  Path planning for robotic coverage is the task of determining a
collision-free robot trajectory that observes all points of interest in an
environment. Robots employed for such tasks are often capable of exercising
active control over onboard observational sensors during navigation. In this
paper, we tackle the problem of planning robot and sensor trajectories that
maximize information gain in such tasks where the robot needs to cover points
of interest with its sensor footprint. Search-based planners in general
guarantee completeness and provable bounds on suboptimality with respect to an
underlying graph discretization. However, searching for kinodynamically
feasible paths in the joint space of robot and sensor state variables with
standard search is computationally expensive. We propose two alternative
search-based approaches to this problem. The first solves for robot and sensor
trajectories independently in decoupled state spaces while maintaining a
history of sensor headings during the search. The second is a two-step approach
that first quickly computes a solution in decoupled state spaces and then
refines it by searching its local neighborhood in the joint space for a better
solution. We evaluate our approaches in simulation with a kinodynamically
constrained unmanned aerial vehicle performing coverage over a 2D environment
and show their benefits.
