Hao Ding,
Gunther Reißig,
Dominic Groß,
and Olaf Stursberg.
Mixed-Integer Programming for Optimal Path Planning of Robotic Manipulators.
Proc. 2011 IEEE Conference on Automation Science and Engineering
(CASE), Trieste, Italy, August 24-27, 2011, pp. 133-138.
Full text.
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Abstract:
One of the fundamental problems in the field of robotic motion planning is
to safely and efficiently drive the end effector of a robotic
manipulator to a specified goal position. Here, safety refers
to the requirement that the robotic manipulator must have no collision
with surrounding obstacles, and efficiency requires that
some predefined cost function is minimized. In addition, kinematic and dynamic
constraints have to be satisfied.
These requirements lead to non-convex optimization
problems, which may be approximated by mixed-integer linear
programs (MILPs). The solution of the latter, however, is often
intolerably complex due to a huge number of binary decision variables.
In the present paper, we consider motion planning scenarios with
polyhedral obstacles and velocity constraints for the joint positions
of the robotic manipulator.
We provide a geometric result whose application leads to MILPs with
drastically reduced numbers of binary decision
variables. Computational efficiency is demonstrated for two- and three-link
manipulators interacting with obstacles, where the number of simplex
steps during the MILP solution is reduced by a factor of roughly 200
over previous methods. We also demonstrate the application of the
proposed method to an industrial robot.
BibTeX entry:
@InProceedings{yourchoice,
author = {Hao Ding and Gunther Rei{\ss}ig and Dominic Gro{\ss} and Olaf Stursberg},
title = {Mixed-Integer Programming for Optimal Path Planning of Robotic Manipulators},
booktitle = {Proc. 2011 IEEE Conference on Automation Science and Engineering (CASE), Trieste, Italy, 24-27 August 2011},
pages = {133-138},
year = 2011,
publisher = {IEEE},
doi = {10.1109/CASE.2011.6042462}
}
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