A comparison of decision making criteria and optimization methods for active robotic sensing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2542, Issue , 2003, Pages 316-324

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

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

NUMERICAL METHODS; ROBOTICS;

ACTIVE SENSING; DECISION MAKING CRITERIA; INFORMATION GAIN; NON-HOLONOMIC MOBILE ROBOTS; OPTIMIZATION METHOD; POSITION SENSORS; ROBOTIC SENSING; STATISTICAL DECISION-MAKING;

DECISION MAKING;

EID: 35248826966     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36487-0_35     Document Type: Review

References (23)

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.
2. Roy, N., Burgard, W., Fox, D., and Thrun, S.: Coastal Navigation - Mobile Robot Navigation with Uncertainty in Dynamic Environments. Proc. of ICRA (1999)
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)
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.
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.
6. Sutton, R. and Barto, A.: Reinforcement Learning, An introduction. MIT (1998)
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.
8. Bar-Shalom, Y. and Li, X.: Estimation and Tracking: Principles, Techniques and Software. Artech House (1993)
9. ∼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.
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.
12. Fedorov, V.: Theory of Optimal Experiments. Academic press, New York ed. (1972)
13. Shannon, C.: A Mathematical Theory of Communication, I and II. The Bell System Technical Journal, Vol. 27 (1948) 379-423 and 623-656.
14. Kullback, S.: On Information and Sufficiency. Annals of mathematical Statistics, Vol. 22 (1951) 79-86.
15. Mihaylova, L., De Schutter, J., and Bruyninckx, H.: A Multisine Approach for Trajectory Optimization Based on Information Gain. Proc. of IROS Conf. (2002)
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.
17. NEOS: Argonne national laboratory and northwestern university, optimization technology center. http://www-fp.mcs.anl.gov/otc/Guide/.
18. 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.
19. Bellman, R.: Dynamic Programming. New Jersey: Princeton Univ. Press (1957)
20. Howard, R. A.: Dynamic Programming and Markov Processes. MIT Press (1960)
21. Schweitzer, P. and Seidmann, A.: Generalized Polynomial Approximations in Markovian Decision Processes. J. of MA and Appl., Vol. 110 (1985) 568-582.
22. Boutilier, C., Dean, T., and Hanks, S.: Decision-Theoretic Planning: Structural Assumptions and Computational Leverage. J. of AI Research, Vol. 11 (1999) 1-94.
23. Lovenjoy, W. S.: A Survey of Algorithmic Methods for Partially Observed Markov Decision Processes. Annals of Operations Research, Vol. 18 (1991) 47-66.