{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132699"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132699","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Debris collision avoidance maneuver optimization (CAMO) for satellite constellations","abstract":"The exponential growth of the orbital debris population in Near-Earth space poses a significant threat to the sustainability of current and future satellite constellations. Traditional collision avoidance strategies, which typically rely on single-impulse maneuvers executed in response to ground-based warnings, often suffer from high propellant costs and operational inefficiencies due to late detection and reaction times. This thesis proposes and validates an autonomous, multi-objective optimization framework for collision avoidance maneuvers (CAMs) tailored for Medium Earth Orbit (MEO) constellations, specifically the Global Positioning System (GPS). The core of this research is the development of a “Hybrid Three-Burn Maneuver” strategy that ensures a closed-loop trajectory, returning the satellite precisely to its nominal station-keeping slot after evading the threat. The optimization engine utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimize collision probability (Pc) and total velocity change (∆V ). A high-fidelity simulation environment was constructed in MATLAB, incorporating J2-perturbed dynamics for debris and Keplerian propagation for satellites to capture realistic relative motion and nodal drift. The Probability of Collisionis computed using a robust K-series expansion method, enabling computationally efficient and numerically stable risk assessment. The framework was tested against six high-risk conjunction scenarios identified within a simulated GPS constellation, including a critical head-on encounter with a 221-meter miss distance. A parametric study was conducted across three temporal regimes: Strategic (> 8 hours warning), Operational (∼ 3 hours), and Tactical (10 minutes). Key findings indicate: 1. The Cost of Delay: There is a severe nonlinear relationship between maneuver warning time and fuel consumption. Strategic maneuvers executed hours in advance require approximately 0.5 m/s of ∆V , whereas emergency tactical maneuvers require over 8.0 m/s—an 18-fold increase in fuel cost representing a power-law scaling with reduced warning time. 2. Quarter-Period Optimal Time Scale: When unconstrained, the optimization algorithm consistently converges to a maneuver duration of k ≈ T /4 (where T is the orbital period), representing the fundamental optimal time scale for closed-loop collision avoidance in circular orbits. For GPS satellites with T = 43,080 s, this yields k ≈ 10,800 s (3 hours). This convergence occurs because the three-burn return-to-station constraint can only be exactly satisfied when 2k = nT /2 (where n is a positive integer), with n = 1 providing the minimum-energy solution. Maneuver durations significantly below T /4 enter the hyperbolic scaling regime, while durations above T /4 yield no additional fuel savings. 3. Algorithm Robustness: The hybrid evolutionary algorithm successfully identified safe trajectories (Pc < 10−6) for all test cases, demonstrating its capability to handle diverse encounter geometries. 4. Operational Viability: The proposed autonomous system enables a “low-energy drift” avoidance mode that is functionally unavailable to reactive ground-based systems, potentially extending satellite operational lifetimes by preserving critical station-keeping propellant. This research provides a quantitative basis for the implementation of onboard autonomous conjunction assessment and maneuver planning, offering a pathway to significantly enhance the resilience and longevity of critical space infrastructure.","abstract_html":"The exponential growth of the orbital debris population in Near-Earth space poses a significant threat to the sustainability of current and future satellite constellations. Traditional collision avoidance strategies, which typically rely on single-impulse maneuvers executed in response to ground-based warnings, often suffer from high propellant costs and operational inefficiencies due to late detection and reaction times. This thesis proposes and validates an autonomous, multi-objective optimization framework for collision avoidance maneuvers (CAMs) tailored for Medium Earth Orbit (MEO) constellations, specifically the Global Positioning System (GPS). The core of this research is the development of a “Hybrid Three-Burn Maneuver” strategy that ensures a closed-loop trajectory, returning the satellite precisely to its nominal station-keeping slot after evading the threat. The optimization engine utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimize collision probability (Pc) and total velocity change (∆V ). A high-fidelity simulation environment was constructed in MATLAB, incorporating J2-perturbed dynamics for debris and Keplerian propagation for satellites to capture realistic relative motion and nodal drift. The Probability of Collisionis computed using a robust K-series expansion method, enabling computationally efficient and numerically stable risk assessment. The framework was tested against six high-risk conjunction scenarios identified within a simulated GPS constellation, including a critical head-on encounter with a 221-meter miss distance. A parametric study was conducted across three temporal regimes: Strategic (&gt; 8 hours warning), Operational (∼ 3 hours), and Tactical (10 minutes). Key findings indicate: 1. The Cost of Delay: There is a severe nonlinear relationship between maneuver warning time and fuel consumption. Strategic maneuvers executed hours in advance require approximately 0.5 m/s of ∆V , whereas emergency tactical maneuvers require over 8.0 m/s—an 18-fold increase in fuel cost representing a power-law scaling with reduced warning time. 2. Quarter-Period Optimal Time Scale: When unconstrained, the optimization algorithm consistently converges to a maneuver duration of k ≈ T /4 (where T is the orbital period), representing the fundamental optimal time scale for closed-loop collision avoidance in circular orbits. For GPS satellites with T = 43,080 s, this yields k ≈ 10,800 s (3 hours). This convergence occurs because the three-burn return-to-station constraint can only be exactly satisfied when 2k = nT /2 (where n is a positive integer), with n = 1 providing the minimum-energy solution. Maneuver durations significantly below T /4 enter the hyperbolic scaling regime, while durations above T /4 yield no additional fuel savings. 3. Algorithm Robustness: The hybrid evolutionary algorithm successfully identified safe trajectories (Pc &lt; 10−6) for all test cases, demonstrating its capability to handle diverse encounter geometries. 4. Operational Viability: The proposed autonomous system enables a “low-energy drift” avoidance mode that is functionally unavailable to reactive ground-based systems, potentially extending satellite operational lifetimes by preserving critical station-keeping propellant. This research provides a quantitative basis for the implementation of onboard autonomous conjunction assessment and maneuver planning, offering a pathway to significantly enhance the resilience and longevity of critical space infrastructure.","abstract_has_math":false,"creators":["Chandramukhi, Poornadithya"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Coverstone, Victoria L"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Collision Avoidance, Satellite Constellations, Orbital Debris, Trajectory Optimization, Space Situational Awareness, Autonomous Systems, Multi-Objective Optimization"],"languages":["en"],"rights":["Copyright 2025 Poornadithya Chandramukhi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132699","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Coverstone, Victoria L"]},{"key":"dc:creator","label":"Author","values":["Chandramukhi, Poornadithya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-09"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Collision Avoidance, Satellite Constellations, Orbital Debris, Trajectory Optimization, Space Situational Awareness, Autonomous Systems, Multi-Objective Optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Poornadithya Chandramukhi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132699"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The exponential growth of the orbital debris population in Near-Earth space poses a significant threat to the sustainability of current and future satellite constellations. Traditional collision avoidance strategies, which typically rely on single-impulse maneuvers executed in response to ground-based warnings, often suffer from high propellant costs and operational inefficiencies due to late detection and reaction times. This thesis proposes and validates an autonomous, multi-objective optimization framework for collision avoidance maneuvers (CAMs) tailored for Medium Earth Orbit (MEO) constellations, specifically the Global Positioning System (GPS). The core of this research is the development of a “Hybrid Three-Burn Maneuver” strategy that ensures a closed-loop trajectory, returning the satellite precisely to its nominal station-keeping slot after evading the threat. The optimization engine utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimize collision probability (Pc) and total velocity change (∆V ). A high-fidelity simulation environment was constructed in MATLAB, incorporating J2-perturbed dynamics for debris and Keplerian propagation for satellites to capture realistic relative motion and nodal drift. The Probability of Collisionis computed using a robust K-series expansion method, enabling computationally efficient and numerically stable risk assessment. The framework was tested against six high-risk conjunction scenarios identified within a simulated GPS constellation, including a critical head-on encounter with a 221-meter miss distance. A parametric study was conducted across three temporal regimes: Strategic (> 8 hours warning), Operational (∼ 3 hours), and Tactical (10 minutes). Key findings indicate: 1. The Cost of Delay: There is a severe nonlinear relationship between maneuver warning time and fuel consumption. Strategic maneuvers executed hours in advance require approximately 0.5 m/s of ∆V , whereas emergency tactical maneuvers require over 8.0 m/s—an 18-fold increase in fuel cost representing a power-law scaling with reduced warning time. 2. Quarter-Period Optimal Time Scale: When unconstrained, the optimization algorithm consistently converges to a maneuver duration of k ≈ T /4 (where T is the orbital period), representing the fundamental optimal time scale for closed-loop collision avoidance in circular orbits. For GPS satellites with T = 43,080 s, this yields k ≈ 10,800 s (3 hours). This convergence occurs because the three-burn return-to-station constraint can only be exactly satisfied when 2k = nT /2 (where n is a positive integer), with n = 1 providing the minimum-energy solution. Maneuver durations significantly below T /4 enter the hyperbolic scaling regime, while durations above T /4 yield no additional fuel savings. 3. Algorithm Robustness: The hybrid evolutionary algorithm successfully identified safe trajectories (Pc < 10−6) for all test cases, demonstrating its capability to handle diverse encounter geometries. 4. Operational Viability: The proposed autonomous system enables a “low-energy drift” avoidance mode that is functionally unavailable to reactive ground-based systems, potentially extending satellite operational lifetimes by preserving critical station-keeping propellant. This research provides a quantitative basis for the implementation of onboard autonomous conjunction assessment and maneuver planning, offering a pathway to significantly enhance the resilience and longevity of critical space infrastructure.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Poornadithya Chandramukhi, accepted the attached license on 2025-12-08 at 18:36.","The student, Poornadithya Chandramukhi, submitted this Thesis for approval on 2025-12-08 at 18:58.","This Thesis was approved for publication on 2025-12-09 at 16:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23104 on 2026-02-19 at 18:46:52"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Debris collision avoidance maneuver optimization (CAMO) for satellite constellations"]}]}],"canonical_facts":{"dc:contributor":["Coverstone, Victoria L"],"dc:creator":["Chandramukhi, Poornadithya"],"dc:date":["2025-12","2025-12-09"],"dc:description":["The exponential growth of the orbital debris population in Near-Earth space poses a significant threat to the sustainability of current and future satellite constellations. Traditional collision avoidance strategies, which typically rely on single-impulse maneuvers executed in response to ground-based warnings, often suffer from high propellant costs and operational inefficiencies due to late detection and reaction times. This thesis proposes and validates an autonomous, multi-objective optimization framework for collision avoidance maneuvers (CAMs) tailored for Medium Earth Orbit (MEO) constellations, specifically the Global Positioning System (GPS). The core of this research is the development of a “Hybrid Three-Burn Maneuver” strategy that ensures a closed-loop trajectory, returning the satellite precisely to its nominal station-keeping slot after evading the threat. The optimization engine utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimize collision probability (Pc) and total velocity change (∆V ). A high-fidelity simulation environment was constructed in MATLAB, incorporating J2-perturbed dynamics for debris and Keplerian propagation for satellites to capture realistic relative motion and nodal drift. The Probability of Collisionis computed using a robust K-series expansion method, enabling computationally efficient and numerically stable risk assessment. The framework was tested against six high-risk conjunction scenarios identified within a simulated GPS constellation, including a critical head-on encounter with a 221-meter miss distance. A parametric study was conducted across three temporal regimes: Strategic (> 8 hours warning), Operational (∼ 3 hours), and Tactical (10 minutes). Key findings indicate: 1. The Cost of Delay: There is a severe nonlinear relationship between maneuver warning time and fuel consumption. Strategic maneuvers executed hours in advance require approximately 0.5 m/s of ∆V , whereas emergency tactical maneuvers require over 8.0 m/s—an 18-fold increase in fuel cost representing a power-law scaling with reduced warning time. 2. Quarter-Period Optimal Time Scale: When unconstrained, the optimization algorithm consistently converges to a maneuver duration of k ≈ T /4 (where T is the orbital period), representing the fundamental optimal time scale for closed-loop collision avoidance in circular orbits. For GPS satellites with T = 43,080 s, this yields k ≈ 10,800 s (3 hours). This convergence occurs because the three-burn return-to-station constraint can only be exactly satisfied when 2k = nT /2 (where n is a positive integer), with n = 1 providing the minimum-energy solution. Maneuver durations significantly below T /4 enter the hyperbolic scaling regime, while durations above T /4 yield no additional fuel savings. 3. Algorithm Robustness: The hybrid evolutionary algorithm successfully identified safe trajectories (Pc < 10−6) for all test cases, demonstrating its capability to handle diverse encounter geometries. 4. Operational Viability: The proposed autonomous system enables a “low-energy drift” avoidance mode that is functionally unavailable to reactive ground-based systems, potentially extending satellite operational lifetimes by preserving critical station-keeping propellant. This research provides a quantitative basis for the implementation of onboard autonomous conjunction assessment and maneuver planning, offering a pathway to significantly enhance the resilience and longevity of critical space infrastructure.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Poornadithya Chandramukhi, accepted the attached license on 2025-12-08 at 18:36.","The student, Poornadithya Chandramukhi, submitted this Thesis for approval on 2025-12-08 at 18:58.","This Thesis was approved for publication on 2025-12-09 at 16:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23104 on 2026-02-19 at 18:46:52"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132699"],"dc:language":["en"],"dc:rights":["Copyright 2025 Poornadithya Chandramukhi"],"dc:subject":["Collision Avoidance, Satellite Constellations, Orbital Debris, Trajectory Optimization, Space Situational Awareness, Autonomous Systems, Multi-Objective Optimization"],"dc:title":["Debris collision avoidance maneuver optimization (CAMO) for satellite constellations"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}