Advanced Space Concepts Laboratory · RPI

Defining the future of
autonomous, safe and
sustainable
space exploration

We develop rigorous analytical and computational frameworks for multi-body dynamical systems, enabling observer constellation design for space domain awareness (SDA), adversarial RSO tracking and maneuver detection, terrain-relative navigation, and autonomous guidance, navigation, and control (GN&C) for planetary landing and deep-space missions.

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Research Focus

Multi-disciplinary challenges at the frontier of space travel and surveillance

01

Cislunar SDA · AFOSR-funded

Sensor-Exclusion Informed, Information-Theoretic Design of Lunar Surface Observer Network for Selenocentric Tracking

Submodular D-optimality optimization of lunar surface sensor placements for robust cislunar RSO custody. The framework integrates terrain-aware bright-body exclusion (Sun, Earth), IEKF-based FIM aggregation across full RSO catalogs, and a Greedy Sequential Differential Evolution (GSDE) optimizer for near-real-time placement decisions.

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Key Capabilities

Submodular placement with Sun/Earth exclusion zones

D-optimality via log-det FIM maximization

IEKF tracking across full RSO catalog

Greedy Sequential Differential Evolution (GSDE)

Ground-track & 3D constellation visualization

CSV export of network performance metrics

Key Contributions

Node-removal sensitivity analysis across orbit families (L1/L2 Halo, NRHO, DRO)

FIM-based degradation curves quantifying coverage loss per node lost

Identification of critical vs. redundant constellation members

Attrition-resilience ranking across orbit family geometries

02

Adversarial SDA · Constellation Survivability

Characterizing Adversarial Attrition Rate for Cislunar Observer Constellations

Quantifies how quickly a hostile actor can degrade a cislunar SDA constellation's tracking coverage through targeted removal or denial of individual observer nodes, across L1/L2 Halo, NRHO, and DRO periodic orbit families, establishing attrition-rate metrics for resilient constellation design under adversarial threat models.

03

Low-Thrust Trajectories · Probabilistic Reachability

Surrogate-Based Probabilistic Reachability Level-Sets for Cislunar Continuous-Thrust Transfers

Gaussian process surrogates accelerate low-thrust reconfiguration and transfers by learning the optimal cost landscape across periodic orbit families. Probabilistic reachability level sets rapidly quantify accessible destinations under navigation uncertainty without repeated TPBVP solves, while invariant-manifold-seeded ballistic terminal coast arcs reduce propellant requirements.

Key Contributions

GP surrogates with ARD kernels — NHL/SHL families

Probabilistic reachability under navigation error

Manifold-seeded TPBVP initialization

Ballistic terminal coast arc exploitation

Key Contributions

Classification of linear and non-linear quasi-periodic behavior

Formulation of OCP in torus space

Comparison of results with phase space TPBVP solutions

Rephasing for reconfiguration of space constellations

04

Astrodynamics · Invariant Tori · CR3BP

Optimal Transport Architectures for Geometry Reconfiguration through Rephasing on Invariant Tori in CR3BP

Expand reachability framework to leverage quasi-periodic invariant structures seeded from periodic orbits in the CR3BP. Develop cost functions that define novel and relevant metrics and compare solutions in torus and phase spaces.

All Research →

Funding & Partnerships

Research Sponsors & Collaborators

Supported by federal agencies and industry partners advancing the cislunar mission.

AFOSR Grant FA9550-25-1-0313 — Reconfigurable Cislunar Autonomous Tracking-Constellations for Space Domain Awareness (RCAT-CS), in collaboration with Texas A&M University. Aerospace America coverage ↗

Latest

Lab Updates & Events

ASCLab @ Events

Recent News

July 2026
Kevin Alvarado and Hadley Earl publish their paper on Resilient Maneuver Detection using modal decomposition to characterize adversarial intent in the Journal of the Astronautical Sciences.
April 2026
Ryan Rebetti and Nathan Nguyen place 1st and 2nd at the 2026 Graduate Research Symposium Poster Competition.
March 2026
ASCLab Alumni and Students featured in the MANE Newsletter.
March 2026
Alumni Calvin Chan publishes in AIAA Journal of Spacecraft and Rockets.
October 2025
Aerospace America covers the RCAT-CS project as lead story.
September 2025
AFOSR grant awarded — reconfigurable cislunar RSO tracking constellation in collaboration with Texas A&M.
August 2025
4 papers presented at the 2025 AAS/AIAA Astrodynamics Specialist Conference, Boston, MA.
May 2025
Prof. Singh receives RPI OVPR Catalyst Grant 2025 for Modal Decomposition Feature Space for RSO Maneuver Characterization.
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April 2025
PhD student Scott Blender on Honorable Mentions list for NSF GRFP 2025.
April 2025
Prof. Singh invited as judge for the 2025 NASA Techleap Prize competition.
March 2025
Kevin Alvarado publishes research article in Aerospace, MDPI.
December 2024
Ickbum Kim publishes on Probabilistic Regression for TRN in Nature Scientific Reports.
July 2024
Prof. Singh awarded the Class of 1951 Outstanding Teaching Award (2024).
May 2024
Kevin Alvarado publishes first peer-reviewed journal article in MDPI Aerospace.
January 2024
Dr. Singh weighs in on NASA's CLPS initiative — Wall Street Journal.
October 2023
Dr. Singh discusses the space-race to the lunar south pole and Chandrayaan-3.
August 2023
Kevin Alvarado receives NASA NY Space Grant Student Fellowship 2024.
August 2023
Dr. Singh chairs the GN&C II session at the 2023 AAS/AIAA ASC, Big Sky, MT.
March 2023
Dr. Singh named RPI representative to the Universities Space Research Association.
March 2023
Dr. Singh selected to serve on the AIAA Astrodynamics Technical Committee.

Get in Touch

Contact ASCLab

Send all enquiries for research positions to sandes5@rpi.edu. Include your CV with education, past research/work experience and a list of references.

Email
sandes5@rpi.edu
Address
J. Erik Jonsson Engineering Center, JEC 2112
110 8th Street, Troy NY 12180
Phone
(518) 276-2625

J. Erik Jonsson Engineering Center · RPI Campus · Troy, NY