Safe motion planning and learning for unmanned aerial systems

dc.contributor.advisor İnalhan, Gökhan Perk, Barış Eren
dc.contributor.authorID 511142104
dc.contributor.department Aeronautics and Astronautics Engineering 2023-12-08T06:49:44Z 2023-12-08T06:49:44Z 2022-05-06
dc.description Thesis(Ph.D) -- Istanbul Technical University, Graduate School, 2022
dc.description.abstract To control unmanned aerial systems, we rarely have a perfect system model. Safe and aggressive planning is also challenging for nonlinear and under-actuated systems. Expert pilots, however, demonstrate maneuvers that are deemed at the edge of plane envelope. Inspired by biological systems, in this paper, we introduce a framework that leverages methods in the field of control theory and reinforcement learning to generate feasible, possibly aggressive, trajectories. For the control policies, Dynamic Movement Primitives (DMPs) imitate pilot-induced primitives, and DMPs are combined in parallel to generate trajectories to reach original or different goal points. The stability properties of DMPs and their overall systems are analyzed using contraction theory. For reinforcement learning, Policy Improvement with Path Integrals (PI2) was used for the maneuvers. The results in this paper show that PI2 updated policies are a feasible and parallel combination of different updated primitives transfer the learning in the contraction regions. Our proposed methodology can be used to imitate, reshape, and improve feasible, possibly aggressive, maneuvers. In addition, we can exploit trajectories generated by optimization methods, such as Model Predictive Control (MPC), and a library of maneuvers can be instantly generated. For application, 3-DOF (degrees of freedom) Helicopter and 2D-UAV (unmanned aerial vehicle) models are utilized to demonstrate the main results. Ph.D.
dc.language.iso en_US
dc.publisher Graduate School
dc.sdg.type Goal 9: Industry, Innovation and Infrastructure
dc.sdg.type Goal 8: Decent Work and Economic Growth
dc.subject safe motion planning
dc.subject güvenli hareket planlama
dc.subject unmanned aerial systems
dc.subject İnsansız hava sistemleri
dc.subject helicopter
dc.subject helikopter
dc.title Safe motion planning and learning for unmanned aerial systems
dc.title.alternative İnsansız hava sistemleri için güvenli hareket planlama ve öğrenme
dc.type Doctoral Thesis
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