Basic behaviour control of the vision‐based cognitive robotic disassembly automation
Author:
Vongbunyong Supachai,Kara Sami,Pagnucco Maurice
Abstract
PurposeThe purpose of this paper is to develop an automated disassembly cell that is flexible and robust to the physical variations of a product. In this way it is capable of dealing with any model of product, regardless of the level of detail in the supplied information.Design/methodology/approachThe concept of cognitive robotics is used to replicate human level expertise in terms of perception and decision making. As a result, difficulties with respect to the uncertainties and variations of the product in the disassembly process are resolved.FindingsCognitive functions, namely reasoning and execution monitoring, can be used in basic behaviour control to address problems in variations of the disassembly process due to variations in the product's structure particularly across different models of the product.Research limitations/implicationsThe paper provides a practical approach to formulating the disassembly domain and behaviour control of the cognitive robotic agent via a high‐level logical programming language that combines domain‐specific heuristic knowledge with search to deal with variations in products and uncertainties that arise during the disassembly process.Practical implicationsFull disassembly automation that is flexible and robust to the uncertainties that may arise potentially replaces human labour in a difficult and hazardous task. Consequently, the disassembly process will be more economically feasible, especially in developed countries.Originality/valueThe paper provides a practical approach to the basic cognitive functions that replicate the human expert's behaviour to the disassembly cell.
Subject
Industrial and Manufacturing Engineering,Control and Systems Engineering
Reference36 articles.
1. Bailey‐Van Kuren, M. (2006), “Flexible robotic demanufacturing using real time tool path generation”, Robotics & Computer‐Integrated Manufacturing, Vol. 22, pp. 17‐24. 2. Bannat, A., Bautze, T., Beetz, M., Blume, J., Diepold, K., Ertelt, C., Geiger, F., Gmeiner, T., Gyger, T., Knoll, A., Lau, C., Lenz, C., Ostgathe, M., Reinhart, G., Roesel, W., Ruehr, T., Schuboe, A., Shea, K., Stork Genannt Wersborg, I., Stork, S., Tekouo, W., Wallhoff, F., Wiesbeck, M. and Zaeh, M.F. (2011), “Artificial cognition in production systems”, IEEE Transactions on Automation Science and Engineering, Vol. 8 No. 148, p. 174, Art. No. 5524092. 3. Beetz, M., Buss, M. and Wollherr, D. (2007), “Cognitive technical systems – what is the role of artificial intelligence?”, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4667 LNAI, Springer, New York, NY, pp. 19‐42. 4. Berger, U. and Schmidt, A. (1995), “Active vision system for planning and programming of industrial robots in one‐of‐a‐kind manufacturing”, Proceedings of SPIE – The International Society for Optical Engineering, pp. 135‐46. 5. Büker, U., Drüe, S., Götze, N., Hartmann, G., Kalkreuter, B., Stemmer, R. and Trapp, R. (1999), “Active object recognition system for disassembly tasks”, IEEE Symposium on Emerging Technologies and Factory Automation, ETFA, Vol. 1, pp. 79‐88.
Cited by
48 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|