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Coverage path planning is a well-studied problem in robotics in which a robotmust 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 pointsneed to be visited more frequently than others, this problem has been extendedto non-uniform coverage planning. This paper considers the variant ofnon-uniform coverage in which the robot does not know the distribution ofrelevant events beforehand and must nevertheless learn to maximize the rate ofdetecting events of interest. This continual area sweeping problem has beenpreviously formalized in a way that makes strong assumptions about theenvironment, and to date only a greedy approach has been proposed. Wegeneralize the continual area sweeping formulation to include fewerenvironmental constraints, and propose a novel approach based on reinforcementlearning in a Semi-Markov Decision Process. This approach is evaluated in anabstract simulation and in a high fidelity Gazebo simulation. These evaluationsshow significant improvement upon the existing approach in general settings,which is especially relevant in the growing area of service robotics.

Deep R-Learning for Continual Area Sweeping