Abstract
The GLEAM algorithm and its implementation are a new evolutionary method application in the field of robotics. The GLEAM software generates control code for real industrial robots. Therefore GLEAM allows a time related description of the robot movement (not only a static description of robot arm configurations). This internal representation of primitive move commands is mapped to a representation of move statements of an industrial robot language, which can be loaded at the robot control and executed.
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.
References
Blume, C., Jakob, W.: Closing the Optimization Gap in Production by Genetic Algorithms. Proc. of the European Congress on Intelligent Techniques and Soft Computing (EUFIT 93), 1993, Aachen
Blume, C.: Industrielle Anwendungen Evolutionärer Algorithmen (Industrial Applications of evolutionary algorithms). Contribution: Planung kollisionsfreier Bewegungen für Industrieroboter (Planning of collision free movements of industrial robots). Ed.: S. Hafner, R. Oldenbourg Verlag, München Wien 1998
Blume, C.: GLEAM-A System for Simulated „Intuitive Learning“. Proceedings of the 1st International Workshop on Problem Solving from Nature, Dortmund, Germany, October 1–3, 1990
Blume, C., Jakob, W., Krisch, S.: Robot Trajectory Planning with Collision Avoidance using Genetic Algorithms and Simulation. Proc. of the 25th International Symposium on Industrial Robots, 25.–27. April 1994, Hannover, pp. 169–175
Planning and Optimization of Scheduling in Industrial Production by Genetic Algorithms and Evolutionary Strategy. Proc. of the Second Biennial European Joint Conference on Engineering Systems Design and Analysis (ESDA), July 4–7, 1994, London, England
Blume, C., Gerbe, M.: Deutliche Senkung der Produktionskosten durch Optimierung des Ressourceneinsatzes. (Reduction of production costs by optimizing the resource planning). atp-Automatisierungstechnische Praxis, 36 (1994) pp. 5–9
Blume, C., Früauf, P.: Standardization of Programming Methods and languages for Manipulating Industrial Robots. 27th International Symposium on Industrial Robots, Oktober 1996, Mailand
Dai, F.: Collision-Free Motion of an Articulated Kinematic Chain in a Dynamic Environment, IEEE Computer Graphics & Application, January 1989, pp. 70–74
Goldberg, D., Parker, J., Khoogar, A.: Inverse Kinematics of Redundant Robots using Genetic Algorithms. IEEE International Conference on Robotics and Automation 7, (1989). pp. 271–276
Heine, R., Schnare, T.: Kollisionsfreie Bahnplanung für Roboter (Collision free path planning for robots). Robotersysteme 7, (1991). pp. 17–22
Generation of Optimized Collision Free Robot Move Statements Based on Genetic Algorithms. Proceedings of the World Automation Congress (WAC’ 96) Volume 3: Robotic and Manufacturing Systems, May 28–30, 1996, Montpellier, France. pp. 89–94
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Blume, C. (2000). Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software GLEAM. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_32
Download citation
DOI: https://doi.org/10.1007/3-540-45561-2_32
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67353-8
Online ISBN: 978-3-540-45561-5
eBook Packages: Springer Book Archive