  We consider the problem of an autonomous agent equipped with multiple
sensors, each with different sensing precision 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. The
challenge lies in reasoning about the effects of sensing and movement while
respecting the agent's resource and dynamic constraints. We formulate the
problem as a trajectory optimization problem and solve it using a
projection-based trajectory optimization approach where the objective is to
reduce the variance of the Gaussian process world belief. Our approach
outperforms previous approaches in long horizon trajectories by achieving an
overall variance reduction of up to 85% and reducing the root-mean square error
in the environment belief by 50%. This approach was developed in support of
rover path planning for the NASA VIPER Mission.
