Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-28T03:04:54.409Z Has data issue: false hasContentIssue false
  • English
  • Français

Parallel computation of symbolic robot models of pipelined processor architectures

Published online by Cambridge University Press:  09 March 2009

N. Kirćanski ,
T. Petović  and
M. Vukobratović
N. Kirćanski
Affiliation:
Mihailo Pupin Institute, University of Belgrade, POB 15 Belgrade Yugoslavia
T. Petović
Affiliation:
Mihailo Pupin Institute, University of Belgrade, POB 15 Belgrade Yugoslavia
Get access Rights & Permissions [Opens in a new window]

Summary

Increased speed of inverse dynamics computation is essential for improving the characteristics of robot control systems. This is achieved by reducing the numerical complexity of the models and by introducing parallelism in model computation. In this paper customized symbolic models with a near minimum numerical complexity will be used as a basis for the examination of parallelism in inverse dynamic robot models. A scheduling algorithm for the distribution of computational load onto an arbitrary linear array of pipelined processors will be developed. The proposed algorithm is experimentally evaluated on a transputer network.

Keywords

Heuristic scheduling functionCustomized symbolic modelsTime-delayPipelined transputers
Type
Research Article
Information
Robotica , Volume 11 , Issue 1 , January 1993 , pp. 37 - 47
Copyright
Copyright © Cambridge University Press 1993

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.Nigam, R. and Lee, G., “A Multiprocessor-Based Controller for the Control of Mechanical Manipulators,” IEEE J. Robotics and Autom., RA-1, No. 4, 173–182 (1985).CrossRefGoogle Scholar
2.Stepanenko, J. and Vukobratović, M., “Dynamics of Articulated Open-chain Active Mechanisms Math. Bio-sciences 28, Nos. 1/2, 137–170 (1976).CrossRefGoogle Scholar
3.Luh, J.Y.S., Walker, M.W. and Paul, R.P.C., “On-line Computational Scheme for Mechanical Manipulators” Trans. ASME, J. Dynam. Syst., Meas. Contr. 120, 69–76 (1980).CrossRefGoogle Scholar
4.Paul, R.P., Robot Manipulators: Mathematics, Control, and Programming (MIT Press, Cambridge, MA 1981).Google Scholar
5.Hollerbach, J.M., “A Recursive Lagrangian Formulation of Manipulator Dynamics and a Comparative Study of Dynamics Formulation Complexity” IEEE Trans. Syst. Man, and Cybern., SMC-10, 11, 730–736 (11, 1980).CrossRefGoogle Scholar
6.Kirćanski, N. and Vukobratović, M., “Computer Aided Procedure of Forming Robot Motion Equations in Analytical Forms” In PROC. OF VI IFToMM CONGRESS, New Delhi (1983).Google Scholar
7.Vukobratovic, M. and Kirćanski, N., Real-Time Dynamics of Manipulation Robots, in Series: Scientific Fundamentals of Robotics No. 4 (Springer Verlag, New York, 1985).Google Scholar
8. Horak, T.D., “A Fast Computational Scheme for Dynamic Control of Manipulators” In: Proc. of 1984. American Control Conference, San Francisco, CA(1984) pp. 625–630.Google Scholar
9.Neuman, Ch. P. and Murray, J.J., “Computational Robot Dynamics: Foundations and Applications,” J. Robotic Systems 2, No. 4, 425–452 (1985).CrossRefGoogle Scholar
10.Li, J.C., “A New Method for Dynamic Analysis of Robot” Proc. IEEE Int. Conf. on Robotics and Automation, San Francisco, (1986) pp. 227–233.Google Scholar
11.Burdick, J., “An Algorithm for Generation of Efficient Manipulator Dynamic Equations” In: Proc. IEEE Int. Conf. on Robotics and Automation, San Francisco (1986) pp. 212–218.Google Scholar
12.Khalil, W., Kleinfinger, J.F. and Gautier, M., “Reducing the Computational Burden of the Dynamic Models of Robots” In: Proc. 1986 IEEE Int. Conf. on Robotics and Automation (1986) pp. 525–532.Google Scholar
13.Kirčanski, M., Vukobratović, M., Kircanski, N. and Timcénko, A., “A New Program Package for the Generation of Efficient Manipulator Kinematic and Dynamic Equations in Symbolic Form” Robotica 6, part 4, 311–318 (1988).CrossRefGoogle Scholar
14.Timčenko, A., Kirćanski, N., Urošević, D.’ and Vukobratović, M., “SYM-Program Environment for Manipulator Modeling, Control and Simulation” In: Proc. IEEE Conf. on Robotics and Automation (1991) pp. 1122–1127.Google Scholar
15.Timčenko, A., Kirćanski, N. and Vukobratovic, M., “A Two-Step Algorithm for Generating Efficient Manipulator Models in Symbolic Form” In: Proc. IEEE Conf. on Robotics and Automation (1991) pp. 1887–1892.Google Scholar
16.Vukobratović, M., Kirćanski, N., Timčenko, A., and Kirćanski, M., “SYM-Program Package for Computer-Aided Generation of Optimal Symbolic Models of Robot Manipulators, In: Multibody Systems Handbook (Schiehlen, W., ed.) (Springer-Verlag, New York, 1990) pp. 37–61.Google Scholar
17.Luh, J.Y.S. and Lin, C.S., “Scheduling of Parallel Computation for a Computer Controlled Mechanical Manipulator” IEEE Trans. Syst. Man, and Cybern., SMC-12, 214–234 (1982).CrossRefGoogle Scholar
18.Kasahara, H. and Narita, S., “Parallel Processing of Robot-Arm Control Computation on a Multimicroprocessor System,” IEEE J. Robotics and Autom., RA-1, No. 2, 104–113 (1985).CrossRefGoogle Scholar
19.Vukobratović, M., Kirćanski, N. and Li, S.G., “An Approach to Parallel Processing of Dynamic Robot Models,” Int. J. Robotics Research, 7, No. 2, 64–71 (04, 1988).CrossRefGoogle Scholar
20.Lee, C.S.G. and Chang, P.R., “Efficient Parallel Algorithm for Robot Inverse Dynamics Computation” In: Proc. IEEE Int. Conf. on Robotics and Autom. San Francisco (1986) pp. 851–857.Google Scholar
21.Zheng, Y.F. and Hemami, H., “Computation of Multibody System Dynamics by a Multi-processor Scheme” IEEE Trans. Syst. Man, and Cyber. SMC-16, No. 1, 102–110 (1986).CrossRefGoogle Scholar
22.Fijany, A. and Bejczy, A.K., “A Class of Parallel Algorithms for Computation of the Manipulator Inertia Matrix” In: Proc. IEEE Int. Conf. on Robotics and Autom., San Francisco (1986) pp. 1818–1826.Google Scholar
23.Amin-Javaheri, M. and Orin, D.E., “A Systolic Architecture for Computation of the Manipulator Inertia Matrix,” In: Proc. IEEE Int. Conf. on Robotics and Automation, 2, (1987) pp. 647–653.Google Scholar
24.Lee, C.S.G. and Chang, P.R., “Efficient Parallel Algorithms for robot forward Dynamics computation” IEEE Trans. Syst., Man, and Cybern, SMC-18 (2), 238–251 (1988).CrossRefGoogle Scholar
25.Lee, C.S.G. and Chang, P.R., “Efficient Parallel Algorithm for Robot Inverse Dynamics Computation” IEEE Trans. Syst., Man, and Cybern. SMC-16(4), 532–542 (1986).CrossRefGoogle Scholar
26.Lathrop, L.H., “Parallelism in Manipulator Dynamics” Int. J. Robotics Res. 4(2), 80–102 (1985).CrossRefGoogle Scholar
27.Hockney, R.W. and Jesshope, C.R., Parallel computers (Adam Hilger, UK, 1981).Google Scholar
28.Kirćanski, N., Timčenko, A., Jovanović, Z., Kirćanski, M., Vukobratović, M., Milunov, R., “Computation of Customized Symbolic Robot Models on Peripheral Array Processors” In: Proc. 1989 IEEE Conf. on Robotics and Automation (1989) pp. 1180–1185.Google Scholar
29.Kirćanski, N., Vukobratović, M., Petrović, T. and Timcenko, A., “Parallel Computational Methods Based on Symbolic Robot Models-Theory and Application on Pipelined Processor Architectures” In: Proc. Fifth Int. o Conf. on Advanced Robotics, '91 ICAR, Pisa, Italy (1991) pp. 705–711.Google Scholar
30.Kirćanski, N., Leković, Dj., Borić, M., Vukobratović, M., Djurović, M., Djurović, N., Petrović, T., Karan, B., Urošević, D. “A Distributed PC-based Control System for Education in Robotics” Robotica, 9 part 2, 235–245 (1991).CrossRefGoogle Scholar
31.Transputer Reference Manual (INMOS Limited, Prentice Hall, 1988).Google Scholar
32.Transputer Technical Notes (INMOS Limited, Prentice Hall, 1989).Google Scholar
33.Marshell, R.M., “Automatic Generation of Controller Systems from Control Software” In: ICCAD ‘86 Proc. IEEE. Int. Conf. on CAD, Santa Clara, CA (Nov., 1986) pp. 256–259.Google Scholar
34.Parallel C Transputer Tollset, Logical Systems (distributed by Micro Way, Inc., P.O. Box 79, Kingston, MA 02364 USA, 1988).Google Scholar
35.EXPRESS A Communication Environment for Parallel Computers (ParaSoft Corporation, 27415 Trabuco Circle, Mission Viejo, CA 92692 USA, 1988).Google Scholar