Reinforcement learning based motion planning of quadrotors using motion primitives - Efe Camci (Nanyang Technological University, 2020)
Motion planning of robots in real world is challenging due to the uncertainty in environments and robot models, the computation and sensing limitations on hardware, and the complexity of the tasks to be performed during operations. Motivated by these problems, the main contribution of this thesis is a Reinforcement Learning (RL) based approach for motion planning of quadrotors that can deal with uncertainties, work with rudimentary hardware, and minimize expert user intervention during complex operations. Aligned with the hierarchical motion planning pipeline of quadrotors, i.e., high-level, position, and orientation planning, we propose a novel planning framework at each domain based upon RL.