Multi-Active Debris Removal Tour Design
Working with Astroscale, we developed and studied the feasiblity of a two-spacecraft system for removing multiple tonne class orbital debris per mission.
Space Sustainability and Autonomy Lab
Developing intelligent, safe, and sustainable autonomous systems for space exploration, space debris removal, and proximity operations.
Learn More
As of August 2024, more than 140 million pieces of debris smaller than 1 cm (0.4 in), about 1.2 million pieces of debris 1–10 cm, and around 54,000 pieces larger than 10 cm (3.9 in) were estimated to be in orbit around the Earth [ESA Space Debris Report].
These populations are growing.
About
SSA Lab is an academic lab in the Grainger School of Engineering at the University of Illinois, Urbana-Champaign. We focus primarily on solving the space debris problem by addressing critical challenges in missions with rendezvous and proximity operations.
Researchers
Minduli Wijayatunga
Director/Assistant Professor at UIUC
Adjunct Assistant Professor of Aerospace, UIUC - 2025-2026
Incoming Assistant Professor of Aerospace, UIUC - 2026
Visiting Scientist, MIT - 2025-2026
Research Associate, Australian Centre for Field Robotics - 2024-2025.
Interests: Rendezvous and Proximity Operations, Astrodynamics, Reinforcement Learning, Multi-Objective Trajectory Optimization, Model Predictive Control, and Convex/Indirect Optimization Methods
Research Projects
Preliminary Mission Design Tool (PMDT)
PMDT generates multi-target, fuel- and time-optimal tours for active debris removal (ADR) missions using low-thrust propulsion. It accounts for various mission factors, such as J2 perturbations, drag, eclipses, and duty cycles. The tool can rapidly compute reference trajectories, providing solutions for multi-target low-thrust tours within seconds. It uses J2 for making RAAN changes, reducing fuel consumption.
Multi-Objective Trajectory Optimization
We participate in Global Trajectory Optimization Competitions (GTOCs) where we develop indirect and convex-based tools for complex trajectory optimization problems.
CORTEX: Convex Optimization for Rendezvous Trajectory Optimization
CORTEX is a sunlight aware, robust convex-tracking scheme for final approach in RPO missions
Model Predictive Control
We developed a convex-based MPC for low thrust transfer trajectory guidance
Reinforcement Learning
We developed a robust guidance scheme for far-range rendezvous using reinforcement learning with safety and observability considerations and are looking into other RL applications in space.
GRASP: Guidance and Rendezvous for Autonomous Servicing and Planning
We developed the GRASP framework to handle end-to-end transfer and guidance of spacecraft servicing missions.
Publications
Selected journal and conference papers.
Journal Articles
- Robust trajectory design and guidance for far-range rendezvous using reinforcement learning with safety and observability considerations
- GTOC12: Results from TheAntipodes
- Convex-Optimization-Based Model Predictive Control for Space Debris Removal Mission Guidance
- Exploiting Scaling Constants to Facilitate the Convergence of Indirect Trajectory Optimization Methods
- Design and guidance of a multi-active debris removal mission
Conference Papers
- GRASP: An Integrated Framework for Mission Design and Guidance for Autonomous In-Orbit Servicing Missions
- System Design and Hardware-In-The-Loop Testbed Development for an Australian ISAM Demonstrator Mission
- CORTEX: Real-Time Trajectory Optimization and Guidance for the Final Approach Phase of In-Orbit Servicing Missions
- Assessing the Feasibility of Ion Beam-Based Asteroid Deflection for Planetary Defense
- State-dependent trust region for successive convex optimization of spacecraft trajectories
Open Positions
I am currently looking for two PhD students to start in 2026 for the first two topics listed. Please contact me if interested
- Rendezvous and Proximity Operations and Hardware Testing (Requirement: Astrodynamics background, previous publications, strong grades, some hardware experience)
- Space Debris Removal (Requirement: Astrodynamics background, previous publications, strong grades, interest in debris removal)