Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-28T22:19:59.404Z Has data issue: false hasContentIssue false

State Matrix Representation of Assembly and Robot Planning

Published online by Cambridge University Press:  09 March 2009

S. M. Noorhosseini
Affiliation:
Center for Intelligent Machines, McCill University, Montreal, Quebec, Canada, H3A 2A7
A. S. Malowany
Affiliation:
Center for Intelligent Machines, McCill University, Montreal, Quebec, Canada, H3A 2A7

Summary

A new approach to represent assembly called state matrix representation and an algorithm for automatic robot assembly planning based on this representation is proposed. The state matrix representation of assembly is configured by considering the inter-relationships of parts and objects involved in the initial and the goal structures. Thanks to this new representation, the planning lgorithm is straightforward and can be easily and efficiently implemented with simple matrix manipulation. Unlike other planning methods, the actions involved in the assembly process are not defined in advance but are generated at planning time. The syntax of actions are designed so that while directly reflecting the semantics of actions, they can be easily manipulated by the planner. Two examples of how to plan an assembly based on this representation are given in the paper.

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.Chapman, D., “Planning for conjunctive goalsArtificial Intelligence 32 333337 (1987).CrossRefGoogle Scholar
2.Wilkins, D. E., Practical Planning (Morgan Kaufmann, 1985).Google Scholar
3.Fikes, R. E. and Nilsson, N.Strips: A new approach to the application of theorem proving to problem solvingArtificial Intelligence 2 189208 (1971).CrossRefGoogle Scholar
4.Sacerdoti, E., A Structure for Plans and Behavior (American Elsevier, Nwe York, 1977).Google Scholar
5.Curie, K. and Tate, A.O-plan: the open planning architectureArtificial Intelligence 52 4986 (1991).CrossRefGoogle Scholar
6.Sussman, G., A Computational Model of Skill Acquisition (American Elsevier, Nwe York, 1975).Google Scholar
7.Tate, A., “Interplan: A plan generation system which can deal with interactions between goals” Machine Intelligence Research Memorandum MIP-R-109 (University of Edinburg, 1974).Google Scholar
8.Noorhosseini, S.M. and Malowany, A.S., “Task planning for circuit board repair task in a robotic workcell” Proceedings of Canadian Conference on Electrical and Computer Engineering, 65.5.1–66.1.1., Quebec, Canada, 09 25–27 1991 (1991) pp. 65.5.1–66.1.1.Google Scholar
9.Teonshaff, H.K., Menzel, E. and Park, H., “Knowledgebased system for automated assembly planningCIRP Annals, 41(1) 1922 (1992).CrossRefGoogle Scholar
10.Huang, Y. and Lee, C., “A framework of knowledge-based assembly planning” Proceedings of the 1991 IEEE International Conference on Robotics and Automation,Sacramento, California,April 1991 (1991) pp. 599604.Google Scholar
11.Homen de Mello, L.S. and Sanderson, A.C., “Two criteria for the selection of assembly plans; maximizing the flexibility of sequencing the assembly tasks and minimizing the assembly time through parallel execution of assembly tasksIEEE Transactions on Robotics and Automation, 7(5) 626633 (10, 1991).CrossRefGoogle Scholar
12.Noorhosseini, S. and Malowany, A., “Robot workcell planning” Technical Report CIM-93–12 (McGill Research Center for Intelligent Machines, McGill University, Montreal, Canada, 1993).Google Scholar
13.Hayward, V. et al. , “The evolutionary design of mcpl the mss command and programming language” Proceedings of IEEE International Workshop on Intelligent Robots and Systems '90, IROS'90 Japan, July 5–6 1990 (1990) pp.413421.Google Scholar
14.Klein, I. and Backstrom, C., “On the planning problem in sequential control” Proceedings of IEEE Conf. on Decision and Control,Brighton, England,Dec. 11–13 1991 (1991) pp. 18191823.Google Scholar
15.Drummond, M. and Currie, K., “Goal ordering in partially ordered plans” Proceedings of Elevent International Joint Conference on Artificial Intelligence,Detroit, Michigan,20–25 August 1989 (1989) pp. 960965.Google Scholar
16.Cheng, J. and Irani, K.B., “Ordering problem subgoals” Proceedings of Elevent International Joint Conference on Artificial Intelligence,Detroit, Michigan, USA,20–25 August 1989 (Morgan Kaufmann Publishers, 1989) pp. 931936.Google Scholar
17.Homen de Mello, L.S., Sanderson, A.C. and Zhang, H., “Assembly sequence planningAl Magazine, 11(1) 6281 (1990).Google Scholar
18.ElMaraghy, H. and Laperriere, L., “Modelling and sequence generation for robotized mechanical assemblingRobotics and Autonomous Systems, 9, 137141 (1992).CrossRefGoogle Scholar
19.Ginsberg, M.L. and Smith, D.E., “Reasoning about actions: A possible worlds approachArtificial Intelligence, 35(2) 165195 (06, 1988).CrossRefGoogle Scholar
20.Sedgwick, R., Algorithms (Addison-Wesley, Hemel Hempstead, UK, 1988).Google Scholar