  In this paper, we study the problem of coverage of an environment with an
energy-constrained robot in the presence of multiple charging stations. As the
robot's on-board power supply is limited, it might not have enough energy to
cover all the points in the environment with a single charge. Instead, it will
need to stop at one or more charging stations to recharge its battery
intermittently. The robot cannot violate the energy constraint, i.e., visit a
location with negative available energy. To solve this problem, we propose a
deep Q-learning framework that produces a policy to maximize the coverage and
minimize the budget violations. Our proposed framework also leverages the
memory of a recurrent neural network (RNN) to better suit this multi-objective
optimization problem. We have tested the presented framework within a 16 x 16
grid environment having charging stations and various obstacle configurations.
Results show that our proposed method finds feasible solutions and outperforms
a comparable existing technique.
