Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-10T16:17:44.706Z Has data issue: false hasContentIssue false
  • English
  • Français

Recursive field estimation and tracking for autonomous manipulation

Published online by Cambridge University Press:  15 September 2011

Soo Jeon
Soo Jeon*
Affiliation:
Department of Mechanical and Mechatronics Engineering, University of Waterloo 200 University Ave. West, Waterloo ON, N2L 3G1, Canada
*
*Corresponding author. E-mail: soojeon@uwaterloo.ca
Get access Rights & Permissions [Opens in a new window]

Summary

Autonomous operation of mechanical systems often requires the ability to detect and locate a particular phenomenon occurring in the surrounding environment. Being implemented to articulated manipulation, such a capability may realize a wide range of applications in autonomous maintenance and repair. This paper presents the sensor-driven task space control of an end-effector that combines the field estimation and the target tracking in an unknown spatial field of interest. The radial basis function network is adopted to model spatial distribution of an environmental phenomenon as a scalar field. Their weight parameters are estimated by a recursive least square method using collective measurements from the on-board sensors mounted to the manipulator. Then the asymptotic source tracking has been achieved by the control law based on the gradient of the estimated field. A new singularity tolerant scheme has been suggested to command the task space control law despite singular configurations. Simulation results using the three-link planar robot and the 6-revolute elbow manipulator are presented to validate the main ideas.

Keywords

Control of robotic systemsAutomationMechatronic systemsMotion planningManufacturing
Type
Articles
Information
Robotica , Volume 30 , Issue 5 , September 2012 , pp. 743 - 753
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Parker, L. E. and Draper, J. V., “Robotics applications in maintenance and repair,” In: Handbook of Industrial Robotics, 2nd ed. (Nof, Shimon Y., ed.) (John Wiley, New York, 1998).Google Scholar
2.Vertut, J. and Coiffet, P., Teleoperation and Robotics: Applications and Technology (Prentice-Hall. Englewood Cliffs, NJ, 1985).CrossRefGoogle Scholar
3.Erik, K., Pal, L. and Andreas, T. A., “A Robotic Concept for Remote Inspection and Maintenance on Oil Platforms,” In: Proceedings of the ASME 28th International Conference on Ocean, Offshore and Arctic Engineering, Honolulu, Hawaii, USA (2009) pp. 667–674.Google Scholar
4.Glass, K., Colbaugh, R. and Seraji, H., “Real-Time Control for a Serpentine Manipulator,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Grenoble, France (1997) pp. 1775–1780.Google Scholar
5.Choi, J., Oh, S. and Horowitz, R., “Distributed learning and cooperative control for multi-agent systems,” Automatica 45 (12), 2802–2814 (2009).CrossRefGoogle Scholar
6.Martinez, S., “Distributed interpolation schemes for field estimation by mobile sensor networks,” IEEE Trans. Control Syst. Technol. 18 (2), 491–500 (2009).CrossRefGoogle Scholar
7.Leonard, N., Paley, D. A., Lekien, F., Sepulchre, R., Fratantoni, D. M. and Davis, R. E., “Collective motion, sensor networks, and ocean sampling,” Proc. IEEE 95 (1), 48–74 (2007).CrossRefGoogle Scholar
8.Grünbaum, D., “Schooling as a strategy for taxis in a noisy environment,” Evolution. Ecol. 12, 503–522 (1998).CrossRefGoogle Scholar
9.Christopoulos, V. N. and Roumeliotis, S. I., “Adaptive Sensing for Instantaneous Gas Release Parameter Estimation,” In: Proceedings of the IEEE International Conference on Robotics and Aotumation, Barcelona, Spain (2005) pp. 4450–4456.Google Scholar
10.Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 90–98 (1986).CrossRefGoogle Scholar
11.Nakamura, Y. and Hanafusa, H., “Inverse kinematics solutions with singularity robustness for robot manipulator control,” ASME J. Dyn. Syst. Meas. Control 108, 163–171 (1986).CrossRefGoogle Scholar
12.Tsumaki, Y. and Nenchev, D. N., “Jacobian adjoint matrix-based approach to teleoperation,” In: Proceedings of the International Symposium on Microsystems, Intelligent Materials and Robots, Sendai, Japan (1995) pp. 532–535.Google Scholar
13.Oetomo, D. and Ang, M. H. Jr., “Singularity robust algorithm in serial manipulators,” Robot. Comput.-Integr. Manuf. 25, 122–134 (2009).CrossRefGoogle Scholar
14.Khatib, O., “Inertial Properties in robotic manipulation: An object-level framework,” Int. J. Robot. Res. 14 (1), 19–36 (1994).CrossRefGoogle Scholar
15.Yoshikawa, T., “Analysis and control of robot manipulators with redundancy,” In: Robotics Research: 1st International Symposium (Brady, M. and Paul, R., eds.) (MIT Press, Cambridge, MA, 1984) pp. 735–747.Google Scholar
16.Sciavicco, L. and Sicilino, B., Modeling and Control of Robot Manipulators, 2nd ed. (McGraw-Hill, New york, NY, 2000).CrossRefGoogle Scholar
17.Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modeling and Control (John Wiley, New York, 2006).Google Scholar
18.Åström, K. J. and Wittenmark, B., Adaptive Control, 2nd ed. (Addison-Wesley, Boston, MA, 1995).Google Scholar
19.Patel, R. V. and Shadpey, F., Control of Redundant Robot Manipulators: Theory and Experiments (Springer-Verlag, Germany, 2005).Google Scholar
20.Chang, K. S. and Khatib, O., “Manipulator Control at Kinematic Singularities: A Dynamically Consistent Strategy,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Pittsburgh, Pennsylvania, USA (August 5–9, 1995) pp. 84–88.Google Scholar
21.Nakanishi, J., Cory, R., Mistry, M., Peters, J. and Schaal, S., “Operational space control: A theoretical and empirical comparison,” Int. J. Robot. Res. 27 (6), 737–757 (2008).CrossRefGoogle Scholar
22.Cheah, C. C., “Task-space PD control of robot manipulators: Unified analysis and duality property,” Int. J. Robot. Res. 27 (10), 1152–1170 (2008).CrossRefGoogle Scholar
23.Jeon, S. and Ahn, H. J., “Autonomous Manipulation Combining Task Space Control with Recursive Field Estimation,” Proceedings of the 2nd International Conference on Autonomous and Intelligent Systems (AIS), Burnaby BC, Canada (2011).Google Scholar