Estimating first-order geometric parameters and monitoring contact transitions during force-controlled compliant motion

International Journal of Robotics Research

Volumn 18, Issue 12, 1999, Pages 1161-1184

  De Schutter, Joris ^a   Bruyninckx, Herman ^a   Dutré, Stefan ^a,d   De Geeter, Jan ^b   Katupitiya, Jayantha ^c   Demey, Sabine ^a,e   Lefebvre, Tine ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b BELGIAN NUCLEAR RESEARCH CENTRE   (Belgium)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

^d LMS INTERNATIONAL   (Belgium)

^e MATERIALISE NV   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL GEOMETRY; INTEGRATION; KALMAN FILTERING; MATHEMATICAL MODELS; PARAMETER ESTIMATION; TOPOLOGY;

FIRST-ORDER GEOMETRIC PARAMETER ESTIMATION; FORCE-CONTROLLED COMPLIANT MOTION;

MOBILE ROBOTS;

EID: 0033355138     PISSN: 02783649     EISSN: None     Source Type: Journal    
DOI: 10.1177/02783649922067780     Document Type: Article

References (51)

1. Ball, R. S. 1876. Theory of Screws: A Study in the Dynamics of a Rigid Body. Dublin, Ireland: Hodges, Foster, and Co. (Reprinted 1998, Cambridge University Press.)
2. Bar-Shalom, Y., and Li, X.-R. 1993. Estimation and Tracking, Principles, Techniques, and Software. Norwood, MA: Artech House.
3. Baumgarte, J. W. 1983. A new method of stabilization for holonomic constraints. Trans. ASME J. Appl. Mech. 50:869-870.
4. Bay, J. S., and Hemami, H. 1990 (Cincinnati, OH). Dynamics of a learning controller for surface tracking robots on unknown surfaces. Proc. of the IEEE Intl. Conf. on Robot. and Automat. Los Alamitos, CA: IEEE, pp. 1910-1915.
5. Blauer, M., and Bélanger, P. R. 1987. State and parameter estimation for robotic manipulators using force measurements. IEEE Trans. Automat. Control 32(12):1055-1066.
6. Bonnifait, P., and Garcia, G. 1998. Design and experimental validation of an odometric and goniometric localization system for outdoor robot vehicles. IEEE Trans. Robot. Automat. 14(4):541-548.
7. Bretthorst, G. L. 1990. An introduction to parameter estimation using Bayesian probability theory. In Fougère, P. F. (ed.) Maximum Entropy and Bayesian Methods. Dordrecht, The Netherlands: Kluwer, pp. 53-79.
8. Bruyninckx, H. 1995. Kinematic models for robot compliant motion with identification of uncertainties. PhD thesis, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
9. Bruyninckx, H., Demey, S., Dutré, S., and De Schutter, J. 1995. Kinematic models for model based compliant motion in the presence of uncertainty. Intl. J. Robot. Res. 14(5):465-482.
10. Bruyninckx, H., and De Schutter, J. 1996. Specification of force-controlled actions in the Task Frame Formalism: A survey. IEEE Trans. Robot Automat. 12(5):581-589.
11. Bruyninckx, H., and De Schutter, J. 1998 (Leuven, Belgium). Modeling and specification of compliant motions with two and three contact points Proc. of the IEEE Intl. Conf. on Robot. and Automat. Washington, DC: IEEE, pp. 1938-1943.
12. De Geeter, J., De Schutter, J., Van Brussel, H., and Decréton, M. 1997. A smoothly constrained Kalman filter. IEEE Trans. Pattern Analysis Machine Intell. 19(10:1171-1177.
13. De Luca, A., and Manes, C. 1994. Modeling of robots in contact with a dynamic environment. IEEE Trans. Robot. Automat. 10(4):542-548.
14. Demey, S., Bruyninckx, H., and De Schutter, J. 1997. Model-based planar contour following in the presence of pose and model errors. Intl. J. Robot. Res. 16(6):840-858.
15. Denham, W. F., and Pines, S. 1966. Sequential estimation when measurement function nonlinearity is comparable to measurement error. AAIA J. 4(6):1071-1076.
16. De Schutter, J., Bruyninckx, H., Zhu, W.H., and Spong, M. W. 1997. Force control A bird's-eye view. In Siciliano, B. (ed.) Control Problems in Robotics and Automation: Future Directions. San Diego, CA: Springer-Verlag, pp. 1-17.
17. De Schutter, J., and Van Brussel, H. 1988a. Compliant robot motion I. A formalism for specifying compliant motion tasks. Intl. J. Robot. Res. 7(4):3-17.
18. De Schutter, J., and Van Brussel, H. 1988b. Compliant robot motion II. A control approach based on external control loops. Intl. J. Robot. Res. 7(4):18-33.
19. Dutré, S. 1998. Identification and monitoring of contacts in force controlled robotic manipulation. PhD thesis, Dept. of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium.
20. Dutré, S., Bruyninckx, H., Demey, S., and De Schutter, J. 1997. Solving contact and grasp uncertainties. Proc. of the Intl. Conf. on Intell. Robots and Sys.. Grenoble, France, pp. 114-119.
21. Eberman, B. 1997. A model-based approach to Cartesian manipulation contact sensing. Intl. J. Robot. Res. 16(4):508-528.
22. Featherstone, R. 1987. Robot Dynamics Algorithms. Boston, MA: Kluwer Academic.
23. Fu, K. S. 1971. Learning control systems and intelligent control systems: An intersection of artificial intelligence and automatic control. IEEE Trans. Automat. Control 16:70-72.
24. Gelb, A. E. 1978. Optimal Estimation, (3rd ed.). Cambridge, MA: MIT Press.
25. Haug, E. J. 1989. Computer Aided Kinematics and Dynamics of Mechanical Systems. Volume 1: Basic Methods, series in engineering. Boston, MA: Allyn and Bacon.
26. Hovland, G., and McCarragher, B. J. 1998. Hidden Markov models as a process monitor in robotic assembly. Intl. J. Robot. Res. 17(2):153-168.
27. Jaynes, E. T. 1957. Information theory and statistical mechanics. Physical Rev. 106:620-630. (Reprinted in E. T. Jaynes: Papers on Probability, Statistics, and Statistical Physics, pp. 6-16 (see Rosenkrantz 1983)).
28. Jaynes, E. T. 1957. Information theory and statistical mechanics. Physical Rev. 106:620-630. (Reprinted in E. T. Jaynes: Papers on Probability, Statistics, and Statistical Physics, pp. 6-16 (see Rosenkrantz 1983)).
29. Jaynes, E. T. 1976. Confidence intervals vs. Bayesian intervals. In Harper, W. L., and Hooker, C. A. (eds.) Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science. Dordrecht: D. Reidel. (Reprinted in E. T. Jaynes: Papers on Probability, Statistics, and Statistical Physics, p. 151 (see Rosenkrantz 1983))
30. Jaynes, E. T. 1976. Confidence intervals vs. Bayesian intervals. In Harper, W. L., and Hooker, C. A. (eds.) Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science. Dordrecht: D. Reidel. (Reprinted in E. T. Jaynes: Papers on Probability, Statistics, and Statistical Physics, p. 151 (see Rosenkrantz 1983))
31. Jazwinski, A. H. 1970. Stochastic Processes and Filtering Theory, volume 64 of Mathematics in Science and Engineering. New York: Academic.
32. Jeffreys, H. 1939. Theory of Probability. Oxford, UK: Clarendon Press.
33. Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82:34-45.
34. Leonard, J. J. and Durrant-Whyte, H. F. 1992. Directed Sonar Sensing for Mobile Robot Navigation. Boston, MA: Kluwer.
35. Lončarić, J. 1987. Normal forms of stiffness and compliance matrices. IEEE J. Robot. Automat. 3(6):567-572.
36. Loredo, T. J. 1990. From Laplace to supernova SN 1987a: Bayesian inference in astrophysics. In Fougère, P. F. (ed.) Maximum Entropy and Bayesian Methods. Dordrecht, The Netherlands: Kluwer, pp. 81-142.
37. Mason, M. T. 1981. Compliance and force control for computer controlled manipulators. IEEE Trans. on Sys. Man Cybernet. 11(6):418-432.
38. Mason, M., and Salisbury, K. 1985. Robot Hands and the Mechanics of Manipulation. Cambridge, MA: MIT Press.
39. Mehra, R. K. 1971. On-line identification of linear dynamic systems with applications to Kalman filtering. IEEE Trans. Automat. Control 16:12-21.
40. Meinhold, R. J., and Singpurwalla, N. D. 1983. Understanding the Kalman filter. Am. Statistician 37:123-127.
41. Nuttin, M. 1998. Learning approaches to robotics and manufacturing, contributions and experiments. PhD thesis, Dept. of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium.
42. Nuttin, M., and Van Brussel, H. 1996. Learning assembly operations: A case study with real-world objects. Studies Informatics Control 5(3):205-221.
43. Patarinski, S., and Botev, R. 1993. Robot force control: A review. Mechatronics 3(4):377-398.
44. Rabiner, L. R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2):257-286.
45. Raibert, M., and Craig, J. J. 1981. Hybrid position/force control of manipulators. Trans. ASME J. Dyn. Sys. Meas. Control 102: 126-133.
46. Rosenkrantz, R. D. (ed.) 1983. E. T. Jaynes: Papers on Probability, Statistics and Statistical Physics. D. Reidel. (Second paperbound ed., Kluwer Academic Publishers, 1989.)
47. Saridis, G. N. 1979. Self-organizing control of stochastic systems. New York: Marcel Dekker.
48. Soatto, S., Frezza, R., and Perona, P. 1996. Motion estimation via dynamic vision. IEEE Trans. Automat. Control 41:393-414.
49. Sorenson, H. W. 1985. Kalman filtering: Theory and Application. New York: IEEE Press.
50. Van de Poel, P., Witvrouw, W., Bruyninckx, H., and De Schutter, J. 1993 (Tokyo). An environment for developing and optimizing compliant robot motion tasks. Proc. of the Intl. Conf. on Adv. Robotics, pp. 713-718.
51. Whitney, D. E. 1987. Historical perspective and state of the art in robot force control. Intl. J. Robot. Res. 6(1):3-14.