Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Naredo:2023:MCS,
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author = "Enrique Naredo and Candelaria Sansores and
Flaviano Godinez and Francisco Lopez and Paulo Urbano and
Leonardo Trujillo and Conor Ryan",
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title = "Comprehensive Analysis of Learning Cases in an
Autonomous Navigation Task for the Evolution of General
Controllers",
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journal = "Mathematical and Computational Applications",
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year = "2023",
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volume = "28",
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number = "2",
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month = mar,
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, navigation robotics, generalisation",
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publisher = "MDPI AG",
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ISSN = "2297-8747",
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DOI = "doi:10.3390/mca28020035",
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size = "15 pages",
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abstract = "Robotics technology has made significant advancements
in various fields in industry and society. It is clear
how robotics has transformed manufacturing processes
and increased productivity. Additionally, navigation
robotics has also been impacted by these advancements,
with investors now investing in autonomous
transportation for both public and private use. This
research aims to explore how training scenarios affect
the learning process for autonomous navigation tasks.
The primary objective is to address whether the initial
conditions (learning cases) have a positive or negative
impact on the ability to develop general controllers.
By examining this research question, the study seeks to
provide insights into how to optimize the training
process for autonomous navigation tasks, ultimately
improving the quality of the controllers that are
developed. Through this investigation, the study aims
to contribute to the broader goal of advancing the
field of autonomous navigation and developing more
sophisticated and effective autonomous systems.
Specifically, we conducted a comprehensive analysis of
a particular navigation environment using evolutionary
computing to develop controllers for a robot starting
from different locations and aiming to reach a specific
target. The final controller was then tested on a large
number of unseen test cases. Experimental results
provide strong evidence that the initial selection of
the learning cases plays a role in evolving general
controllers. This work includes a preliminary analysis
of a specific set of small learning cases chosen
manually, provides an in-depth analysis of learning
cases in a particular navigation task, and develops a
tool that shows the impact of the selected learning
cases on the overall behaviour of a robots
controller.",
- }
Genetic Programming entries for
Enrique Naredo
Candelaria Elizabeth Sansores Perez
Flaviano Godinez
Francisco Lopez Valverde
Paulo Urbano
Leonardo Trujillo
Conor Ryan
Citations