Patching process optimization in an agent-controlled timber mill
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Repair and patching of wood defects is a costly process of inline production in timber industry. A large variety of plain as well as laminated wooden products demands for offline human interaction and skilled handcrafting in order to achieve the desired quality of the final products. The EU FP7 project Hol-I-Wood PR demonstrates the transformation of a traditional wood patching line for shuttering panels into a fully automated, flexible patching plant. The focus of this paper is set on the optimization of the different production steps of a patching robot, which comprises optimal patch placement, path planning and trajectory generation. Based on this, the processing time of each workpiece can be accurately estimated. These computations serve as an input for advanced panel scheduling, which assigns panels to one of several identical parallel patching lines in a throughput-optimal manner. In order to ensure high modularity of the components and scalability for various wood mills, an agent-based approach was chosen for the implementation of the automation system.
KeywordsWood patching robot Patch placement Polygon covering Path planning Traveling salesman problem Trajectory generation Agent technology
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 284573.
- Applegate, D. L., Bixby, R. E., Chvátal, V., & Cook, W. J. (2006). The traveling salesman problem: A computational study. Princeton series in applied mathematics. Princeton, NJ: Princeton University Press.Google Scholar
- Biagiotti, L., & Melchiorri, C. (2008). Trajectory planning for automatic machines and robots. Berlin, Heidelberg: Springer.Google Scholar
- Bussmann, S., Jennings, N. R., & Wooldridge, M. (2004). Multiagent systems for manufacturing control: A design methodology. Springer series on agent technology. Berlin, Heidelberg: Springer.Google Scholar
- Chu, S.-C., Roddick, J.F., Su, C.-J., & Pan, J.-S. (2004). Constrained ant colony optimization for data clustering. In Proceedings of the 8th Pacific Rim international conference on artificial intelligence, Auckland, NZ, pp. 534–543.Google Scholar
- Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the congress on evolutionary computation, vol. 2, Washington, D.C., USA, pp. 1470–1477.Google Scholar
- Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.Google Scholar
- Gen, M., & Lin, L. (2013). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866, (2013), doi: 10.1007/s10845-013-0804-4.
- Gerelli, O., & Guarino Lo Bianco, C. (2010). A discrete-time filter for the on-line generation of trajectories with bounded velocity, acceleration, and Jerk. In Proceedings of the IEEE international conference on robotics and automation, Anchorage, Alaska, USA, pp. 3989–3994.Google Scholar
- Guarino Lo Bianco, C., & Ghilardelli, F. (2012). Third order systemfor the generation of minimum-time trajectories with asymmetric bounds on velocity, acceleration, and Jerk. In Proceedings of the international conference on intelligent robots and systems, Vilamoura, Algarve, Portugal, pp. 137–143.Google Scholar
- Hegny, I., Hummer, O., Zoitl, A., Koppensteiner, G., & Merdan, M. (2008). Integrating software agents and IEC 61499 realtime control for reconfigurable distributed manufacturing systems. In Proceedings of the international symposium on industrial embedded systems, La Grande-Motte, FR, pp. 249–252.Google Scholar
- Hu, X.-B., & Chen, W.-H. (2005). Genetic algorithm based on receding horizon control for arrival sequencing and scheduling. Engineering Applications of Artificial Intelligence, 18(5), 633–642.Google Scholar
- Hu, X.-B., Chen, W.-H., & Di Paolo, E. (2007). Multiairport capacity management: Genetic algorithm with receding horizon. IEEE Transactions on Intelligent Transportation Systems, 8(2), 254–263.Google Scholar
- Jade, Java Agent DEvelopment Framework. http://jade.tilab.com/, 2014. [23 July 2014].
- Jennings, N. R., & Sycara, K. (1998). A roadmap of agent research and development, 1998.Google Scholar
- Melik-Merkumians, M., Baier, T., Steinegger, M., Lepuschitz, W., Hegny, I., & Zoitl, A. (2012). Towards OPC UA as portable SOA middleware between control software and external added value applications. In Proceedings of the IEEE 17th international conference on emerging technologies and factory automation, Krakow, PL, pp. 1–8.Google Scholar
- Merdan, M., Moser, T., Wahyudin, D., Biffl, S., & Vrba, P. (2008). Simulation of workflow scheduling strategies using the MAST test management system. In Proceedings of the 10th international conference on control, automation, robotics and vision, Hanoi, Vietnam, pp. 1172–1177.Google Scholar
- Mucientes, M., Vidal, J. C., Bugarin, A., & Lama, M. (2008). Processing times estimation in a manufacturing industry through genetic programming. In Proceedings of the 3rd international workshop on genetic and evolving systems, Witten-Bommerholz, GER, pp. 95–100.Google Scholar
- Ollinger, L., Zuhlke, D., Theorin, A., & Johnsson, C. (2013). A reference architecture for service-oriented control procedures and its implementation with SysML and Grafchart. In Proceedings of the IEEE 18th conference on emerging technologies factory automation, Cagliari, IT, pp. 1–8.Google Scholar
- Or, I., & Duman, E. (1996). Optimization issues in automated production of printed circuit boards: Operations sequencing, feeder configuration and load balancing problems. In Proceedings of the IEEE conference on emerging technologies and factory automation, vol. 1, Kauai, Hawaii, pp. 227–232.Google Scholar
- Ribeiro, L., Barata, J., & Mendes, P. (2008). MAS and SOA: Complementary automation paradigms. In Innovation in manufacturing networks. Springer, NY, USA, pp. 259–268.Google Scholar
- Sipser, M. (2012). Introduction to the theory of computation. Boston, MA: Cengage Learning.Google Scholar
- Vallée, M., Kaindl, H., Merdan, M., Lepuschitz, W., Arnautovic, E., & Vrba, P. (2009). An automation agent architecture with a reflective world model in manufacturing systems. In Proceedings of the IEEE international conference on systems, man and cybernetics, San Antonio, TX, USA, pp. 305–310.Google Scholar
- Vincze, M., Biegelbauer, G., & Pichler, A. (2004). Painting parts automatically at lot size one. In Proceedings of the international workshop on robot sensing, Graz, AUT, pp. 35–40.Google Scholar
- Williams, R. (1979). The geometrical foundation of natural structure: A source book of design. New York: Dover Publications.Google Scholar
- Zambonelli, F., Jennings, N.R., Omicini, A., & Wooldridge, M. (2000). Agent-oriented software engineering for internet applications. In Book coordination of internet agents: Models, technologies and applications. Springer, Heidelberg, GER, pp. 326–346.Google Scholar
- Zanasi, R., & Morselli, R. (2002). Third order trajectory generator satisfying velocity, acceleration and jerk constraints. In Proceedings of the international conference on control applications, Vol. 2, Glasgow, Scotland, UK, pp. 1165–1170.Google Scholar