Evolving Infotaxis for Meandering Environments
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- @InProceedings{Macedo:2021:IROS,
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author = "Joao Macedo and Lino Marques and Ernesto Costa",
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title = "Evolving Infotaxis for Meandering Environments",
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booktitle = "2021 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS)",
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year = "2021",
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pages = "8431--8436",
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abstract = "Locating odour sources with mobile robots is a
difficult task with many real world applications. Over
the years, researchers have devised bio-inspired and
cognitive methods to enable mobile robots to fulfil
this task. One of the most popular cognitive approaches
is Infotaxis, which computes a probability map for the
location of the chemical source and, on each time step,
moves the robot in the direction that minimises the
entropy of that probability map. The main difficulty
for applying Infotaxis in the real world is selecting
proper values for the parameters of its internal gas
dispersion model, as it has been shown that its
performance is greatly influenced by the accuracy of
said model. This work proposes a Genetic Algorithm for
optimising those parameters for specific environments.
The proposed method is applied to environments with
distinct wind and odour dispersion characteristics and
the resulting parameters are compared. Moreover, the
performance of Infotaxis is compared to that of
reactive search strategies evolved by Geometric
Syntactic Genetic Programming. The statistically
validated results show that the evolved reactive
strategies achieve equivalent success rates to
Infotaxis, while being significantly faster. Real world
experiments conducted in a controlled wind tunnel
validated the simulation results.",
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keywords = "genetic algorithms, genetic programming, Biological
system modelling, Atmospheric modelling, Wind tunnels,
Syntactics, Mobile robots, Task analysis",
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DOI = "doi:10.1109/IROS51168.2021.9636779",
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ISSN = "2153-0866",
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month = sep,
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notes = "Also known as \cite{9636779}",
- }
Genetic Programming entries for
Joao Macedo
Lino Marques
Ernesto Costa
Citations