An improved frontier-based robot exploration strategy combined with deep reinforcement learning

The map of the environment is the basis for autonomous robot navigation. This paper introduces an improved approach to frontier-based exploration by utilizing deep reinforcement learning to select target points. This study proposes a novel approach for map sampling and developing a corresponding neural network architecture. Our method aims to adapt effectively to unfamiliar environments with varying dimensions and diverse action spaces while reducing the loss of information caused by map sampling. We train and validate the neural network in a simulation environment. The results show that our proposed method can stably explore unknown environments of different sizes, while the distance traveled to complete the exploration is shorter than other methods. In addition, we conducted experiments on a real robot, and the results show that our method can be easily transferred from the simulation environment to the real environment.