Learning-based autonomous navigation for mobile robot in unknown environments - Wen, Mingxing (Nanyang Technological University, 2023)
As an essential capability for mobile robots, autonomous navigation has been extensively studied for decades. However, it remains challenging due to the inherent complexity and diversity of the problem, especially when the robots are deployed in unknown environments. The traditional methods highly rely on maintaining a global consistent map to re-localize, plan and control the mobile robots, which significantly increase the computation complexity. Besides, they often include a quantity of manually designed and tuned parameters, leading to poor generalizability. To overcome the bottlenecks of traditional methods, learning-based approaches have gained much popularity in the past years. Most of the existing learning-based approaches to autonomous navigation in unknown environments are subject to certain challenges such as poor interpretability (End-to-End manner), high dependence on large labelled data set, lack of robust and affordable perception capability, and sample inefficiency. This thesis has investigated several machine learning methods to address the problems mentioned above, and proposes a novel learning framework for learning autonomous navigation efficiently in unknown environments.