Abstract
Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.
Highlights
Autonomous navigation is the key capability of an intelligent robot, enabling it to arrive at the destination on the optimal route
The lack of skilled labor promotes the development of social robots for home renovation. erefore, this paper focuses on the indoor navigation of these robots, especially the efficiency of robotic navigation. e methods for autonomous navigation can be classified as the model-based approaches [4,5,6] and the model-free approaches [7]
To validate the effectiveness of our proposed model, it is fairly compared with A2C and tutorstudent models in the same experiment configuration. e tutor-student model is implemented in a two-stage manner
Summary
Autonomous navigation is the key capability of an intelligent robot, enabling it to arrive at the destination on the optimal route. It plays an important role in many fields, i.e., mobile robotics [1], unmanned aerial vehicles [2], human-machine interaction [3], etc. Model-based methods [4,5,6] explicitly implement robotic location and mapping construction for robotic navigation. Given the positions of a robot and a destination, algorithms [8] can plan the optimal path between them, and dynamic control [9, 10] is used to implement robotic motion and navigation
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