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A Distributed On-Line Trajectory Generator for Intelligent Sensory-Based Manipulators

Published online by Cambridge University Press:  09 March 2009

A. M. S. Zalzala  and
A. S. Morris
A. M. S. Zalzala
Affiliation:
Robotics Research Group, Department of Control Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD (UK)
A. S. Morris
Affiliation:
Robotics Research Group, Department of Control Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD (UK)
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Summary

An algorithm is presented for the on-line generation of minimum-time trajectories for robot manipulators. The algorithm is designed for intelligent robots with advanced on-board sensory equipment which can provide the position and orientation of the end-effector. Planning is performed in the configuration (joint) space by the use of optimised combined polynomial splines, along with a search technique to identify the best minimum-time trajectory. The method proposed considers all physical and dynamical limitations inherent in the manipulator design, in addition to any geometric path constraints. Meeting the demands of the heavy computations involved lead to a distributed formulation on a multiprocessor system, for which an intelligent control unit has been created to supervise its proper and practical implementation. Simulation results of a proposed case study are presented for a PUMA 560 robot manipulator.

Keywords

Trajectory generatorOn-lineSensoryMinimum-time trajectoryIntelligent robots
Type
Article
Information
Robotica , Volume 9 , Issue 2 , April 1991 , pp. 145 - 155
Copyright
Copyright © Cambridge University Press 1991

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References

1.Paul, R.P., Robot Manipulators: Mathematics, Programming and Control (MIT Press, Cambridge, Mass., 1981).Google Scholar
2.Luh, J.Y.S., Walker, M.W. and Paul, R.P.C., “On-Line Computational Scheme for Mechanical Manipulators” Trans. ASME, J. of Dyn. Syst., Meas, and Control 102, 69–76 (1980).Google Scholar
3.Hollerbach, J.M., “A Recursive Lagrangian Formulation of Manipulator Dynamics and a Comparative Study of Dynamics Formulation Complexity” IEEE Trans. Syst., Man, Cyber. SMC-10, 730–36 (1980).Google Scholar
4.Lozano, T.-Perez, “A Simple Motion-Planning Algorithm for General Robot Manipulators” IEEE J. Robotics and Automation RA-3, No. 3, 224–38 (1987).CrossRefGoogle Scholar
5.Sahar, G. and Hollerbach, J.M., “Planning of Minimum-time Trajectories for Robot Arms” Int. J. Robotics Research 5, No. 3, 90–100 (1986).CrossRefGoogle Scholar
6.Zalzala, A.M.S. and Morris, A.S., “An Optimum Trajectory Planner for Robot Manipulators in Joint-Space and Under Physical Constraints” Research Report #349 (Dept. Control Eng., Univ. of Sheffield, UK, 1988).Google Scholar
7.Hirzinger, G. and Dietrich, J., “Multisensory Robots and Sensorybased Path Generation” Proc. IEEE Int. Conf. Robotics and Automation 3, 1992–2001 (1986).Google Scholar
8.Porrill, J., Pollard, S.B., Pridmore, T.P., Bowen, J.B., Mayhew, J.E.W. and Frisby, J.P., “TINA: The Sheffield Aivru Vision System” AIVRU memo #27 (AI Vision Research Unit, Sheffield University, United Kingdom, 1988).Google Scholar
9.Dickinson, M. and Morris, A.S., “Co-ordinate Determination and Performance Analysis for Robot Manipulators and Guided Vehicles” IEE Proceedings, Part-A 135, No. 2, 95–98 (1988).Google Scholar
10.Zalzala, A.M.S. and Morris, A.S., “Structured Motion Planning in The Local Configuration Space” Robotica (in press).Google Scholar
11.Lin, C.S., Chang, P.R., and Luh, J.Y.S., “Formulation and Optimization of Cubic Polynomial Joint Trajectories For Industrial Robots” IEEE Trans. Automatic Control AC-28, 1066–74 (1983).CrossRefGoogle Scholar
12.Bollinger, J. and Duffie, N., “Computer Algorithms for High Speed Continuous-Path Robot Manipulators” Annals of the CIRP, 28, 391–95 (1979).Google Scholar
13.Vandergraft, J.S., Introduction to Numerical Computations (Academic Press, New York 1978).Google Scholar
14.De-Boor, C., A Practical Guide to Splines (Springer-Verlag, Berlin, 1978).CrossRefGoogle Scholar
15.Hollerbach, J.M., “Dynamic Scaling of Manipulator Trajectories” Trans. ASME, J. of Dyn. Syst., Meas. and Control 106, 102–06 (1984).Google Scholar
16.Lin, C.S. and Chang, P.R., “Approximate Optimum Paths of Robot Manipulators Under Realistic Physical Constraints” Proc. IEEE Int. Conf. on Robotics and Automation 737–42 (1985).Google Scholar
17.Fu, K.S., Gonzalez, R.C., and Lee, C.S.G., Robotics: Control, Sensing, Vision and Intelligence (McGraw Hill, New York, 1987).Google Scholar
18.Brady, J.M., Hollerbach, J.M., Johnson, T.L., Lozano-Perez, T. and Mason, M.T., Robot Motion: Planning and Control (MIT Press, Cambridge, Mass., 1982).Google Scholar
19.Zalzala, A.M.S. and Morris, A.S., “A Distributed Pipelined Architecture of the Recursive Lagrangian Equations of Motion for Robot Manipulators with VLSI Implementation” Research Report #353 (Dept. Control Eng., Univ. of Sheffield, UK, 1989).Google Scholar
20.Lee, C.S.G. and Chang, P.R., “A Maximum Pipelined CORDIC Architecture for Robot Inverse Kinematics Computation” Report TR-EE-86−5 (Purdue University, 1986).CrossRefGoogle Scholar
21.SUN, Floating-Point Programmer's Guide for the Sun Workstation (Sun Microsystems Inc., USA, 1986).Google Scholar