Show simple item record

dc.contributor.advisorThanga, Jekan
dc.contributor.authorNallapu, Ravi teja
dc.creatorNallapu, Ravi teja
dc.date.accessioned2021-02-16T21:16:14Z
dc.date.available2021-02-16T21:16:14Z
dc.date.issued2020
dc.identifier.citationNallapu, Ravi teja. (2020). Automated Swarm Design Architectures for Reconnaissance of Small Bodies (Doctoral dissertation, University of Arizona, Tucson, USA).
dc.identifier.urihttp://hdl.handle.net/10150/656752
dc.description.abstractSmall body surface exploration has potential benefits to planetary science, space security, and economy. Exploring small-bodies is a challenge due to the low-gravity fields and uncertainty in the gravitational environment. Attempting surface missions with inadequate gravity field information is prone to high-risk of failure. Additionally, gravity environments around these bodies constrain the radius of orbiter missions for reconnaissance. This leaves spacecraft flybys as an alternate strategy for reconnaissance. Flybys may be the only viable approach when it becomes difficult to orbit or land on a small body. The challenge with flybys is that they are time-limited, thus providing only a limited glimpse of the target. These disadvantages can be overcome using a swarm approach where a complex task is delegated to multiple-low cost agents to achieve synergistic performance. While swarms are an important tool for small body exploration, designing their mission concepts is a complex task, and importantly there is no end-to-end tool to design swarm mission concepts. This thesis develops an automated software solution to design small body reconnaissance missions using spacecraft swarm flybys. The developed software is called the Integrated Design Engineering and Automation of Swarms (IDEAS). IDEAS automates the spacecraft, swarm, and trajectory design processes in a swarm mission. The key focus of this thesis is on the development of the Automated Swarm Designer module of IDEAS, which designs minimum sized swarm missions for space-limited and time-limited reconnaissance missions using Evolutionary Algorithms. In this thesis, the mission concept design process inside the IDEAS framework is demonstrated through multiple case studies of small body reconnaissance missions. First, the design of global surface mapping mission concepts to planetary moons using hyperbolic flybys of the central planet is presented using the IDEAS framework. The design process is then demonstrated using a global surface mapping mission concept to the Martian moon Phobos. The second case study explores the design of co-orbiting missions where the spacecraft enter into resonant co-orbits around the central planet for moon exploration. The design of these co-orbits to space and time-limited visual reconnaissance missions are then explored using the IDEAS framework. Specifically, the design of global surface mapping and region of interest observation mission concepts are presented using the Martian moon Deimos as the case study target. These principles are then extended to design reconnaissance missions to tumbling asteroids using mother-daughter swarm architectures. The design of such a swarms mission, using IDEAS, is demonstrated by designing a global surface mapping mission to the asteroid 4179 Toutatis. These case studies indicate that IDEAS is capable of generating small body reconnaissance missions concepts that overcome the spatial and temporal coverage limitations of flybys. Additionally, the sensitivity of the spatial and temporal coverages of the designed missions to perturbations such as spacecraft outages, uncertainty in encounter locations, and errors due to dynamical modeling is examined. These analyses describe the feasibility of the mission concepts identified by IDEAS. Finally, a testbed called as the Multi-Agent Photogrammetry of Small bodies (MAPS) is developed, which serves as a platform to demonstrate multi-spacecraft reconnaissance algorithms using autonomous UAVs. The UAV mapping mission design is formulated as a global surface mapping problem to generate a near-complete map of the target small body using a minimum number of UAVs. A near-optimal design with two UAVs is identified by IDEAS and is implemented inside the MAPS testbed. The UAV recordings are then processed inside a structure from motion (SfM) pipeline to generate a 3D reconstruction of the target. The reconstruction results demonstrate the successful performance of the mission concepts identified by IDEAS. In this way, the current work develops a software tool that automates the swarm mission concept generation and provides a hardware platform that can be used to demonstrate swarm mission concepts. Such tools will augment human mission designers in developing optimal, safe, and reliable reconnaissance mission concepts, thereby providing new pathways to explore the solar system.
dc.language.isoen
dc.publisherThe University of Arizona.
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectAstrodynamics
dc.subjectEvolutionary Algorithms
dc.subjectMission Concept Design
dc.subjectSmall Body Exploration
dc.subjectSpace Robotics
dc.subjectSpacecraft Swarms
dc.titleAutomated Swarm Design Architectures for Reconnaissance of Small Bodies
dc.typetext
dc.typeElectronic Dissertation
thesis.degree.grantorUniversity of Arizona
thesis.degree.leveldoctoral
dc.contributor.committeememberAsphaug, Erik
dc.contributor.committeememberButcher, Eric
dc.contributor.committeememberReddy, Vishnu
dc.contributor.committeememberFurfaro, Roberto
thesis.degree.disciplineGraduate College
thesis.degree.disciplineAerospace Engineering
thesis.degree.namePh.D.
refterms.dateFOA2021-02-16T21:16:14Z


Files in this item

Thumbnail
Name:
azu_etd_18464_sip1_m.pdf
Size:
31.08Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record