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A contribution to parallelization of symbolic robot models

Published online by Cambridge University Press:  09 March 2009

Summary

This paper is focused on task scheduling in multiprocessor robot controllers. To minimize the input-output time delay our consideration is restricted to parallel architectures that include complete crossbar interconnection networks. In this paper, an efficient scheduling algorithm based on a heuristic function is considered. This function takes into account delays caused by interprocessor communication and minimizes both the execution time and the communication cost. Robot control computation based on a highly efficient customized symbolic method is decomposed into a large number of simple tasks, each involving a single floating-point operation. Starting with an empty partial schedule, each step of the search extends the current partial schedule by adding one of the tasks yet to be scheduled. The heuristic function used in the algorithm actively directs the search for a feasible schedule, i.e. it helps choose the task that extends the current partial schedule. To increase the computational rate we introduced overlapping of computations.

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Articles
Copyright
Copyright © Cambridge University Press 1995

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References

1.Dertouzos, M.L. and Mok, A.K., “Multiprocessor On-Line Scheduling Hard-Real-Time TasksIEEE Trans, on Software Engineering 15, No. 12, 14891506 (12, 1989).CrossRefGoogle Scholar
2.Gershon, D., “Parallel process decomposition of a dynamic manipulation task: Robotic sewingIEEE Trans, on Robotics and Automation 6, No. 3, 357367 (06, 1990).CrossRefGoogle Scholar
3.Shaffer, P.L., “Minimization of interprocessor synchronization in multiprocessors with shared and private memory” Proc. 1989 Int. Conf. on Parallel Processing, (Vol. III, Algorithms and Applications, Edited by Ris, F. and Kogge, P.M.) (The Pennsylvania State Univer. Press 1989), pp. III–138142.Google Scholar
4.Kircanski, N., Leković, Dj., Borić, M., Vukobratović, M., Djurović, M., Djurović, N., Petrović, T., Karan, B. and Urošević, D., “A distributed PC-based control system for education in roboticsRobotica 9, 235245 (1991).CrossRefGoogle Scholar
5.Lathrop, L.H., “Parallelism in manipulator dynamicsInt.J.of Robotics Res. 4(2), 80102 (1985).CrossRefGoogle Scholar
6.Paul, R.P., Robot Manipulators: Mathematics, Control, and Programming (MIT Press, Cambridge, MA, 1981).Google Scholar
7.Luh, J.Y.S., Walker, M. W. and Paul, R.P.C., “On-line computational scheme for mechanical manipulatorsTrans. ASME, J. Dynam. Syst., Meas. Contr. 120, 6976 (1980).CrossRefGoogle Scholar
8.Tarn, T.J.. Bejczy, A.K., Isidori, A. and Chen, Y., “Nonlinear Feedback in Robot Arm Control” Proc. of the 23rd IEEE Conf. on Decision and Control,Las Vegas, Nevada (1984) pp. 736751.Google Scholar
9.Bejczy, A.K. and Tarn, T.J., “Dynamic control of robot arms in task space using nonlinear feedbackAutomatisierungstechnik 36, No. 10, 374388 (1988).CrossRefGoogle Scholar
10.Khatib, O., “A unified approach for motion and force control of robot manipulator: The operational space formulationIEEE J. of Robotics and Automation RA-3, No. 1,4353 (1987).CrossRefGoogle Scholar
11.Craig, J.J., Adaptive Control of Mechanical Manipulators (Addison-Wesley, Reading, MA, 1988).Google Scholar
12.Kasahara, H. and Narita, S., “Practical multiprocessor scheduling algorithms for efficient parallel processingIEEE Trans, on Computers C-33, No. 11,10231029 (11, 1984).CrossRefGoogle Scholar
13.Kasahara, H. and Narita, S., “Parallel processing of robot-arm control computation on a multimircoprocessor systemIEEE J.Robotics and Autom. RA-1, No. 2, 104113(1985).CrossRefGoogle Scholar
14.Lee, C.S.G. and Chang, P.R., “A maximum pipelined CORDIC architecture for inverse kinematic position computationIEEE J.Robotics and Automation RA-3, No. 5, 445458 (1987).CrossRefGoogle Scholar
15.Geffin, S. and Furht, B., “A dataflow multiprocessor system for robot arm controlInt. J.of Robotics Research 9, No.3, 93103 (06, 1990).CrossRefGoogle Scholar
16.Kirćanski, N., Petrović, T. and Vukobratović, M., “Parallel Computation of Symbolic Robot Models and Control Laws: Theory and Application on Transputer NetworksJ.Robotic Systems 10, No. 3, 345368 (04, 1993).CrossRefGoogle Scholar
17.Fijany, A. and Bejczy, A.K. “A class of parallel algorithms for computation of the manipulator inertia matrix” Proc. IEEE Int. Conf. on Robotics and Autom.,San Francisco(1989) pp. 18181826.Google Scholar
18.Luh, J.Y.S. and Lin, C.S., “Scheduling of parallel computation for a computer controlled mechanical manipulatorIEEE Trans.Syst., Man, and Cybern. SMC-12, 214234 (1982).CrossRefGoogle Scholar
19.Vukobratović, M., Kirdanski, N. and Li, S.G., “An approach to parallel processing of dynamic robot modelsInt. J.Robotics Research 7, No. 2, 6471 (04, 1988).CrossRefGoogle Scholar
20.Zheng, Y.F. and Hemami, H., “Computation of multibody system dynamics by a multiprocessor schemeIEEE Trans.Syst., Man, and Cybern. SMC-16, No. 1, 102110 (1986).CrossRefGoogle Scholar
21.Nigam, R. and Lee, G., “A multiprocessor-based controller for the control of mechanical manipulatorsIEEE J.Robotics and Autom. RA-1, No. 4, 173182 (1985).CrossRefGoogle Scholar
22.Lee, C.S.G. and Chang, P.R., “Efficient parallel algorithm for robot inverse dynamics computationIEEE Trans.Syst., Man, and Cybern. SMC-16(4), 532542.Google Scholar
23.Lee, C.S.G. and Chang, P.R., “Efficient parallel algorithm for robot forward dynamics computationIEEE Trans.Syst., Man, and Cybern. SMC-18(2), 238251 (1988). CrossRefGoogle Scholar
24.Wander, J. and Tesar, D., “Pipelined computation of manipulator modeling matricesIEEE J. Robotics and Automation RA-3, No. 6, 556566 (1987).CrossRefGoogle Scholar
25.Kircanski, N., Timcenko, A., Jovanovid, Z., Kirdanski, M., Vukobratovid, M. and Milunov, R., “Computation of customized symbolic models on peripheral array processorsProc.1989 IEEE Conf. on Robotics and Automation,(1989) pp. 11801185.Google Scholar
26.Sadayappan, P., Ling, Yong-Long C., Olson, K.W. and Orin, D.E.. “A restructurable VLSI robotics vector processor architecture for real-time controlIEEE Trans, on Robotics and Automation 5, No. 5, 583599 (1989).CrossRefGoogle Scholar
27.Chang, P.R. and Lee, G., “Residue arithmetic VLSI array architecture for manipulator pseudo-inverse Jacobian computationIEEE Trans, on Robotics and Automation 5, No. 5, 569582 (1989).CrossRefGoogle Scholar
28.Kung, S.Y. and Hwang, J.N., “Neural network architectures for robotic applicationsIEEE Trans, on Robotics and Automation 5, No. 5, 641657 (1989).CrossRefGoogle Scholar
29.Miller, W.T., Hewes, R.P., Glanz, F.H. and Kraft, L.G., “Real-time dynamic control of an industrial manipulator using a neural-network-based learning controllerIEEE Trans, on Robotics and Automation 6, No. 1, 19 (1990).CrossRefGoogle Scholar
30.Adam, T.L., Chandy, K.M. and Dickson, J.R., “A comparison of list schedules for parallel processing systemsCommun. Ass.comput Mach. 17, 685690 (1974).Google Scholar
31.Vukobratović, M. and Kirćanski, N., “Computer-Assisted Generation of Robot Dynamic Models in an Analytical FormAda Applicandae Mathematicae 3, 4870 (1985).Google Scholar
32.Vukobratović, 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
33.Renaud, M., “An efficient iterative analytical procedure for obtaining a robot manipulator dynamic model“ Proc. First Int. Symp. on Robotics Res., Bretton Wood 749–762 (1983) pp. 749762.Google Scholar
34.Fijany, A. and Bejczy, A.K., “Parallel computation of manipulator inverse dynamicsJ.Robotic Systems 8, No. 5, 599635 (1991).CrossRefGoogle Scholar
35.Amin-Javaheri, M. and Orin, D.E., “A systolic architecture for computation of the manipulator inertia matrixProc.IEEE Int.Conf. on Robotics and Automation, 2 (1987) pp. 647653.Google Scholar
Robot Modelling” Proc. 27 Midwest Symp. on Circuits and Systems, Morgantown, WV (1984) pp. 479481.Google Scholar
37.Neuman, Ch. P. and Murray, J.J., “Computational robot dynamics: Foundations and applicationsJ. of Robotic Systems 2, No. 4, 425452 (1985).CrossRefGoogle Scholar
38.Li, J.C., “A new method for dynamic analysis of robot” Proc. IEEE Int. Conf. on Robotics and Automation,San Francisco(1986) pp. 227233.Google Scholar
39.Burdick, J., “An algorithm for generation of efficient manipulator dynamic equations“ Proc. IEEE Int. Conf. on Robotics and Automation,San Francisco (1986) pp. 212218.Google Scholar
40.Khalil, W., Kleinfinger, J.F. and Gautier, M., “Reducing the computational burden of the dynamic models of robots” Proc. 1986 IEEE Int. Conf. on Robotics and Automation,(1986) pp. 525532.Google Scholar
41.Izaguirre, A. and Paul, R., “Automatic generation of the dynamic equations of the robot manipulators using a LISP program Proc. IEEE Int. Conf. on Robotics and Automation,San Francisco(1986) pp. 220236.Google Scholar
42.Kirćanski, M., Vukobratović, M., Kirćanski, N. and TimCenko, A., “A new program package for the generation of efficient manipulator kinematic and dynamic equations in symbolic formRobotica 6, 311318 (1988).CrossRefGoogle Scholar
43.Mihailo Pupin Institute, PC-SYM-Program Package for Computer-aided Generation of Robot Symbolic Models and Control Laws (Beograd, 1990).Google Scholar
44.Timčenko, A., Kirćanski, N. and Vukobratović, M., “A two-step algorithm for generating efficient manipulator models in symbolic formProc. IEEE Int. Conf. on Robotics and Automation, 3, (1991) pp. 18871892.Google Scholar
45.Kirćanski, N., Petrović, T. and Vukobratović, M.Parallel Computation of Symbolic Robot Models on Pipelined Processor ArchitecturesRobotica 11, Part 1, 3747 (02., 1993).CrossRefGoogle Scholar
46.Quinn, M.J., Designing Efficient Algorithms for Parallel Computers (McGraw-Hill series in supercomputing and artificial intelligence, McGraw-Hill, New York, 1987).Google Scholar
47.Logical Systems, Parallel C Transputer Toolset (Distributed by MicroWay, Inc., P.O. Box 79, Kingston, MA 02364 USA, 1988).Google Scholar
48.Stepanenko, Y. and Vukobratović, M., “Dynamics of articulated open-chain active mechanismsMath. Biosciences 28, No. 1/2, 137170 (1976).CrossRefGoogle Scholar
49.INMOS Limited, Transputer Reference Manual (Prentice Hall, New York, 1988).Google Scholar
50.INMOS Limited, Transputer Technical Notes (Prentice Hall, New York, 1989).Google Scholar