PhD Oral Exam - Yintao Zhang, Mechanical Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Uncrewed Aerial Vehicles (UAVs) are playing an increasingly significant role in modern life. In the past decades, lots of commercial and scientific communities all over the world have been developing autonomous techniques of UAV for a broad range of applications, such as forest fire monitoring, parcel delivery, disaster rescue, natural resource exploration, and surveillance. This brings a large number of opportunities and challenges for UAVs to improve their abilities in path planning, motion control and fault-tolerant control (FTC) directions. Meanwhile, due to the powerful decision making, adaptive learning and pattern recognition capabilities of machine learning (ML) and deep reinforcement learning (DRL), the use of ML and DRL have been developing rapidly and obtain major achievement in a variety of applications.

However, there is not many researches on the ML and DRL in the field of motion control and real-time path planning of UAVs. This thesis focuses on the development of ML and DRL in the path planning, motion control and FTC of UAVs. A number of contributions pertaining to the state space definition, reward function design and training method improvement have been made in this thesis, which improve the effectiveness and efficiency of applying DRL in UAV motion control problems. In addition to the control problems, this thesis also presents real-time path planning contributions, including relative state space definition and human pedestrian inspired reward function, which provide a reliable and effective solution of the real-time path planning in a complex environment.