Multi-Robot Path Planning Via Genetic Programming
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gp-bibliography.bib Revision:1.8129
- @Misc{DBLP:journals/corr/abs-1912-09503,
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author = "Alexandre Trudeau and Christopher M. Clark",
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title = "Multi-Robot Path Planning Via Genetic Programming",
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howpublished = "arXiv",
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volume = "abs/1912.09503",
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year = "2019",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://arxiv.org/abs/1912.09503",
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archiveprefix = "arXiv",
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eprint = "1912.09503",
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timestamp = "Fri, 03 Jan 2020 00:00:00 +0100",
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biburl = "https://dblp.org/rec/journals/corr/abs-1912-09503.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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size = "18 pages",
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abstract = "This paper presents a Genetic Programming (GP)
approach to solving multi-robot path planning (MRPP)
problems in single-lane workspaces, specifically those
easily mapped to graph representations. GP's
versatility enables this approach to produce programs
optimizing for multiple attributes rather than a single
attribute such as path length or completeness. When
optimizing for the number of time steps needed to solve
individual MRPP problems, the GP constructed programs
outperformed complete MRPP algorithms, i.e.
Push-Swap-Wait (PSW), by 54.1percent. The GP
constructed programs also consistently outperformed PSW
in solving problems that did not meet PSW's
completeness conditions. Furthermore, the GP
constructed programs exhibited a greater capacity for
scaling than PSW as the number of robots navigating
within an MRPP environment increased. This research
illustrates the benefits of using Genetic Programming
for solving individual MRPP problems, including
instances in which the number of robots exceeds the
number of leaves in the tree-modeled work space.",
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notes = "ARMS 2019 Workshop (AAMAS)",
- }
Genetic Programming entries for
Alexandre Trudeau
Christopher M Clark
Citations