A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking

Localization of odors is essential to animal survival, and thus animals
are adept at odor-navigation. In natural conditions animals encounter odor
sources in which odor is carried by air flow varying in complexity. We
sought to identify potential minimalist strategies that can effectively be
used for odor-based navigation and asses their performance in an
increasingly chaotic environment. To do so, we compared mouse, in silico
model, and Arduino-based robot odor-localization behavior in a
standardized odor landscape. Mouse performance remains robust in the
presence of increased complexity, showing a shift in strategy towards
faster movement with increased environmental complexity. Implementing
simple binaral and temporal models of tropotaxis and klinotaxis, an in
silico model and Arduino robot, in the same environment as the mice, are
equally successful in locating the odor source within a plume of low
complexity. However,  performance of these algorithms
significantly drops when the chaotic nature of the plume is increased.
Additionally, both algorithm-driven systems show more successful
performance when using a strictly binaral model at a larger sensor
separation distance and more successful performance when using a temporal
and binaral model when using a smaller sensor separation distance. This
suggests that with an increasingly chaotic odor environment, mice rely on
complex strategies that allow for robust odor localization that cannot be
resolved by minimal algorithms that display robust performance at low
levels of complexity. Thus, highlighting that an animal’s ability to
modulate behavior with environmental complexity is beneficial for odor
localization.