Research Clone

Spacecraft autonomy is foundational to sustained, complex in-space operations and uninterrupted delivery of space-based services. It is especially critical for missions that require rendezvous, proximity operations, and docking (RPOD). The RPOD process involves multiple steps, some of which may require inspection, use of a manipulator, or coordinating movements between spacecraft. The LASR Lab investigates multiple research problems towards the development of more efficient, adaptive, and safe RPOD operations, including:

• Use of a Reinforcement Learning (RL) controller to incentivize acquisition of well-illuminated optical data during on-orbit spacecraft inspection
• Simulation of a manipulator using Simscape Multibody with validation on a mock satellite bus
• Development of control barrier functions to enable safe servicing agent relocation under pose uncertainty
• Development of a computationally efficient multi-agent model predictive control (MPC) formulation that uses an integral Chebyshev collocation method
• Minimum energy, low-thrust relative trajectory design for the deployment of spacecraft servicing modules onto an uncontrolled bundle of assembly components
• Use of tensegrity principles to design a formation-keeping controller for loitering spacecraft
• Use of probabilistic Markov models to transition between guidance modes to enable robust and efficient proximity operations

Relevant literature:

• van Wijk, D., Dunlap, K., Majji, M., and Hobbs, K. L., “Deep Reinforcement Learning for Autonomous Spacecraft Inspection Using Illumination,” arXiv preprint arXiv:2308.02743, 2023.
• Tidmore, H., Hilburn, E., van Wijk, D. E., and Majji, M., “Validation of In-Space Servicing, Assembly, and Manufacturing Missions Using Simscape Multibody,” 2025 22nd International Conference on Ubiquitous Robots (UR), IEEE, 2025, pp. 375–381.
• Parikh, D., van Wijk, D., and Majji, M., “Safe Multi-Agent Satellite Servicing with Control Barrier Functions,” arXiv preprint arXiv:2502.10480, 2025.
• Parikh, D., Ahrens, T. L., and Majji, M., “A Rapid Trajectory Optimization and Control Framework for Resource-Constrained Applications,” 2025 American Control Conference (ACC), IEEE, 2025, pp. 1687–1692.
• Down, I. M., van Wijk, D. E., Parikh, D., and Majji, M., “Autonomous Satellite Servicing Infrastructure for In-Space Assembly and Manufacturing,” Journal of Manufacturing Science and Engineering, Vol. 146, No. 12, 2024, p. 121008.
• Ingalls, B., Nelson, Q., Fry, M., Hussain, I., Majji, M., and Garcia Carrillo, L. R., “Hardware Demonstration of Tensegrity-Based Multi-Agent Formation Control,” 2026 AAS/AIAA Guidance, Navigation, and Control Conference, 2026.
• Parikh, D., Khowaja, A. H., and Majji, M., “Robust Proximity Operations Using Probabilistic Markov Models,” arXiv preprint arXiv:2409.19062, 2024.