{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/44236"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/44236","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automated planetary satellite tour planning with a novel low-thrust direct transcription method","abstract":"Upon completion of the heliocentric leg of its mission, it is almost always the case that an interplanetary spacecraft has scientific objectives to accomplish. This is especially true if the spacecraft performs a planetary moon tour. Over the past decade, mission designers have started to consider the use of low-thrust electric propulsion systems on board the spacecraft to enable future tours. The optimal trajectory for a given mission profile using low-thrust may be highly non-intuitive. There are thus many challenging aspects to the design of multiple flyby, low-thrust trajectories. One of the most significant, from the point of view of a numerical optimizer, can be the characteristic time scale of the dynamical system. Trajectories in a setting with a short characteristic time scale (e.g. those occurring in the Jovian system) are more challenging to optimize than those with a longer time scale (e.g. heliocentric trajectories to the outer solar system) because the spacecraft must often perform many revolutions about the central body as well as several flyby maneuvers. In this work, a novel way of parametrizing a low-thrust trajectory is explored and results using this method are presented. In addition to this, methods are outlined to increase the speed of execution and robustness of a numerical optimizer employing the Sims-Flanagan transcription method. To illustrate the difficulty of low-thrust trajectory optimization in a dynamical system characterized by a short time scale, and to provide an example of a tool that would benefit from the previously mentioned improvements, an existing medium-fidelity interplanetary trajectory optimizer, the Evolutionary Mission Trajectory Generator (EMTG), is modified and used to revisit the preliminary design phase of the Jupiter Icy Moons Orbiter (JIMO) reference trajectory. The results of this analysis are presented and compared with the high-fidelity version of the JIMO reference trajectory generated using the software package Mystic.","abstract_html":"Upon completion of the heliocentric leg of its mission, it is almost always the case that an interplanetary spacecraft has scientific objectives to accomplish. This is especially true if the spacecraft performs a planetary moon tour. Over the past decade, mission designers have started to consider the use of low-thrust electric propulsion systems on board the spacecraft to enable future tours. The optimal trajectory for a given mission profile using low-thrust may be highly non-intuitive. There are thus many challenging aspects to the design of multiple flyby, low-thrust trajectories. One of the most significant, from the point of view of a numerical optimizer, can be the characteristic time scale of the dynamical system. Trajectories in a setting with a short characteristic time scale (e.g. those occurring in the Jovian system) are more challenging to optimize than those with a longer time scale (e.g. heliocentric trajectories to the outer solar system) because the spacecraft must often perform many revolutions about the central body as well as several flyby maneuvers. In this work, a novel way of parametrizing a low-thrust trajectory is explored and results using this method are presented. In addition to this, methods are outlined to increase the speed of execution and robustness of a numerical optimizer employing the Sims-Flanagan transcription method. To illustrate the difficulty of low-thrust trajectory optimization in a dynamical system characterized by a short time scale, and to provide an example of a tool that would benefit from the previously mentioned improvements, an existing medium-fidelity interplanetary trajectory optimizer, the Evolutionary Mission Trajectory Generator (EMTG), is modified and used to revisit the preliminary design phase of the Jupiter Icy Moons Orbiter (JIMO) reference trajectory. The results of this analysis are presented and compared with the high-fidelity version of the JIMO reference trajectory generated using the software package Mystic.","abstract_has_math":false,"creators":["Ellison, Donald"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Conway, Bruce A."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-24T21:55:09Z","date_published":"2013-05-24T21:55:09Z","updated_at":"2026-07-22T22:25:33Z","subjects":["Sims-Flanagan","Low-Thrust","Trajectory","Optimization","Spacecraft","Orbit","Genetic Algorithm","Sparse Nonlinear OPTimizer (SNOPT)","Automatic Differentiation","Jacobian","Nonlinear Program","Jupiter","Monotonic Basin Hopping","Mission Analysis Low-Thrust Trajectory Optimization (MALTO)"],"languages":["en"],"rights":["Copyright 2013 Donald Ellison"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/44236","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Conway, Bruce A."]},{"key":"dc:creator","label":"Author","values":["Ellison, Donald"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-05-24T21:55:09Z","2013-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sims-Flanagan","Low-Thrust","Trajectory","Optimization","Spacecraft","Orbit","Genetic Algorithm","Sparse Nonlinear OPTimizer (SNOPT)","Automatic Differentiation","Jacobian","Nonlinear Program","Jupiter","Monotonic Basin Hopping","Mission Analysis Low-Thrust Trajectory Optimization (MALTO)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Donald Ellison"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/44236"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Upon completion of the heliocentric leg of its mission, it is almost always the case that an interplanetary spacecraft has scientific objectives to accomplish. This is especially true if the spacecraft performs a planetary moon tour. Over the past decade, mission designers have started to consider the use of low-thrust electric propulsion systems on board the spacecraft to enable future tours. The optimal trajectory for a given mission profile using low-thrust may be highly non-intuitive. There are thus many challenging aspects to the design of multiple flyby, low-thrust trajectories. One of the most significant, from the point of view of a numerical optimizer, can be the characteristic time scale of the dynamical system. Trajectories in a setting with a short characteristic time scale (e.g. those occurring in the Jovian system) are more challenging to optimize than those with a longer time scale (e.g. heliocentric trajectories to the outer solar system) because the spacecraft must often perform many revolutions about the central body as well as several flyby maneuvers. In this work, a novel way of parametrizing a low-thrust trajectory is explored and results using this method are presented. In addition to this, methods are outlined to increase the speed of execution and robustness of a numerical optimizer employing the Sims-Flanagan transcription method. To illustrate the difficulty of low-thrust trajectory optimization in a dynamical system characterized by a short time scale, and to provide an example of a tool that would benefit from the previously mentioned improvements, an existing medium-fidelity interplanetary trajectory optimizer, the Evolutionary Mission Trajectory Generator (EMTG), is modified and used to revisit the preliminary design phase of the Jupiter Icy Moons Orbiter (JIMO) reference trajectory. The results of this analysis are presented and compared with the high-fidelity version of the JIMO reference trajectory generated using the software package Mystic.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-26T14:02:00Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 37 UVC_SF_Jacobian_sparsity.png: 24876 bytes, checksum: 681451c7b2ddfdaa340195cc17a99f5f (MD5) sims_flanagan_cartoon.png: 94309 bytes, checksum: 71f0eeac756cb7b951fcf826774a9a6e (MD5) SF_Jacobian_sparsity.png: 28794 bytes, checksum: 1860b88e7584f32c49c596d90b41c968 (MD5) reactor.png: 252814 bytes, checksum: 157b7bb7612ceb4948dd3e1c0e241525 (MD5) PB1.png: 107574 bytes, checksum: c810e49bc8a5ff12dbde26999ba7ec26 (MD5) MP_SF_Jacobian_sparsity.png: 26048 bytes, checksum: dd00b5ab32ea7e7054870448f05d19d3 (MD5) MP_Callisto_rendezvous_derivatives_closeup.png: 31863 bytes, checksum: 53a0f5287b77773e367b91dce2f05817 (MD5) MP_Callisto_rendezvous_derivatives.png: 27198 bytes, checksum: 3e23e091064fe85ea9e3886abc46b188 (MD5) MP_Callisto_rendezvous_2phase_closeup.png: 30264 bytes, checksum: af2413e6ffd76d448b5f493869ba70d9 (MD5) MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) MBH.png: 56566 bytes, checksum: 07d61f9c9cff131964b378dda245f7e5 (MD5) JIMO.png: 508610 bytes, checksum: f51f64f3c2160aef2b7d3f71a98be4c5 (MD5) GGEE.png: 54637 bytes, checksum: 65d09702c9b16efe6432850c532b9c92 (MD5) GE_reference.png: 121418 bytes, checksum: a784cb91ae7f79fe2516e2cf6e265aad (MD5) flyby_vectors.png: 16510 bytes, checksum: 2a5842f2c6a289073a1ccb97867828b5 (MD5) flyby_geometry.png: 26359 bytes, checksum: 4dfc8c604675b6f0facda1fbb45dd5b4 (MD5) Europa_rotating_reference.png: 100844 bytes, checksum: 3f2a68a6ff600f60925089905457b6b0 (MD5) entry_point.png: 19398 bytes, checksum: e561d8ffd313e396fbf639e096c5f550 (MD5) EC_reference.png: 117576 bytes, checksum: 79e144cff608b99308a51edd6ad898cf (MD5) CG_reference.png: 129275 bytes, checksum: 547d6b8b25960cf67eec2e241a24780f (MD5) CCGG.png: 54354 bytes, checksum: d75424623052fcd8deeb66ea5951b737 (MD5) CCCC.png: 35098 bytes, checksum: e7e852eb891dd028c25beac0ec9a2ffd (MD5) Callisto_capture_reference.png: 60774 bytes, checksum: 3d21222c6006acacba8b1617d3ba65dc (MD5) masters_thesis.tex: 5755 bytes, checksum: 66ba0fcb9479f56f70a6020107d9cbc5 (MD5) AAS_publication.bst: 17438 bytes, checksum: b0110560a858beeea248ed6fbdc49cd1 (MD5) chapter6_future_work.tex: 13324 bytes, checksum: 944c332b2fa5836a68cd33e8e1d0f910 (MD5) chapter5_EMTG_robustness_improvements.tex: 28581 bytes, checksum: 4caadc16be08de5ed96c6650070ce453 (MD5) chapter4_JIMO_mission_simulation.tex: 32316 bytes, checksum: d0d103467ea746eb19a80b5604e583f9 (MD5) chapter3_EMTG.tex: 27639 bytes, checksum: cb8288c1a6593404bb0ba2c97b71a34f (MD5) chapter2_dynamical_system.tex: 11110 bytes, checksum: 26e09a1dad74e2b28fc45936e2d92876 (MD5) chapter1_introduction.tex: 8455 bytes, checksum: b751d38304ada206ec3215bae31ef1cf (MD5) apx.tex: 3775 bytes, checksum: cf2b0202c11ffba9ede1a91747393765 (MD5) ack.tex: 4429 bytes, checksum: ffae207346383ca2018ca803f5d36a42 (MD5) abs.tex: 2160 bytes, checksum: aedc7d445655c894b5e40e05f9d4184f (MD5) abbreviations.tex: 4744 bytes, checksum: 59ce1916d23ca9855484b401210651a7 (MD5) Ellison_Donald.pdf: 2061343 bytes, checksum: 4327b421a1af7d59acaf2e108cea3f2e (MD5)","Made available in DSpace on 2013-05-24T21:55:09Z (GMT). No. of bitstreams: 38 Donald_Ellison.pdf: 2061343 bytes, checksum: 4327b421a1af7d59acaf2e108cea3f2e (MD5) UVC_SF_Jacobian_sparsity.png: 24876 bytes, checksum: 681451c7b2ddfdaa340195cc17a99f5f (MD5) sims_flanagan_cartoon.png: 94309 bytes, checksum: 71f0eeac756cb7b951fcf826774a9a6e (MD5) SF_Jacobian_sparsity.png: 28794 bytes, checksum: 1860b88e7584f32c49c596d90b41c968 (MD5) reactor.png: 252814 bytes, checksum: 157b7bb7612ceb4948dd3e1c0e241525 (MD5) PB1.png: 107574 bytes, checksum: c810e49bc8a5ff12dbde26999ba7ec26 (MD5) MP_SF_Jacobian_sparsity.png: 26048 bytes, checksum: dd00b5ab32ea7e7054870448f05d19d3 (MD5) MP_Callisto_rendezvous_derivatives_closeup.png: 31863 bytes, checksum: 53a0f5287b77773e367b91dce2f05817 (MD5) MP_Callisto_rendezvous_derivatives.png: 27198 bytes, checksum: 3e23e091064fe85ea9e3886abc46b188 (MD5) MP_Callisto_rendezvous_2phase_closeup.png: 30264 bytes, checksum: af2413e6ffd76d448b5f493869ba70d9 (MD5) MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) 1_MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) MBH.png: 56566 bytes, checksum: 07d61f9c9cff131964b378dda245f7e5 (MD5) JIMO.png: 508610 bytes, checksum: f51f64f3c2160aef2b7d3f71a98be4c5 (MD5) GGEE.png: 54637 bytes, checksum: 65d09702c9b16efe6432850c532b9c92 (MD5) GE_reference.png: 121418 bytes, checksum: a784cb91ae7f79fe2516e2cf6e265aad (MD5) flyby_vectors.png: 16510 bytes, checksum: 2a5842f2c6a289073a1ccb97867828b5 (MD5) flyby_geometry.png: 26359 bytes, checksum: 4dfc8c604675b6f0facda1fbb45dd5b4 (MD5) Europa_rotating_reference.png: 100844 bytes, checksum: 3f2a68a6ff600f60925089905457b6b0 (MD5) entry_point.png: 19398 bytes, checksum: e561d8ffd313e396fbf639e096c5f550 (MD5) EC_reference.png: 117576 bytes, checksum: 79e144cff608b99308a51edd6ad898cf (MD5) CG_reference.png: 129275 bytes, checksum: 547d6b8b25960cf67eec2e241a24780f (MD5) CCGG.png: 54354 bytes, checksum: d75424623052fcd8deeb66ea5951b737 (MD5) CCCC.png: 35098 bytes, checksum: e7e852eb891dd028c25beac0ec9a2ffd (MD5) Callisto_capture_reference.png: 60774 bytes, checksum: 3d21222c6006acacba8b1617d3ba65dc (MD5) masters_thesis.tex: 5755 bytes, checksum: 66ba0fcb9479f56f70a6020107d9cbc5 (MD5) AAS_publication.bst: 17438 bytes, checksum: b0110560a858beeea248ed6fbdc49cd1 (MD5) chapter6_future_work.tex: 13324 bytes, checksum: 944c332b2fa5836a68cd33e8e1d0f910 (MD5) chapter5_EMTG_robustness_improvements.tex: 28581 bytes, checksum: 4caadc16be08de5ed96c6650070ce453 (MD5) chapter4_JIMO_mission_simulation.tex: 32316 bytes, checksum: d0d103467ea746eb19a80b5604e583f9 (MD5) chapter3_EMTG.tex: 27639 bytes, checksum: cb8288c1a6593404bb0ba2c97b71a34f (MD5) chapter2_dynamical_system.tex: 11110 bytes, checksum: 26e09a1dad74e2b28fc45936e2d92876 (MD5) chapter1_introduction.tex: 8455 bytes, checksum: b751d38304ada206ec3215bae31ef1cf (MD5) apx.tex: 3775 bytes, checksum: cf2b0202c11ffba9ede1a91747393765 (MD5) ack.tex: 4429 bytes, checksum: ffae207346383ca2018ca803f5d36a42 (MD5) abs.tex: 2160 bytes, checksum: aedc7d445655c894b5e40e05f9d4184f (MD5) abbreviations.tex: 4744 bytes, checksum: 59ce1916d23ca9855484b401210651a7 (MD5) license.txt: 4064 bytes, checksum: 65df42d025394e888c95b3c0ddc59173 (MD5)"]},{"key":"dc:title","label":"Title","values":["Automated planetary satellite tour planning with a novel low-thrust direct transcription method"]}]}],"canonical_facts":{"dc:contributor":["Conway, Bruce A."],"dc:creator":["Ellison, Donald"],"dc:date":["2013-05-24T21:55:09Z","2013-05"],"dc:description":["Upon completion of the heliocentric leg of its mission, it is almost always the case that an interplanetary spacecraft has scientific objectives to accomplish. This is especially true if the spacecraft performs a planetary moon tour. Over the past decade, mission designers have started to consider the use of low-thrust electric propulsion systems on board the spacecraft to enable future tours. The optimal trajectory for a given mission profile using low-thrust may be highly non-intuitive. There are thus many challenging aspects to the design of multiple flyby, low-thrust trajectories. One of the most significant, from the point of view of a numerical optimizer, can be the characteristic time scale of the dynamical system. Trajectories in a setting with a short characteristic time scale (e.g. those occurring in the Jovian system) are more challenging to optimize than those with a longer time scale (e.g. heliocentric trajectories to the outer solar system) because the spacecraft must often perform many revolutions about the central body as well as several flyby maneuvers. In this work, a novel way of parametrizing a low-thrust trajectory is explored and results using this method are presented. In addition to this, methods are outlined to increase the speed of execution and robustness of a numerical optimizer employing the Sims-Flanagan transcription method. To illustrate the difficulty of low-thrust trajectory optimization in a dynamical system characterized by a short time scale, and to provide an example of a tool that would benefit from the previously mentioned improvements, an existing medium-fidelity interplanetary trajectory optimizer, the Evolutionary Mission Trajectory Generator (EMTG), is modified and used to revisit the preliminary design phase of the Jupiter Icy Moons Orbiter (JIMO) reference trajectory. The results of this analysis are presented and compared with the high-fidelity version of the JIMO reference trajectory generated using the software package Mystic.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-26T14:02:00Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 37 UVC_SF_Jacobian_sparsity.png: 24876 bytes, checksum: 681451c7b2ddfdaa340195cc17a99f5f (MD5) sims_flanagan_cartoon.png: 94309 bytes, checksum: 71f0eeac756cb7b951fcf826774a9a6e (MD5) SF_Jacobian_sparsity.png: 28794 bytes, checksum: 1860b88e7584f32c49c596d90b41c968 (MD5) reactor.png: 252814 bytes, checksum: 157b7bb7612ceb4948dd3e1c0e241525 (MD5) PB1.png: 107574 bytes, checksum: c810e49bc8a5ff12dbde26999ba7ec26 (MD5) MP_SF_Jacobian_sparsity.png: 26048 bytes, checksum: dd00b5ab32ea7e7054870448f05d19d3 (MD5) MP_Callisto_rendezvous_derivatives_closeup.png: 31863 bytes, checksum: 53a0f5287b77773e367b91dce2f05817 (MD5) MP_Callisto_rendezvous_derivatives.png: 27198 bytes, checksum: 3e23e091064fe85ea9e3886abc46b188 (MD5) MP_Callisto_rendezvous_2phase_closeup.png: 30264 bytes, checksum: af2413e6ffd76d448b5f493869ba70d9 (MD5) MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) MBH.png: 56566 bytes, checksum: 07d61f9c9cff131964b378dda245f7e5 (MD5) JIMO.png: 508610 bytes, checksum: f51f64f3c2160aef2b7d3f71a98be4c5 (MD5) GGEE.png: 54637 bytes, checksum: 65d09702c9b16efe6432850c532b9c92 (MD5) GE_reference.png: 121418 bytes, checksum: a784cb91ae7f79fe2516e2cf6e265aad (MD5) flyby_vectors.png: 16510 bytes, checksum: 2a5842f2c6a289073a1ccb97867828b5 (MD5) flyby_geometry.png: 26359 bytes, checksum: 4dfc8c604675b6f0facda1fbb45dd5b4 (MD5) Europa_rotating_reference.png: 100844 bytes, checksum: 3f2a68a6ff600f60925089905457b6b0 (MD5) entry_point.png: 19398 bytes, checksum: e561d8ffd313e396fbf639e096c5f550 (MD5) EC_reference.png: 117576 bytes, checksum: 79e144cff608b99308a51edd6ad898cf (MD5) CG_reference.png: 129275 bytes, checksum: 547d6b8b25960cf67eec2e241a24780f (MD5) CCGG.png: 54354 bytes, checksum: d75424623052fcd8deeb66ea5951b737 (MD5) CCCC.png: 35098 bytes, checksum: e7e852eb891dd028c25beac0ec9a2ffd (MD5) Callisto_capture_reference.png: 60774 bytes, checksum: 3d21222c6006acacba8b1617d3ba65dc (MD5) masters_thesis.tex: 5755 bytes, checksum: 66ba0fcb9479f56f70a6020107d9cbc5 (MD5) AAS_publication.bst: 17438 bytes, checksum: b0110560a858beeea248ed6fbdc49cd1 (MD5) chapter6_future_work.tex: 13324 bytes, checksum: 944c332b2fa5836a68cd33e8e1d0f910 (MD5) chapter5_EMTG_robustness_improvements.tex: 28581 bytes, checksum: 4caadc16be08de5ed96c6650070ce453 (MD5) chapter4_JIMO_mission_simulation.tex: 32316 bytes, checksum: d0d103467ea746eb19a80b5604e583f9 (MD5) chapter3_EMTG.tex: 27639 bytes, checksum: cb8288c1a6593404bb0ba2c97b71a34f (MD5) chapter2_dynamical_system.tex: 11110 bytes, checksum: 26e09a1dad74e2b28fc45936e2d92876 (MD5) chapter1_introduction.tex: 8455 bytes, checksum: b751d38304ada206ec3215bae31ef1cf (MD5) apx.tex: 3775 bytes, checksum: cf2b0202c11ffba9ede1a91747393765 (MD5) ack.tex: 4429 bytes, checksum: ffae207346383ca2018ca803f5d36a42 (MD5) abs.tex: 2160 bytes, checksum: aedc7d445655c894b5e40e05f9d4184f (MD5) abbreviations.tex: 4744 bytes, checksum: 59ce1916d23ca9855484b401210651a7 (MD5) Ellison_Donald.pdf: 2061343 bytes, checksum: 4327b421a1af7d59acaf2e108cea3f2e (MD5)","Made available in DSpace on 2013-05-24T21:55:09Z (GMT). No. of bitstreams: 38 Donald_Ellison.pdf: 2061343 bytes, checksum: 4327b421a1af7d59acaf2e108cea3f2e (MD5) UVC_SF_Jacobian_sparsity.png: 24876 bytes, checksum: 681451c7b2ddfdaa340195cc17a99f5f (MD5) sims_flanagan_cartoon.png: 94309 bytes, checksum: 71f0eeac756cb7b951fcf826774a9a6e (MD5) SF_Jacobian_sparsity.png: 28794 bytes, checksum: 1860b88e7584f32c49c596d90b41c968 (MD5) reactor.png: 252814 bytes, checksum: 157b7bb7612ceb4948dd3e1c0e241525 (MD5) PB1.png: 107574 bytes, checksum: c810e49bc8a5ff12dbde26999ba7ec26 (MD5) MP_SF_Jacobian_sparsity.png: 26048 bytes, checksum: dd00b5ab32ea7e7054870448f05d19d3 (MD5) MP_Callisto_rendezvous_derivatives_closeup.png: 31863 bytes, checksum: 53a0f5287b77773e367b91dce2f05817 (MD5) MP_Callisto_rendezvous_derivatives.png: 27198 bytes, checksum: 3e23e091064fe85ea9e3886abc46b188 (MD5) MP_Callisto_rendezvous_2phase_closeup.png: 30264 bytes, checksum: af2413e6ffd76d448b5f493869ba70d9 (MD5) MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) 1_MP_Callisto_rendezvous_2phase.png: 35113 bytes, checksum: b077ab5fe4e296f8f717c195f9c79b70 (MD5) MBH.png: 56566 bytes, checksum: 07d61f9c9cff131964b378dda245f7e5 (MD5) JIMO.png: 508610 bytes, checksum: f51f64f3c2160aef2b7d3f71a98be4c5 (MD5) GGEE.png: 54637 bytes, checksum: 65d09702c9b16efe6432850c532b9c92 (MD5) GE_reference.png: 121418 bytes, checksum: a784cb91ae7f79fe2516e2cf6e265aad (MD5) flyby_vectors.png: 16510 bytes, checksum: 2a5842f2c6a289073a1ccb97867828b5 (MD5) flyby_geometry.png: 26359 bytes, checksum: 4dfc8c604675b6f0facda1fbb45dd5b4 (MD5) Europa_rotating_reference.png: 100844 bytes, checksum: 3f2a68a6ff600f60925089905457b6b0 (MD5) entry_point.png: 19398 bytes, checksum: e561d8ffd313e396fbf639e096c5f550 (MD5) EC_reference.png: 117576 bytes, checksum: 79e144cff608b99308a51edd6ad898cf (MD5) CG_reference.png: 129275 bytes, checksum: 547d6b8b25960cf67eec2e241a24780f (MD5) CCGG.png: 54354 bytes, checksum: d75424623052fcd8deeb66ea5951b737 (MD5) CCCC.png: 35098 bytes, checksum: e7e852eb891dd028c25beac0ec9a2ffd (MD5) Callisto_capture_reference.png: 60774 bytes, checksum: 3d21222c6006acacba8b1617d3ba65dc (MD5) masters_thesis.tex: 5755 bytes, checksum: 66ba0fcb9479f56f70a6020107d9cbc5 (MD5) AAS_publication.bst: 17438 bytes, checksum: b0110560a858beeea248ed6fbdc49cd1 (MD5) chapter6_future_work.tex: 13324 bytes, checksum: 944c332b2fa5836a68cd33e8e1d0f910 (MD5) chapter5_EMTG_robustness_improvements.tex: 28581 bytes, checksum: 4caadc16be08de5ed96c6650070ce453 (MD5) chapter4_JIMO_mission_simulation.tex: 32316 bytes, checksum: d0d103467ea746eb19a80b5604e583f9 (MD5) chapter3_EMTG.tex: 27639 bytes, checksum: cb8288c1a6593404bb0ba2c97b71a34f (MD5) chapter2_dynamical_system.tex: 11110 bytes, checksum: 26e09a1dad74e2b28fc45936e2d92876 (MD5) chapter1_introduction.tex: 8455 bytes, checksum: b751d38304ada206ec3215bae31ef1cf (MD5) apx.tex: 3775 bytes, checksum: cf2b0202c11ffba9ede1a91747393765 (MD5) ack.tex: 4429 bytes, checksum: ffae207346383ca2018ca803f5d36a42 (MD5) abs.tex: 2160 bytes, checksum: aedc7d445655c894b5e40e05f9d4184f (MD5) abbreviations.tex: 4744 bytes, checksum: 59ce1916d23ca9855484b401210651a7 (MD5) license.txt: 4064 bytes, checksum: 65df42d025394e888c95b3c0ddc59173 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/44236"],"dc:language":["en"],"dc:rights":["Copyright 2013 Donald Ellison"],"dc:subject":["Sims-Flanagan","Low-Thrust","Trajectory","Optimization","Spacecraft","Orbit","Genetic Algorithm","Sparse Nonlinear OPTimizer (SNOPT)","Automatic Differentiation","Jacobian","Nonlinear Program","Jupiter","Monotonic Basin Hopping","Mission Analysis Low-Thrust Trajectory Optimization (MALTO)"],"dc:title":["Automated planetary satellite tour planning with a novel low-thrust direct transcription method"],"dc:type":["text"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:33Z"}