decision-Theoretic Planning: Structural Assumptions and Computational Leverage. J. of AI Research, Vol. 11 (1999) 1–94.

    MATH  MathSciNet  Google Scholar 

  22. Lovenjoy, W. S.: A Survey of Algorithmic Methods for Partially Observed Markov Decision Processes. Annals of Operations Research, Vol. 18 (1991) 47–66.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Katholieke Universiteit Leuven, Celestijnenlaan 300B, B-3001, Heverlee, Belgium

    Lyudmila Mihaylova, Tine Lefebvre, Herman Bruyninckx, Klaas Gadeyne & Joris De Schutter

Authors
  1. Lyudmila Mihaylova
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  2. Tine Lefebvre
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  3. Herman Bruyninckx
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  4. Klaas Gadeyne
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  5. Joris De Schutter
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Editor information

Editors and Affiliations

  1. Central Laboratory for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev, bl. 25A, 1113, Sofia, Bulgaria

    Ivan Dimov , Ivan Lirkov  & Svetozar Margenov ,  & 

  2. National Environmental Research Institute, Frederiksborgvej 399, P.O. Box 358, 4000, Roskilde, Denmark

    Zahari Zlatev

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© 2003 Springer-Verlag Berlin Heidelberg

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Mihaylova, L., Lefebvre, T., Bruyninckx, H., Gadeyne, K., De Schutter, J. (2003). A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing. In: Dimov, I., Lirkov, I., Margenov, S., Zlatev, Z. (eds) Numerical Methods and Applications. NMA 2002. Lecture Notes in Computer Science, vol 2542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36487-0_35

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  • DOI: https://doi.org/10.1007/3-540-36487-0_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00608-4

  • Online ISBN: 978-3-540-36487-0

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