Abstract
Today’s industrial robots use programming languages that do not allow learning and task knowledge acquisition and probably this is one of the reasons of its restricted used for complex task in unstructured environments. In this paper, results on the implementation of a novel task planner using a 6 DOF industrial robot as an alternative to overcome this limitation are presented. Different Artificial Neural Networks (ANN) models were assessed first to evaluate their learning capabilities, stability and feasibility of implementation in the planner. Simulations showed that the Adaptive Resonance Theory (ART) outperformed other connectionist models during tests and therefore this model was chosen. This work describes initial results on the implementation of the planner showing that the manipulator can acquire manipulative skills to assemble mechanical components using only few clues.
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
Jörg, S., Langwald, J., Stelter, J., Natale, C., Hirzinger, G.: Flexible Robot- Assembly using a Multi-Sensory Approach. In: Proc. IEEE Int. Conference on Robotics and Automation, San Francisco, CA, pp. 3687–3694 (2000)
Nuttin, M., Van Brussel, H., Peirs, J., Soembagijo, A.S., Sonck, S.: Learning the Peg-into-Hole Assembly Operation with a Connectionist Reinforcement Technique. Katholieke Universiteit Leuven, Belgium
Lopez-Juarez, I.: On-line learning for Robotic Assembly Using artificial Neural Networks and Force Sensing. PhD Thesis. The Nottingham Trent University (2000)
Howarth, M.: An Investigation of task Level Programming for Robotic Assembly. PhD Thesis. The Nottingham Trent University (1998)
Howarth, M., Sivayoganathan, K., Thomas, P.D., Gentle, C.R.: Robotic Task Level Programming Using Neural Networks. In: Artificial Neural Networks. Conference Publication, vol. 409, IEEE, Los Alamitos (1995)
Cervera, E., Del Pobil, A.P.: Programming and learning in real-world manipulation tasks. In: Proc. of Int. Conference on Intelligent Robot and Systems (IEEE/RSJ), vol. 1, pp. 471–476 (1997)
Gullapalli, V., Franklin, J.A., Benbrahim, H.: Control Under Uncertainty Via Direct Reinforcement Learning. Robotics and Autonomous Systems, 237–246 (1995)
Serrano-Gotarredonda, T., Linares-Barranco, B., Andreou, A.G.: Adaptive Resonance Theory Microchips. Kluwer Academic Publisher, USA (1998)
Aguado, B.A.: Temas de Identificación y Control Adaptable. Instituto de Cibernética, Matemática y Física, La Habana, Cuba (2000)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30 (2001)
Arbib, M.: The Handbook of Brain theory and Neural Networks. In: Part 1: Background: The elements of brain theory and neural networks I, 2nd edn., pp. 17–18. MIT Press, Cambridge (2003)
Carpenter, G.A., Grossberg, S.: Adaptive Resonance Theory (ART). In: Arbib, M.A. (ed.) The Handbook of Brain theory and Neural Networks, 2nd edn., pp. 79–82. MIT Press, Cambridge (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castuera, J.C., Lopez-Juarez, I. (2004). Intelligent Task Level Planning for Robotic Assembly: Issues and Experiments. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_90
Download citation
DOI: https://doi.org/10.1007/978-3-540-24694-7_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
Online ISBN: 978-3-540-24694-7
eBook Packages: Springer Book Archive