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Bayesian Models for Science-Driven Robotic Exploration

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posted on 07.12.2021, 18:44 by Alberto Candela GarzaAlberto Candela Garza
Planetary rovers allow for science investigations in remote environments. They
have traversed many kilometers and made major scientific discoveries. However,
rovers spend a considerable amount of time awaiting instructions from mission control.
The reason is that they are designed for highly supervised data collection, not
for autonomous exploration. The exploration of farther worlds will face increasing
challenges and constraints. Such missions will demand a new approach.
This work advocates Bayesian models as powerful tools for a new paradigm
for robotic explorers. In this approach, the explorer’s description of where to go
is not prescribed by a fixed set of instructions, but instead by a model of what the
explorer believes. This formulation has several benefits. Bayesian models provide
a mathematically grounded framework to reason about uncertainty. They can allow
robots to gain a deeper understanding of the evolving scientific goals guiding the
mission. Furthermore, they can empower scientists by providing explainable results.
To this end, this research develops models that allow for data interpretation by
learning and exploiting structure in the data and the environment. It shows how these
models enable robotic explorers to make intelligent decisions based on instantaneous
information. Ultimately, it demonstrates how science productivity is improved by
measuring science value with information-theoretic variables and by formulating
the exploration problem in terms of Bayesian experimental design.
This work makes several contributions to the field of science-driven robotic exploration.
First, it introduces three different deep generative models for the analysis
of data that enable scientists to quantify and interpret learned statistical dependencies.
Then, it presents an adaptive exploration model that leverages contextual information
from remote data to efficiently extrapolate features from in situ observations,
as well as a corresponding strategy for improving science productivity. Afterward, it
establishes a hierarchical probabilistic structure in which scientists initially describe
their abstract beliefs and hypotheses, and then this belief evolves as the robot makes
raw measurements; additionally, science information gain is efficiently computed
and maximized. Finally, it proposes a comprehensive model for planetary rover exploration
that considers both science productivity and risk.
The presented Bayesian models are validated and evaluated in various science
investigation scenarios that can provably benefit from autonomous robotic exploration.
Such scenarios include terrestrial, airless, and Martian surface surveys, as
well as marine biology studies. Emphasis is placed on spectroscopic data, which is
widely used in the natural sciences for composition analysis. Promising results are
shown in simulations and field experiments using the autonomous rover Zo¨e.