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Unmanned Aerial Systems: Autonomy, Cognition and Control

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Forthcoming

Standard

Unmanned Aerial Systems : Autonomy, Cognition and Control. / Montazeri, Allahyar; Can, Aydin; Imran, Imil.

Unmanned Aerial Systems: Theoretical Foundation and Applications. Elsevier, 2020.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Harvard

APA

Montazeri, A., Can, A., & Imran, I. (Accepted/In press). Unmanned Aerial Systems: Autonomy, Cognition and Control. In Unmanned Aerial Systems: Theoretical Foundation and Applications Elsevier. https://www.elsevier.com/books/unmanned-aerial-systems/koubaa/978-0-12-820276-0

Vancouver

Montazeri A, Can A, Imran I. Unmanned Aerial Systems: Autonomy, Cognition and Control. In Unmanned Aerial Systems: Theoretical Foundation and Applications. Elsevier. 2020

Author

Montazeri, Allahyar ; Can, Aydin ; Imran, Imil. / Unmanned Aerial Systems : Autonomy, Cognition and Control. Unmanned Aerial Systems: Theoretical Foundation and Applications. Elsevier, 2020.

Bibtex

@inbook{77c97843df3347f1b8e664b37bfcb06c,
title = "Unmanned Aerial Systems: Autonomy, Cognition and Control",
abstract = "Increasing trend towards higher level of autonomy in unmanned aerial systems(UASs) requires less control by the human operator and increasing capabilityto perform complex tasks by reacting to the environmental influences. Nevertheless, current UASs, are designed to function in static, and predictableenvironments. Therefore, it is envisaged that the existing uncertainties and dynamic changes, caused when an unmanned aerial vehicle (UAV) is operatingin an unknown environment, would degrade its performance signicantly. Theuncertainties can be also incurred through interaction with other complex andintelligent systems, such as humans. We present a compact literature survey ofUASs control and navigation as a basic knowledge to develop UASs from theperspective of control engineer. Besides, we present several control strategies tomaintain a UAS, as well as multi-UASs under a network setting under variousscenarios. Several simulations are given to illustrate the performance of the controllers in MATLAB. Advances in computing power and algorithms currentlyenable development of systems with high degree of autonomy. Nonetheless, there is a large gap between practical operation in a real-world and laboratory implementation, as safe deployment of UASs, requires validation of their behaviour under almost all envisaged scenarios. A reliable and autonomous operation of such a system requires design and development of a cognitive control system that acquires knowledge and understanding of the surrounding environment via perception, reasoning and learning. Cognitive control systems in UASs will enhance their safety and performance. Cognitive control can also be used in cooperative execution of complex tasks where multiple agents such as humans, machines or both interact. Such UASs will have a great potential to be used in extreme environments such as search and rescue in case of disaster, nuclear decommissioning operation, deep-sea exploration, mining, etc.",
author = "Allahyar Montazeri and Aydin Can and Imil Imran",
year = "2020",
month = may,
day = "18",
language = "English",
booktitle = "Unmanned Aerial Systems",
publisher = "Elsevier",

}

RIS

TY - CHAP

T1 - Unmanned Aerial Systems

T2 - Autonomy, Cognition and Control

AU - Montazeri, Allahyar

AU - Can, Aydin

AU - Imran, Imil

PY - 2020/5/18

Y1 - 2020/5/18

N2 - Increasing trend towards higher level of autonomy in unmanned aerial systems(UASs) requires less control by the human operator and increasing capabilityto perform complex tasks by reacting to the environmental influences. Nevertheless, current UASs, are designed to function in static, and predictableenvironments. Therefore, it is envisaged that the existing uncertainties and dynamic changes, caused when an unmanned aerial vehicle (UAV) is operatingin an unknown environment, would degrade its performance signicantly. Theuncertainties can be also incurred through interaction with other complex andintelligent systems, such as humans. We present a compact literature survey ofUASs control and navigation as a basic knowledge to develop UASs from theperspective of control engineer. Besides, we present several control strategies tomaintain a UAS, as well as multi-UASs under a network setting under variousscenarios. Several simulations are given to illustrate the performance of the controllers in MATLAB. Advances in computing power and algorithms currentlyenable development of systems with high degree of autonomy. Nonetheless, there is a large gap between practical operation in a real-world and laboratory implementation, as safe deployment of UASs, requires validation of their behaviour under almost all envisaged scenarios. A reliable and autonomous operation of such a system requires design and development of a cognitive control system that acquires knowledge and understanding of the surrounding environment via perception, reasoning and learning. Cognitive control systems in UASs will enhance their safety and performance. Cognitive control can also be used in cooperative execution of complex tasks where multiple agents such as humans, machines or both interact. Such UASs will have a great potential to be used in extreme environments such as search and rescue in case of disaster, nuclear decommissioning operation, deep-sea exploration, mining, etc.

AB - Increasing trend towards higher level of autonomy in unmanned aerial systems(UASs) requires less control by the human operator and increasing capabilityto perform complex tasks by reacting to the environmental influences. Nevertheless, current UASs, are designed to function in static, and predictableenvironments. Therefore, it is envisaged that the existing uncertainties and dynamic changes, caused when an unmanned aerial vehicle (UAV) is operatingin an unknown environment, would degrade its performance signicantly. Theuncertainties can be also incurred through interaction with other complex andintelligent systems, such as humans. We present a compact literature survey ofUASs control and navigation as a basic knowledge to develop UASs from theperspective of control engineer. Besides, we present several control strategies tomaintain a UAS, as well as multi-UASs under a network setting under variousscenarios. Several simulations are given to illustrate the performance of the controllers in MATLAB. Advances in computing power and algorithms currentlyenable development of systems with high degree of autonomy. Nonetheless, there is a large gap between practical operation in a real-world and laboratory implementation, as safe deployment of UASs, requires validation of their behaviour under almost all envisaged scenarios. A reliable and autonomous operation of such a system requires design and development of a cognitive control system that acquires knowledge and understanding of the surrounding environment via perception, reasoning and learning. Cognitive control systems in UASs will enhance their safety and performance. Cognitive control can also be used in cooperative execution of complex tasks where multiple agents such as humans, machines or both interact. Such UASs will have a great potential to be used in extreme environments such as search and rescue in case of disaster, nuclear decommissioning operation, deep-sea exploration, mining, etc.

M3 - Chapter (peer-reviewed)

BT - Unmanned Aerial Systems

PB - Elsevier

ER -