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Evolutionary motion inverse kinematics

Authors: John Millard;

Evolutionary motion inverse kinematics

Abstract

In disciplines, such as robotics and computer graphics (CG) or industries such as, game development, various tasks involve the manipulation of articulated structures in interesting and complex ways. This may involve controlling robotic manipulators or perhaps posing the skeleton of a virtual human. When performed manually, the task of rotating each individual joint in order to produce a desired pose can be tedious and time consuming. The process of rotating each joint independently is known as forward kinematics (FK). One way to simplify this process is to automatically calculate the angles required for a given joint (known as the end-effector) to reach a goal position - this process is known as inverse kinematics (IK). In this paper an evolutionary algorithm (EA) is proposed as a solution to inverse kinematics. Several extensions are also proposed to allow extra constraints to be included, such as obstacle avoidance and angular joint limits. This approach is called Evolutionary Motion IK (E-MOTION IK).

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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