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dc.contributor.advisorBannova, Olga
dc.creatorBisharat, Tara Marwan Iskandar
dc.date.accessioned2020-06-04T00:22:18Z
dc.date.createdMay 2020
dc.date.issued2020-05
dc.date.submittedMay 2020
dc.identifier.urihttps://hdl.handle.net/10657/6671
dc.description.abstractFuture human exploration missions to Mars are partially Earth-dependent. The inherent risk and complexity of these missions warrant precursor robotic systems to prepare infrastructure critical to the survival of humans before their arrival. At a minimum, they are required to autonomously deploy, test, and verify systems for essential commodities and life-support (such as power sources, ISRU plants, and thermal control), set up a habitat, and even distribute connections. Once humans arrive, robotic systems are still required to assist humans during their stay and later maintain operations after their departure. The undertaking of robotically setting up an outpost requires numerous and consecutive missions that build on top of one another. Therefore, precursor robotic systems need to be versatile, robust, modular, scalable, upgradeable, affordable to engineer and produce, autonomous, and intelligent, all the while resilient and adaptive to extreme environments and the high risk of such mission. However, humanity has built its experience in planetary exploration on standalone unmanned scientific missions. These missions have predefined objectives so far accomplished using rovers operating onboard scientific instruments while traversing harsh terrain. Current robotic systems are highly specialized, non-modular, has no integration capabilities, are expensive, have limited resilience, are non-adaptive, and require constant supervision. Current planetary robotic systems cannot scale up to fulfill prerequisites of future precursor missions. In this paper, I explore how to transition from the current state of practice to next-generation robotic systems. I describe and assess the methodologies used to design current systems and demonstrate how a complete rethinking of these methodologies is fundamental to generating efficient and feasible systems. I culminate this research by proposing an alternative to the design process and demonstrate the new methodology by introducing MAERAL, a research by design concept of scalable, versatile, and intelligent swarm robots for future Mars surface operation missions. It combines concepts in modularity, standardization, machine learning, and collective intelligence to accomplish complex activities using simple basic actions. The vision of this research is to prolong and sustain our space-faring future by making space exploration more accessible, more innovative, more feasible, more efficient, more continuous, and more sustainable.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectRobotics, Space, Architecture. Modular, AI, Swarm
dc.titleMAERAL: Scalable, Versatile & Intelligent Swarm Robotic Concept for Mars Surface Operations
dc.date.updated2020-06-04T00:22:19Z
dc.type.genreThesis
thesis.degree.nameMaster of Science
thesis.degree.levelMasters
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.departmentMechanical Engineering, Department of
dc.contributor.committeeMemberBell, Larry
dc.contributor.committeeMemberKennedy, Kriss J.
dc.contributor.committeeMemberToups, Larry
dc.creator.orcid0000-0002-8702-5846
local.embargo.terms2022-05-01
local.embargo.lift2022-05-01
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
dc.type.dcmiText
dc.format.digitalOriginborn digital
dc.description.departmentMechanical Engineering, Department of
thesis.degree.collegeCullen College of Engineering


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