Emerging applications for robotic data collection include ocean monitoring, emergency
response and urban search and rescue. At the core of these applications is
a robot's ability to make informed decisions on incomplete data. This dissertation
addresses this problem by developing novel techniques for modeling and estimating
structured environments using deep learning. The proposed methods improve the
eciency of robotics systems across a wide array of applications and scenarios.
A challenging problem in robotics is to predict future observations based on
previously-recorded data. Robots often operate in built environments that tend to
contain some underlying structure, such that newly-visited locations may appear
broadly similar to previously visited locations but dier in individual details. The
proposed technique exploits the inherent structure of the environment to train a
convolutional neural network that is leveraged to facilitate robotic search. We start
by investigating environments where the full environmental structure is known, and then we extend the work to unknown environments. Experimental results show the
proposed framework provides a reliable method for decreasing the area searched
to nd a point of interest. We demonstrate the proposed framework increases the
search eciency of a mobile robot in a real-world oce environment.
To utilize uncertainty in our decision making and account for dynamic environments,
we propose a convolutional LSTM network with bootstrapped condence
bounds as a method for modeling spatio-temporal data. By providing estimates
with condence bounds that are accurate far into the future, multi-step planners
can be utilized to improve performance on information gathering missions. This
technique is compared to existing environment modeling techniques. We demonstrate
that our proposed approach constructs long-horizon estimates with greater
accuracy. We also achieve more accurate and more conservative condence bounds.
Validation through simulation shows our technique increases path planning performance
in environmental information gathering missions.
Robots often require a model of their environment to make informed decisions.
In unknown environments, the ability to infer the value of a data eld from
a limited number of samples is essential to many robotics applications. In this
dissertation, we propose a neural network architecture to model these spatially
correlated data elds based on a limited number of spatially continuous samples.
Additionally, we provide a method based on biased loss functions to suggest future
areas of exploration to minimize reconstruction error. We run simulated robotic
information gathering trials on both the MNIST hand written digits dataset and
a Regional Ocean Modeling System (ROMS) ocean dataset for ocean monitoring. Our method outperforms Gaussian process regression in both environments for
modeling the data eld and action selection.