Genetic Programming-Based Inverse Kinematics for Robotic Manipulators
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Reuter:2022:EuroGP,
-
author = "Julia Reuter and Christoph Steup and Sanaz Mostaghim",
-
title = "Genetic Programming-Based Inverse Kinematics for
Robotic Manipulators",
-
booktitle = "EuroGP 2022: Proceedings of the 25th European
Conference on Genetic Programming",
-
year = "2022",
-
editor = "Eric Medvet and Gisele Pappa and Bing Xue",
-
series = "LNCS",
-
volume = "13223",
-
publisher = "Springer Verlag",
-
address = "Madrid, Spain",
-
pages = "130--145",
-
month = "20-22 " # apr,
-
organisation = "EvoStar, Species",
-
keywords = "genetic algorithms, genetic programming, Cooperative
Coevolution, Multi-Objective Optimization, Inverse
Kinematics",
-
isbn13 = "978-3-031-02055-1",
-
DOI = "doi:10.1007/978-3-031-02056-8_9",
-
abstract = "we introduce an inverse kinematics model for a robotic
manipulator using Genetic Programming (GP). The
underlying problem requires learning of multiple joint
parameters of the manipulator to reach a desired
position in the Cartesian space. We present a new
approach to identify a closed-form solution for the
Inverse Kinematics (IK) problem, namely IK-CCGP. The
novelty of IK-CCGP is the cooperative coevolutionary
learning strategy. Unlike other GP approaches, IK-CCGP
is not limited to a certain angle combination to reach
a given pose and is designed to achieve more
flexibility in the learning process. Moreover, it can
operate both as single- and multi-objective variants.
In this paper, we investigate whether the inclusion of
further objectives, i.e. correlation and the
consistency of a solution with physical laws,
contributes to the search process. Our experiments show
that the combination of the two objectives, error and
correlation, performs very well for the given problem
and IK-CCGP performs the best on a kinematic unit of
two joints. While our approach cannot attain the same
accuracy as Artificial Neural Networks, it overcomes
the explainability gap of IK models developed using
ANNs.",
-
notes = "http://www.evostar.org/2022/eurogp/ Part of
\cite{Medvet:2022:GP} EuroGP'2022 held inconjunction
with EvoApplications2022 EvoCOP2022 EvoMusArt2022",
- }
Genetic Programming entries for
Julia Reuter
Christoph Steup
Sanaz Mostaghim
Citations