Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-24T01:22:15.777Z Has data issue: false hasContentIssue false

The method of minimal neighborhood: a new and most effective iterative method for minimum cost trajectory planning in robot manipulators

Published online by Cambridge University Press:  09 March 2009

Ignacy Duleba
Affiliation:
Institute of Engineering Cybernetics, Technical University of Wroclaw, Janiszewskiego 11/17, 50–372 Wroclaw, (Poland)

Summary

In this paper a method of minimal neighborhood for cost optimal trajectory planning along prescribed paths is introduced. The method exploits the phase-plane approach. In the phase-plane, in an iterative procedure, subareas of search are built, called neighborings, which surround the current-best trajectory. In each iteration, in order to find the next-best trajectory, the dynamic programming (pruned to the subarea) is used. The method of minimal neighborhood makes the neighborings as small as possible and therefore speeds up computations maximally. The tests carried out on a model of the IRb-6 ASEA robot have shown that the method of minimal neighborhood is much faster than dynamic programming applied to the whole phase-plane, while preserving the quality of the resulting trajectory.

Type
Articles
Copyright
Copyright © Cambridge University Press 1995

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.Shin, K. G. and McKay, N. D., “Minimum-Time Control of Robotic Manipulators with Geometric Path ConstraintsIEEE Transactions, AC-30 No. 6, 531541 (1985).Google Scholar
2.Kim, B. K. and Shin, K. G., “Minimum-Time Path Planning for Robot Arms and Their DynamicsIEEE Transactions SMC 15, No. 2, 213223 (1985).Google Scholar
3.Ozaki, H. and Mohri, A., “Synthesis of a minimum-time manipulator trajectories with path constraints using time scalingRobitca 6, Part 1, 4146 (1988).Google Scholar
4.Slotine, J. E. and Yang, H. S., “Improving the Efficiency of Time-Optimal Path-Following AlgorithmsIEEE Trans. on Robotics and Automation RA-5(1), 118124 (1989).CrossRefGoogle Scholar
5.Chen, Y. and Chien, S. Y. P. and Desrochers, A. A., “General structure of time-optimal control of robotic manipulators moving along prescribed pathsInt. J. Control 56(4), 767782 (1992).CrossRefGoogle Scholar
6.Shin, K. G. and McKay, N. D., “A Dynamic Programming Approach to Trajectory Planning of Robotic ManipulatorsIEEE Transactions AC 31, No. 6, 491500 (1986).Google Scholar
7.Shin, K. G. and McKay, N. D., “Minimum cost trajectory planning for industrial robots” In: Advances in Robotic Control Systems (ed. by Leondes, M. C.) (Academic Press, New York, 1991) pp. 357403.Google Scholar
8.Jacak, W., Dulȩba, I. and Rogaliński, P., “A graph-searching approach to trajectory planing of robotic manipulatorsRobotica 10, Part 6, 431537 (1992).CrossRefGoogle Scholar
9.Vukobratovic, M. and Kircansky, M., “A Method of Optimal Synthesis of Manipulator Robot TrajectoriesTransactions ASME 104, 188193 (1982).Google Scholar
10.Moiseev, N. N., Elements of Optimal Systems Theory (Moscow, Nauka, 1975) (in Russian).Google Scholar
11.Paul, R. P., Robot Manipulators: Mathematics, Programming and Control (MIT Press, Cambridge, MASS., 1981).Google Scholar
12.Gosiewski, A. et al. , “Dynamic peroperties of IRb-6 and IRb-60 ASEA robots” Automation Dept. Reports (Technical Univ. of Warsaw, 1989).Google Scholar
13.Dulȩba, I., “Algorithms for a cost optimal trajectory planning in robot manipulators. A discrete phase approach” Ph. D. Thesis (Rept. of the Technical Univ. of Wroclaw, 1992) (in Polish).Google Scholar