Mobile robot navigation based on Deep Reinforcement Learning: A brief review

Abstract

Abstract Navigation capacity is a key attribute of robot technology and the foundation for achieving other advanced behaviours. Compared to traditional navigation technology, applying Deep Reinforcement Learning (DRL) to artificial intelligence agents to achieve mobile robot navigation function is currently the academic focus. DRL is based on an end-to-end approach, transforming high-dimensional and continuous inputs into optimal policy to guide mobile robots, forming an advanced perceptual control system. In this article, DRL is first compared with traditional navigation technology and SLAM, and its application advantages are elucidated. Then, the basic background and classic algorithm models of standard reinforcement learning and DRL are systematically elaborated. Finally, the application of DRL in different application scenarios and research fields is introduced.