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Application of Knowledge Driven Mobile Robots for Disassembly Tasks

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 480))

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Abstract

Considering the disassembly as a vital and prospective industry domain, we use the mobile robots to automate the disassembly process. In our system, each mobile robot has particular skills and is supervised by an agent with related objectives and knowledge. An agent has an ontology-based world model, which is responsible to maintain the knowledge about the robot’s activities in relation to its environment as well as to its underlying software parts. The ontology is used to represent a specification of an agent’s domain knowledge. The system functionality is tested with three mobile robots having a task to disassemble a particular Lego construct. Different rule-engines were benchmarked in order to enhance the systems performance.

Based on “Knowledge Driven Mobile Robots Applied in the Disassembly Domain”, by Gottfried Koppensteiner, Reinhard Hametner, Rene Paris, Alejandro Moser Passani, and Munir Merdan which appeared in the Proceedings of the 5th International Conference on Automation, Robotics and Applications (ICARA 2011). © 2011 IEEE.

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Acknowledgments

The authors would like to acknowledge the support by the Sparkling Science program, an initiative of the Austrian Federal Ministry of Science and Research. We also want to thank all partners involved in the DISBOTICS Project, especially the students at Vienna Institute of Technology (TGM), department for Information-Technology, and the KISS Institute of Practical Robotics.

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Correspondence to Gottfried Koppensteiner .

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Koppensteiner, G., Krofitsch, C., Hametner, R., Miller, D.P., Merdan, M. (2013). Application of Knowledge Driven Mobile Robots for Disassembly Tasks. In: Sen Gupta, G., Bailey, D., Demidenko, S., Carnegie, D. (eds) Recent Advances in Robotics and Automation. Studies in Computational Intelligence, vol 480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37387-9_24

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  • DOI: https://doi.org/10.1007/978-3-642-37387-9_24

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