  Coverage path planning is a well-studied problem in robotics in which a robot
must plan a path that passes through every point in a given area repeatedly,
usually with a uniform frequency. To address the scenario in which some points
need to be visited more frequently than others, this problem has been extended
to non-uniform coverage planning. This paper considers the variant of
non-uniform coverage in which the robot does not know the distribution of
relevant events beforehand and must nevertheless learn to maximize the rate of
detecting events of interest. This continual area sweeping problem has been
previously formalized in a way that makes strong assumptions about the
environment, and to date only a greedy approach has been proposed. We
generalize the continual area sweeping formulation to include fewer
environmental constraints, and propose a novel approach based on reinforcement
learning in a Semi-Markov Decision Process. This approach is evaluated in an
abstract simulation and in a high fidelity Gazebo simulation. These evaluations
show significant improvement upon the existing approach in general settings,
which is especially relevant in the growing area of service robotics.
