  Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) considers
the problem of an agent equipped with multiple sensors, each with different
sensing accuracy and energy costs. The agent's goal is to explore the
environment and gather information subject to its resource constraints in
unknown, partially observable environments. Previous work has focused on the
less general Adaptive Informative Path Planning (AIPP) problem, which considers
only the effect of the agent's movement on received observations. The AIPPMS
problem adds additional complexity by requiring that the agent reasons jointly
about the effects of sensing and movement while balancing resource constraints
with information objectives. We formulate the AIPPMS problem as a belief Markov
decision process with Gaussian process beliefs and solve it using a sequential
Bayesian optimization approach with online planning. Our approach consistently
outperforms previous AIPPMS solutions by more than doubling the average reward
received in almost every experiment while also reducing the root-mean-square
error in the environment belief by 50%. We completely open-source our
implementation to aid in further development and comparison.
