  Search missions require motion planning and navigation methods for
information gathering that continuously replan based on new observations of the
robot's surroundings. Current methods for information gathering, such as Monte
Carlo Tree Search, are capable of reasoning over long horizons, but they are
computationally expensive. An alternative for fast online execution is to
train, offline, an information gathering policy, which indirectly reasons about
the information value of new observations. However, these policies lack safety
guarantees and do not account for the robot dynamics. To overcome these
limitations we train an information-aware policy via deep reinforcement
learning, that guides a receding-horizon trajectory optimization planner. In
particular, the policy continuously recommends a reference viewpoint to the
local planner, such that the resulting dynamically feasible and collision-free
trajectories lead to observations that maximize the information gain and reduce
the uncertainty about the environment. In simulation tests in previously unseen
environments, our method consistently outperforms greedy next-best-view
policies and achieves competitive performance compared to Monte Carlo Tree
Search, in terms of information gains and coverage time, with a reduction in
execution time by three orders of magnitude.
