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Robust Multi-agent Mission Operations Management For Heterogeneous Aerial And Space-based Robotics

Mian, Sami T. (2021) Robust Multi-agent Mission Operations Management For Heterogeneous Aerial And Space-based Robotics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Unmanned Aerial Vehicles (UAVs) have become cheaper and more technologically advanced over the past five years, with uses in academia, industry, and government projects. A promising application of UAV technology is multi-agent swarms: using multiple drones to accomplish a group of tasks cooperatively. Currently, drone swarms have been used to aid search and rescue efforts, increase security systems, and produce awe-inspiring art installations. They are even used by groups like NASA to simulate advanced distributed systems, such as satellite constellations. However, as the hardware and sensing capabilities of UAVs increases, so too does the complexity of managing these swarms. Most swarm deployment systems are homogeneous: platforms are identical and assigned one overarching task. There is no allowance for specializations in platform capabilities. This undercuts the benefits of distributed computing: it is operationally restrictive to use custom, specialized UAVs in a large swarm, as the platform management is problematic and impractical. This leads to heavy implementation restrictions for novel sensors in swarms, due to high costs of integration and deployment.
The research of this dissertation creates a fleet mission management system that allows for multiple UAVs to cooperate and accomplish a multitude of mission types. The system employs new control law methods and flight software standards to coordinate the autonomous flight of drones in restrictive environments, while also optimizing for scarce resources like power, communication capabilities, and payload specialties. Furthermore, this research creates a system that allows for the inclusion and use of diverse, unique platforms and sensor payloads without considerable system modifications. The fleet management system is built on top of NASA’s cFS architecture and includes features from open-source software. A novel optimal control technique, called heterogeneous decentralized receding horizon control is developed and tuning using a UAV simulator. Lastly, exploratory research has been conducted on integrating dynamic vision sensors with UAV flight controllers, to test the integration of novel sensors with this fleet management system. The resulting system is readily deployable and can allow groups like NASA to mimic dynamic, diverse UAV swarms with relative ease.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Mian, Sami T.
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMao, Zhi-Hongzhm4@pitt.eduzhm40000-0002-3025-463X
Committee MemberDallal, AhmedAHD12@pitt.eduAHD120000-0003-0573-2326
Committee MemberGeorge, AlanAlan.George@pitt.eduAlan.George0000-0001-9665-2879
Committee MemberGrainger, Brandonbmg10@pitt.edubmg100000-0001-5251-8766
Committee MemberMartin, JamesJRMARTIN@pitt.eduJRMARTIN
Date: 26 January 2021
Date Type: Publication
Defense Date: 9 September 2020
Approval Date: 26 January 2021
Submission Date: 20 September 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 166
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: UAVs, fleet management, optimal control, dynamic vision, reinforcement learning
Date Deposited: 26 Jan 2021 21:10
Last Modified: 26 Jan 2022 06:15


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