  Planning informative trajectories while considering the spatial distribution
of the information over the environment, as well as constraints such as the
robot's limited battery capacity, makes the long-time horizon persistent
coverage problem complex. Ergodic search methods consider the spatial
distribution of environmental information while optimizing robot trajectories;
however, current methods lack the ability to construct the target information
spatial distribution for environments that vary stochastically across space and
time. Moreover, current coverage methods dealing with battery capacity
constraints either assume simple robot and battery models, or are
computationally expensive. To address these problems, we propose a framework
called Eclares, in which our contribution is two-fold. 1) First, we propose a
method to construct the target information spatial distribution for ergodic
trajectory optimization using clarity, an information measure bounded between
[0,1]. The clarity dynamics allows us to capture information decay due to lack
of measurements and to quantify the maximum attainable information in
stochastic spatiotemporal environments. 2) Second, instead of directly tracking
the ergodic trajectory, we introduce the energy-aware (eware) filter, which
iteratively validates the ergodic trajectory to ensure that the robot has
enough energy to return to the charging station when needed. The proposed eware
filter is applicable to nonlinear robot models and is computationally
lightweight. We demonstrate the working of the framework through a simulation
case study.
