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Autonomous Landing of Unmanned Aeria...

Rasul, Mushahid I.

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Autonomous Landing of Unmanned Aerial Vehicles on Mobile Landing Platforms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Autonomous Landing of Unmanned Aerial Vehicles on Mobile Landing Platforms./
作者:	Rasul, Mushahid I.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	63 p.
附註:	Source: Masters Abstracts International, Volume: 85-07.
Contained By:	Masters Abstracts International85-07.
標題:	Engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30636857
ISBN:	9798381415650

Autonomous Landing of Unmanned Aerial Vehicles on Mobile Landing Platforms.
Rasul, Mushahid I.

Autonomous Landing of Unmanned Aerial Vehicles on Mobile Landing Platforms. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 63 p.

Source: Masters Abstracts International, Volume: 85-07.

Thesis (M.S.)--North Carolina Agricultural and Technical State University, 2023.

This item must not be sold to any third party vendors.

The relevance of unmanned aerial vehicles (UAVs) has witnessed significant growth, especially in civilian and military applications. However, ensuring safe and reliable UAV landing remains a persistent challenge, primarily due to factors such as human error, adverse weather conditions, complex train, and unforeseeable circumstances. This thesis addresses this issue by introducing two advanced automated flight control methods, the Height-Adaptive Proportional Integral Derivative controller (HPID) and the Perception-Aware Model Predictive Controller (PAMPC) with the aim of achieving autonomous landing of UAVs on mobile unmanned ground vehicles (UGVs).To aid in landing, the HPID controller utilizes a prediction algorithm (Kalman filter), which returns a vector of future locations of the center of the UGV. The first element in this vector (indexed zero) corresponds to the current position of the landing platform. The next element (indexed one) corresponds to the next predicted position after a user-defined time step. Correspondingly, the element with index two corresponds to a prediction carried out with twice the defined time step. In general, the number of steps in this path of predicted positions is computed as the ratio between a user-provided path time and the time step.On the other hand, the PAMPC operates with a fading horizon approach, continuously solving non-linear optimization problems in the given constraints and boundaries. The algorithm takes into account the system dynamics of the UAV and uses them as inputs to the system. The system outputs a vector of control inputs that are solved using a cost function from point A to point B. The algorithm is fed the new states of the UAV and the algorithm is solved once again. The loop continues until the UAV has successfully reached the desired reference point.The research methodology is built by system modeling, controller design, testing, simulations, and experimentations. Performance evaluation is conducted through data analysis, and data visualization. The results unequivocally demonstrate the superior performance of the PAMPC, outperforming the HPID controller in the context of autonomous UAVs landing on mobile landing platforms on complex terrain.This study demonstrates the use of HPID and PAMPC controllers for multiple UAVs and UGVs. The system's design ensures robustness and scalability in simulations and experimentations.

ISBN: 9798381415650Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Unmanned aerial vehicles

Autonomous Landing of Unmanned Aerial Vehicles on Mobile Landing Platforms.
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520 $a The relevance of unmanned aerial vehicles (UAVs) has witnessed significant growth, especially in civilian and military applications. However, ensuring safe and reliable UAV landing remains a persistent challenge, primarily due to factors such as human error, adverse weather conditions, complex train, and unforeseeable circumstances. This thesis addresses this issue by introducing two advanced automated flight control methods, the Height-Adaptive Proportional Integral Derivative controller (HPID) and the Perception-Aware Model Predictive Controller (PAMPC) with the aim of achieving autonomous landing of UAVs on mobile unmanned ground vehicles (UGVs).To aid in landing, the HPID controller utilizes a prediction algorithm (Kalman filter), which returns a vector of future locations of the center of the UGV. The first element in this vector (indexed zero) corresponds to the current position of the landing platform. The next element (indexed one) corresponds to the next predicted position after a user-defined time step. Correspondingly, the element with index two corresponds to a prediction carried out with twice the defined time step. In general, the number of steps in this path of predicted positions is computed as the ratio between a user-provided path time and the time step.On the other hand, the PAMPC operates with a fading horizon approach, continuously solving non-linear optimization problems in the given constraints and boundaries. The algorithm takes into account the system dynamics of the UAV and uses them as inputs to the system. The system outputs a vector of control inputs that are solved using a cost function from point A to point B. The algorithm is fed the new states of the UAV and the algorithm is solved once again. The loop continues until the UAV has successfully reached the desired reference point.The research methodology is built by system modeling, controller design, testing, simulations, and experimentations. Performance evaluation is conducted through data analysis, and data visualization. The results unequivocally demonstrate the superior performance of the PAMPC, outperforming the HPID controller in the context of autonomous UAVs landing on mobile landing platforms on complex terrain.This study demonstrates the use of HPID and PAMPC controllers for multiple UAVs and UGVs. The system's design ensures robustness and scalability in simulations and experimentations.
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