Abstract
A novel mathematical formulation of the energy optimisation problem for robotic lines is presented, which allows minimising the energy consumption in a robotic cell while keeping the required production cycle time. Different energy saving modes of the robots are utilised as well as the fact that the robot energy consumption during its movement depends on the movement duration. This dependency is modelled with a so-called energy function, which can be obtained by measurements, physical modelling of the robots or simulation. Each of these areas is covered by the presented work. The achieved results show there is a good potential to achieve energy savings at existing robotic cells and their series, and an even bigger potential if the presented approach is used during the design phase of new robotic cells.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The more energy-saving mode is the longer time is required to have the robot back in a ready-to-operate mode.
- 2.
Each activity can be performed by only one assigned robot.
- 3.
The dynamic activity has exactly one successor and one predecessor.
References
Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Upper Saddle River, NJ (1993)
Bard, J.F., Purnomo, H.W.: Cyclic preference scheduling of nurses using a Lagrangian-based heuristic. J. Sched. 10(1), 5–23 (2007)
Bjorkenstam, S., Gleeson, D., Bohlin, R., Carlson, J., Lennartson, B.: Energy efficient and collision free motion of industrial robots using optimal control. In: IEEE International Conference on Automation Science and Engineering, pp. 510–515 (2013)
Bryan, C., Grenwalt, M., Stienecker, A.: Energy consumption reduction in industrial robots. In: Proceedings of ASEE North Conference, pp. 1–4 (2010)
Chemnitz, M., Schreck, G., Kruger, J.: Analyzing energy consumption of industrial robots. In: Proceedings of IEEE Conference on Emerging Technologies & Factory Automation, pp. 1–4 (2011)
Gavrovska, A.M., Paskaš, M.P., Dujković, D., Reljin, I.S.: Region-based phonocardiogram event segmentation in spectrogram image. In: 10th Symposium on Neural Network Applications in Electrical Engineering, NEUREL-2010 - Proceedings, pp. 69–72 (2010). doi:10.1109/NEUREL.2010.5644108
Hanen, C., Munier, A.: A study of the cyclic scheduling problem on parallel processors. Discret. Appl. Math. 57(2–3), 167–192 (1995). Combinatorial optimization 1992
KUKA: KUKA Industrial Robots (2014). Available at http://www.kuka-robotics.com/en/. Accessed 3 June 2014
Lampariello, R., Nguyen-Tuong, D., Castellini, C., Hirzinger, G., Peters, J.: Trajectory planning for optimal robot catching in real-time. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3719–3726 (2011)
Le, C.V., Pang, C.K., Gan, O.P., Chee, X.M., Zhang, D.H., Luo, M., Chan, H.L., Lewis, F.L.: Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems. Trans. Inst. Meas. Control. 35(5), 583–592 (2012). doi:10.1177/0142331212460883
Mashaei, M., Lennartson, B.: Energy reduction in a pallet-constrained flow shop through on–off control of idle machines. IEEE Trans. Autom. Sci. Eng. 10(1), 45–56 (2013)
Michna, V., Wagner, P., Cernohorsky, J.: Constrained optimization of robot trajectory and obstacle avoidance. In: IEEE Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4 (2010)
Othman, A., Belda, K., Burget, P.: Physical modelling of energy consumption of industrial articulated robots. In: 15th ICCAS International Conference on Control, Automation and Systems (2015)
Riazi, S., Bengtsson, K., Wigström, O., Vidarsson, E., Lennartson, B.: Energy optimization of multi-robot systems. In: 20th IEEE CASE Conference on Automation Science and Engineering, pp. 1345–1350 (2015)
Ron, M., Burget, P., Fiala, O.: Identification of operations at robotic welding lines. In: 20th IEEE CASE Conference on Automation Science and Engineering, pp. 470–476 (2015)
Saramago Jr., S., Steffen, V.: Optimization of the trajectory planning of robot manipulators taking into account the dynamics of the system. Mech. Mach. Theory 33(7), 883–894 (1998)
Saravanan, R., Ramabalan, S., Balamurugan, C.: Evolutionary optimal trajectory planning for industrial robot with payload constraints. Int. J. Adv. Manuf. Technol. 38(11–12), 1213–1226 (2008)
Sharma, G.: Optimization of energy in robotic arm using genetic algorithm. Int. J. Comput. Sci. Technol. 2(2), 315–317 (2011)
Siciliano, B., Sciavicco, L., et al.: Robotics - Modelling, Planning and Control. Springer, Berlin (2009)
Simon, L., Hungerbuehler, K.: Real time takagi-sugeno fuzzy model based pattern recognition in the batch chemical industry. In: IEEE International Conference on Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence), pp. 779–782 (2008). doi:10.1109/FUZZY.2008.4630459
Smetanová, A.: Optimization of energy by robot movement. Mod. Mach. Sci. J. 3(1), 172–176 (2010)
Vergnano, A., Thorstensson, C., Lennartson, B., Falkman, P., Pellicciari, M., Leali, F., Biller, S.: Modeling and optimization of energy consumption in cooperative multi-robot systems. IEEE Trans. Autom. Sci. Eng. 9(2), 423–428 (2012)
Wigstrom, O., Lennartson, B.: Integrated OR/CP optimization for discrete event systems with nonlinear cost. In: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC), pp. 7627–7633 (2013)
Wigstrom, O., Sundstrom, N., Lennartson, B.: Optimization of hybrid systems with known paths. In: 4th IFAC Conference on Analysis and Design of Hybrid Systems, 2012, pp. 39–45 (2012). doi:10.3182/20120606-3-NL-3011.00007
Wigstrom, O., Lennartson, B., Vergnano, A., Breitholtz, C.: High-level scheduling of energy optimal trajectories. IEEE Trans. Autom. Sci. Eng. 10(1), 57–64 (2013)
Acknowledgements
This work has been conducted in cooperation with Skoda Auto within contract 830-8301343/13135, with support of the Grant Agency of the Czech Technical University in Prague, grant No. SGS13/209/OHK3/3T/13 and the Grant Agency of the Czech Republic under the Project GACR P103-16-23509S.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Burget, P., Bukata, L., Šůcha, P., Ron, M., Hanzálek, Z. (2017). Optimisation of Power Consumption for Robotic Lines in Automotive Industry. In: Ghezzi, L., Hömberg, D., Landry, C. (eds) Math for the Digital Factory. Mathematics in Industry(), vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-63957-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-63957-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63955-0
Online ISBN: 978-3-319-63957-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)