Research Papers

Energy-Efficient Robot Configuration for Assembly

[+] Author and Article Information
Abdullah Mohammed

Department of Production Engineering,
KTH Royal Institute of Technology,
Brinellvägen 68,
Stockholm 100 44, Sweden
e-mail: agmo@kth.se

Bernard Schmidt

School of Engineering Science,
University of Skövde,
P.O. Box 408,
Skövde SE-541 28, Sweden
e-mail: bernard.schmidt@his.se

Lihui Wang

Fellow ASME
Department of Production Engineering,
KTH Royal Institute of Technology,
Brinellvägen 68,
Stockholm 100 44, Sweden
e-mail: lihuiw@kth.se

1Corresponding author.

Manuscript received July 19, 2016; final manuscript received October 6, 2016; published online November 14, 2016. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 139(5), 051007 (Nov 14, 2016) (7 pages) Paper No: MANU-16-1394; doi: 10.1115/1.4034935 History: Received July 19, 2016; Revised October 06, 2016

Optimizing the energy consumption of robot movements has been one of the main focuses for most of today's robotic simulation software. This optimization is based on minimizing a robot's joint movements. In many cases, it does not take into consideration the dynamic features. Therefore, reducing energy consumption is still a challenging task and it involves studying the robot's kinematic and dynamic models together with application requirements. This research aims to minimize the robot energy consumption during assembly. Given a trajectory and based on the inverse kinematics and dynamics of a robot, a set of attainable configurations for the robot can be determined, perused by calculating the suitable forces and torques on the joints and links of the robot. The energy consumption is then calculated for each configuration and based on the assigned trajectory. The ones with the lowest energy consumption are selected. Given that the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be embedded in energy-efficient robotic assembly.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.


Okwudire, C. , and Rodgers, J. , 2013, “ Design and Control of a Novel Hybrid Feed Drive for High Performance and Energy Efficient Machining,” CIRP Ann.-Manuf. Technol., 62(1), pp. 391–394. [CrossRef]
Liu, Z. , Bu, W. , and Tan, J. , 2010, “ Motion Navigation for Arc Welding Robots Based on Feature Mapping in a Simulation Environment,” Rob. Comput.-Integr. Manuf., 26(2), pp. 137–144. [CrossRef]
Saravanan, R. , Ramabalan, S. , and Balamurugan, C. , 2009, “ Evolutionary Multi-Criteria Trajectory Modeling of Industrial Robots in the Presence of Obstacles,” Eng. Appl. Artif. Intell., 22(2), pp. 329–342. [CrossRef]
de Santos, P. G. , Garcia, E. , Ponticelli, R. , and Armada, M. , 2009, “ Minimizing Energy Consumption in Hexapod Robots,” Adv. Rob., 23(6), pp. 681–704. [CrossRef]
Vanderborght, B. , Tsagarakis, N. G. , Van Ham, R. , Thorson, I. , and Caldwell, D. G. , 2011, “ MACCEPA 2.0: Compliant Actuator Used for Energy Efficient Hopping Robot Chobino1D,” Auton. Rob., 31(1), pp. 55–65. [CrossRef]
Rahimifard, S. , Seow, Y. , and Childs, T. , 2010, “ Minimising Embodied Product Energy to Support Energy Efficient Manufacturing,” CIRP Ann.-Manuf. Technol., 59(1), pp. 25–28. [CrossRef]
Weinert, N. , Chiotellis, S. , and Seliger, G. , 2011, “ Methodology for Planning and Operating Energy-Efficient Production Systems,” CIRP Ann.-Manuf. Technol., 60(1), pp. 41–44. [CrossRef]
Mourtzis, D. , Vlachou, E. , Xanthopoulos, N. , Givehchi, M. , and Wang, L. , 2016, “ Cloud-Based Adaptive Process Planning Considering Availability and Capabilities of Machine Tools,” J. Manuf. Syst., 39, pp. 1–8. [CrossRef]
Wang, L. , 2013, “ Machine Availability Monitoring and Machining Process Planning Towards Cloud Manufacturing,” CIRP J. Manuf. Sci. Technol., 6(4), pp. 263–273. [CrossRef]
Mori, M. , Fujishima, M. , Inamasu, Y. , and Oda, Y. , 2011, “ A Study on Energy Efficiency Improvement for Machine Tools,” CIRP Ann.-Manuf. Technol., 60(1), pp. 145–148. [CrossRef]
Bi, Z. M. , and Wang, L. , 2012, “ Optimization of Machining Processes From the Perspective of Energy Consumption—A Case Study,” J. Manuf. Syst., 31(4), pp. 420–428. [CrossRef]
Vijayaraghavan, A. , and Dornfeld, D. , 2010, “ Automated Energy Monitoring of Machine Tools,” CIRP Ann.-Manuf. Technol., 59(1), pp. 21–24. [CrossRef]
Behrendt, T. , Zein, A. , and Min, S. , 2012, “ Development of an Energy Consumption Monitoring Procedure for Machine Tools,” CIRP Ann.-Manuf. Technol., 61(1), pp. 43–46. [CrossRef]
Peng, T. , Xu, X. , and Wang, L. , 2014, “ A Novel Energy Demand Modelling Approach for CNC Machining Based on Function Blocks,” J. Manuf. Syst., 33(1), pp. 196–208. [CrossRef]
Bi, Z. M. , and Wang, L. , 2012, “ Energy Modelling of Machine Tools for Optimization of Machine Setups,” IEEE Trans. Autom. Sci. Eng., 9(3), pp. 607–613. [CrossRef]
Mohammed, A. , Schmidt, B. , Wang, L. , and Gao, L. , 2014, “ Minimizing Energy Consumption for Robot Arm Movement,” Procedia CIRP, 25, pp. 400–405. [CrossRef]
Vergnano, A. , Thorstensson, C. , Lennartson, B. , Falkman, P. , Pellicciari, M. , Leali, F. , and Biller, S. , 2012, “ Modeling and Optimization of Energy Consumption in Cooperative Multi-Robot Systems,” TASE, 9(2), pp. 423–428.
Pellicciari, M. , Berselli, G. , Leali, F. , and Vergnano, A. , 2013, “ A Method for Reducing the Energy Consumption of Pick-and-Place Industrial Robots,” Mechatronics, 23(3), pp. 326–334. [CrossRef]
Verscheure, D. , Demeulenaere, B. , Swevers, J. , De Schutter, J. , and Diehl, M. , 2009, “ Time-Optimal Path Tracking for Robots: A Convex Optimization Approach,” IEEE Trans. Autom. Control, 54(10), pp. 2318–2327. [CrossRef]
Gauthier, J. F. , Angeles, J. , and Nokleby, S. , 2006, “ Optimization of a Test Trajectory for SCARA Systems,” Adv. Rob. Kinematics: Anal. Des., Eds. Lenarčič, J. and Wenger P., Springer, The Netherlands, pp. 225–234.
Brossog, M. , Kohl, J. , Merhof, J. , Spreng, S. , and Franke, J. , 2014, “ Energy Consumption and Dynamic Behavior Analysis of a Six-Axis Industrial Robot in an Assembly System,” Procedia CIRP, 23, pp. 131–136. [CrossRef]
Ystgaard, P. , Gjerstad, T. B. , Lien, T. K. , and Nyen, P. A. , 2012, “ Mapping Energy Consumption for Industrial Robots,” Leveraging Technology for a Sustainable World, Berkeley, CA.
Chhabra, R. , and Emami, M. R. , 2011, “ Holistic System Modeling in Mechatronics,” Mechatronics, 21(1), pp. 166–175. [CrossRef]
Gielen, D. , and Taylor, M. , 2007, “ Modelling Industrial Energy Use: The IEAs Energy Technology Perspectives,” Energy Econ., 29(4), pp. 889–912. [CrossRef]
Gregory, J. , Olivares, A. , and Staffetti, E. , 2012, “ Energy-Optimal Trajectory Planning for Robot Manipulators With Holonomic Constraints,” Syst. Control Lett., 61(2), pp. 279–291. [CrossRef]
Saravanan, R. , Ramabalan, S. , and Balamurugan, C. , 2008, “ Evolutionary Optimal Trajectory Planning for Industrial Robot With Payload Constraints,” Int. J. Adv. Manuf. Technol., 38(11), pp. 1213–1226. [CrossRef]
Sergaki, E. S. , Stavrakakis, G. S. , and Pouliezos, A. D. , 2002, “ Optimal Robot Speed Trajectory by Minimization of the Actuator Motor Electromechanical Losses,” J. Intell. Rob. Syst., 33(2), pp. 187–207. [CrossRef]
Hansen, C. , Oltjen, J. , Meike, D. , and Ortmaier, T. , “ Enhanced Approach for Energy-Efficient Trajectory Generation of Industrial Robots,” IEEE International Conference on Automation Science and Engineering (CASE), Seoul, Korea, Aug. 20–24, pp. 1–7.
Matthias, P. , and Martin, B. , 2015, “ Reducing the Energy Consumption of Industrial Robots in Manufacturing Systems,” Int. J. Adv. Manuf. Technol., 78(5), pp. 1315–1328.
Denavit, J. , and Hartenberg, R. S. , 1955, “ A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices,” ASME J. Appl. Mech., 22(2), pp. 215–221.
Wang, L. , Sams, R. , Verner, M. , and Xi, F. , 2003, “ Integrating Java 3D Model and Sensor Data for Remote Monitoring and Control,” Rob. Comput.-Integr. Manuf., 19(1–2), pp. 13–19. [CrossRef]
Luh, J. Y. S. , Walker, M. W. , and Paul, R. P. C. , 1980, “ On-Line Computational Scheme for Mechanical Manipulators,” ASME J. Dyn. Syst., Meas., Control, 102(2), pp. 69–76. [CrossRef]


Grahic Jump Location
Fig. 1

System configuration

Grahic Jump Location
Fig. 2

(a) Robot's frame assignments and (b) D–H parameters

Grahic Jump Location
Fig. 3

The robot's joint 1 projected on X–Y plane

Grahic Jump Location
Fig. 4

The robot's joints 1 and 2 projection

Grahic Jump Location
Fig. 5

An energy map in the workspace of an ABB IRB 1600 robot

Grahic Jump Location
Fig. 6

The hypothetical paths

Grahic Jump Location
Fig. 7

The output of the energy optimization for the hypothetical paths

Grahic Jump Location
Fig. 8

The measurements on the real robot of the hypothetical paths




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In