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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.

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References

Figures

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Fig. 3

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

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Fig. 4

The robot's joints 1 and 2 projection

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Fig. 2

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

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Fig. 1

System configuration

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Fig. 6

The hypothetical paths

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Fig. 7

The output of the energy optimization for the hypothetical paths

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Fig. 5

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

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Fig. 8

The measurements on the real robot of the hypothetical paths

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