Abstract
This paper describes the use of a genetic algorithm (GA) in order to optimize the trajectory followed by industrial robots (IRs) in stamping lines. The objective is to generate valid paths or trajectories without collisions in order to minimize the cycle time required to complete all the operations in an individual stamping cell of the line. A commercial software tool is used to simulate the virtual trajectories and potential collisions, taking into account the specific geometries of the different parts involved: robot arms, columns, dies and manipulators. Then, a genetic algorithm is proposed to optimize trajectories. Both systems, the GA and the simulator, communicate as client - server in order to evaluate solutions proposed by the GA. The novelty of the idea is to consider the geometry of the specific components to adjust robot paths to optimize cycle time in a given stamping cell.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mitsi, S., Bouzakis, K.D., Mansour, G., Sagris, D., Maliaris, G.: Off-line programming of an industrial robot for manufacturing. International Journal of Advanced Manufacturing Technology 26, 262–267 (2005)
Zha, X.F., Du, H.: Generation and simulation of robot trajectories in a virtual CAD-based off-line programming environment. International Journal of Advanced Manufacturing Technology 17, 610–624 (2001)
Saramago, S.F.P., Junior, V.S.: Optimal trajectory planning of robot manipulators in the presence of moving obstacles. Mechanism and Machine Theory 35, 1079–1094 (2000)
Tangpattanakul, P., Artrit, P.: Minimum-Time Trajectory of Robot Manipulator Using Harmony Search Algorithm. IEEE Press, New York (2009)
Saravanan, R., Ramabalan, S.: Evolutionary minimum cost trajectory planning for industrial robots. Journal of Intelligent & Robotic Systems 52, 45–77 (2008)
Saravanan, R., Ramabalan, S., Balamurugan, C.: Evolutionary multi-criteria trajectory modeling of industrial robots in the presence of obstacles. Engineering Applications of Artificial Intelligence 22, 329–342 (2009)
Dassault Systemes: Delmia Documentation V5 R18, http://ol.cadfamily.com/delmia/online/DELMIA_default.htm
W. A. T. Solutions: Workspace 5 Robot Simulation and Off-Line Programming Software, http://www.workspace5.com
Johari, N.A.M., Haron, H., Jaya, A.S.M.: Robotic modeling and simulation of palletizer robot using Workspace5. In: Computer Graphics, Imaging and Visualisation: New Advances, pp. 217–222 (2007)
Connolly, C.: Technology and applications of ABB RobotStudio. Industrial Robot-an International Journal 36, 540–545 (2009)
ABB: El abc de industrial IT. ed: Revista ABB, pp. 6–13 (2002)
Kimura, S., Konagaya, A.: A Genetic Algorithm with Distance Independent Diversity Control for High Dimensional Function Optimization. Transactions of the Japanese Society for Artificial Intelligence 18, 193–202 (2003)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd rev. and extended ed., p. 387. Springer, Berlin (1996)
Syswerda, G.: Uniform Crossover in Genetic Algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 2–9. Morgan Kaufmann Publishers, San Francisco (1989)
Walter, C., Rao, V., Tom, S.: Multi-niche crowding in genetic algorithms and its application to the assembly of DNA restriction-fragments. Evolutionary Computation, 321–345 (1995)
Microsoft: Component Object Model technologies (COM), http://www.microsoft.com/com
Dassault Systemes, http://www.3ds.com
Graña, M., Torrealdea, F.J.: Hierarchically structured systems. European Journal of Operational Research 25, 20–26 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Sedano, J.A., Bernardo, J.A., González, A.G., de Gauna, Ó.B.R., de Mendivil, R.Y.G. (2010). Stamping Line Optimization Using Genetic Algorithms and Virtual 3D Line Simulation. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-13769-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13768-6
Online ISBN: 978-3-642-13769-3
eBook Packages: Computer ScienceComputer Science (R0)