Publications

The following publications have been produced using BSK-RL and its predecessors. If you have used BSK-RL in your research, please let us know and we will add your publication to the list.

Journal Papers

  1. A. P. Herrmann and H. Schaub, “Monte Carlo Tree Search Methods for the Earth-Observing Satellite Scheduling Problem,” Journal of Aerospace Information Systems, Vol. 19, No. 1, January 2022, pp. 70–82. doi:10.2514/1.I010992.

  2. A. Harris, T. Valade, T. Teil and H. Schaub, “Generation of Spacecraft Operations Procedures Using Deep Reinforcement Learning,” Journal of Spacecraft and Rockets, Vol. 59, No. 2, March–April 2022, pp. 611–626. doi:10.2514/1.A35169.

  3. A. P. Herrmann and H. Schaub, “Reinforcement Learning for the Agile Earth-Observing Satellite Scheduling Problem,” IEEE Transactions of Aerospace and Electronic Systems, Vol. 59, No. 5, Oct. 2023, pp. 5235–5247. doi:10.1109/TAES.2023.3251307.

  4. A. Herrmann and H. Schaub, “A Comparative Analysis of Reinforcement Learning Algorithms for Earth-Observing Satellite Scheduling,” Frontiers in Space Technologies, November 29, 2023. doi:10.3389/frspt.2023.1263489.

  5. A. Herrmann, M. Stephenson and H. Schaub, “Single-Agent Reinforcement Learning for Scalable Earth-Observing Satellite Constellation Operations,” Journal of Spacecraft and Rockets. doi:10.2514/1.A35736.

  6. A. Herrmann and H. Schaub, “Autonomous Small Body Science Operations Using Reinforcement Learning,” Journal of Aerospace Information Systems, Vol. 21, No. 10, October 2024, pp. 865–884. doi:10.2514/1.I011376.

  7. M. Stephenson and H. Schaub, “Optimal Agile Satellite Target Scheduling with Learned Dynamics,” Journal of Spacecraft and Rockets. doi:10.2514/1.A36097.

  8. M. Stephenson and H. Schaub, “On-board Policies for Autonomous Earth Observing Satellite Scheduling with semi-MDPs,” Journal of Aerospace Information Systems, accepted for publication.

  9. L. Quevedo Mantovani and H. Schaub, “Deep Reinforcement Learning for Satellite Autonomy with Cloud Coverage Uncertainty,” Journal of Aerospace Information Systems, manuscript submitted for publication.

Conference Papers

  1. A. Harris. T. Teil and H. Schaub, “Spacecraft Decision-Making Autonomy using Deep Reinforcement Learning,” AAS Spaceflight Mechanics Meeting, Maui, Hawaii January 13–17, 2019.

  2. A. Harris and H. Schaub, “Spacecraft Command and Control with Safety Guarantees using Shielded Deep Reinforcement Learning” AIAA SciTech Forum, Orlando, Florida, Jan. 6–10, 2020.

  3. A. Herrmann and H. Schaub, “Monte Carlo Tree Search With Value Networks For Autonomous Spacecraft Operations,” AAS/AIAA Astrodynamics Specialist Conference, Lake Tahoe, CA, Aug. 9–13, 2020.

  4. A. Herrmann and H. Schaub, “Autonomous Spacecraft Tasking using Monte Carlo Tree Search Methods,” AAS/AIAA Space Flight Mechanics Meeting, Charlotte, NC, January 31–February 4, 2021.

  5. A. Herrmann and H. Schaub, “Autonomous On-board Planning for Earth-orbiting Spacecraft,” IEEE Aerospace Conference, Big Sky, MT, March 5–12, 2022.

  6. I. Nazmy, A. Harris, M. Lahijanian and H. Schaub, “Shielded Deep Reinforcement Learning for Multi-Sensor Spacecraft Imaging,” American Control Conference, Atlanta, GA, June 8–10, 2022.

  7. A. Herrmann and H. Schaub, “A Comparison Of Deep Reinforcement Learning Algorithms For Earth-Observing Satellite Scheduling,” AAS/AIAA Spaceflight Mechanics Meeting, Austin, TX, Jan. 15–19, 2023.

  8. V. Bajenaru, A. Herrmann, H. Schaub and S. Philipps, “Command and Control of Satellite Constellations using Explainable Deep Reinforcement Learning,” AAS Rocky Mountain GN&C Conference, Breckenridge, CO, Feb. 2–8, 2023.

  9. A. Herrmann, M. Stephenson and H. Schaub, “Reinforcement Learning for Multi-Satellite Agile Earth Observing Scheduling Under Various Communication Assumptions,” AAS Rocky Mountain GN&C Conference, Breckenridge, CO, Feb. 2–8, 2023.

  10. A. Herrmann and H. Schaub, “Reinforcement Learning For Small Body Science Operations,” AAS Astrodynamics Specialist Conference, Charlotte, North Carolina, August 7–10 2022, Paper No. AAS 22-563.

  11. M. Stephenson and H. Schaub, “Optimal Target Sequencing In The Agile Earth-Observing Satellite Scheduling Problem Using Learned Dynamics,” AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Montana, August 13–17 2023.

  12. M. Stephenson, L. Quevedo Mantovani, S. Phillips and H. Schaub, “Using Enhanced Simulation Environments to Accelerate Reinforcement Learning for Long-Duration Satellite Autonomy,” AIAA Science and Technology Forum and Exposition (SciTech), Orlando, Florida, Jan. 8–12, 2024. doi:10.2514/6.2024-0990.

  13. M. Stephenson and H. Schaub, “Reinforcement Learning for Earth-Observing Satellite Autonomy with Event-Based Task Intervals,” AAS Rocky Mountain GN&C Conference, Breckenridge, CO, Feb. 2–7, 2024.

  14. M. Stephenson, L. Quevedo Mantovani and H. Schaub, “Intent Sharing for Emergent Collaboration in Autonomous Earth Observing Constellations,” AAS Rocky Mountain GN&C Conference, Breckenridge, CO, Feb. 2–7, 2024.

  15. L. Quevedo Mantovani, Y. Nagano and H. Schaub, “Reinforcement Learning For Satellite Autonomy Under Different Cloud Coverage Probability Observations,” AAS Astrodynamics Specialist Conference, Broomfield, CO, Aug. 11–15 2024.

  16. M. Stephenson and H. Schaub, “BSK-RL: Modular, High-Fidelity Reinforcement Learning Environments for Spacecraft Tasking,” International Astronautical Congress, Milan, Italy, Oct. 14–18 2024.

  17. M. Stephenson and H. Schaub, “Scalable Autonomous Decentralized Constellation Tasking on Asynchronous Semi-Markov Decision Processes,” International Workshop on Satellite Constellations & Formation Flying, Kaohsiung, Taiwan, December 2–4, 2024.

  18. Y. Nagano and H. Schaub, “Fault Resilience of Reinforcement-Based Satellite Autonomous Task Scheduling,” AAS Space Flight Mechanics Meeting, Kauai, HI, January 19–23, 2025.

  19. M. Stephenson, L. Quevedo Mantovani, A. Cheval and H. Schaub, “Quantifying The Optimality Of A Distributed Rl-Based Autonomous Earth-Observing Constellation,” AAS Guidance, Navigation and Control Conference, Breckenridge, CO, January 31 – February 5, 2025.

  20. L. Quevedo Mantovani and H. Schaub, “Improving Robustness Of Autonomous Spacecraft Scheduling Using Curriculum Learning,” AAS Guidance, Navigation and Control Conference, Breckenridge, CO, January 31 – February 5, 2025.

  21. M. Stephenson, D. Huterer Prats and H. Schaub, “Autonomous Satellite Inspection in Low Earth Orbit with Optimization-Based Safety Guarantees,” International Workshop on Planning & Scheduling for Space, Toulouse, France, April 28–30, 2025.

  22. M. Stephenson, L. Quevedo Mantovani and H. Schaub, “Achieving Near-Optimal Performance in Autonomous Earth Observing Satellite Scheduling using semi-MDPs,” International Workshop on Planning & Scheduling for Space, Toulouse, France, April 28–30, 2025.

  23. L. Quevedo Mantovani and H. Schaub, “Performance Evaluation of Shielded Neural Networks for Autonomous Agile Earth Observing Satellites in Long Term Scenarios,” International Workshop on Planning & Scheduling for Space, Toulouse, France, April 28–30, 2025.

  24. A. Cheval and H. Schaub, “Reinforcement Learning For Autonomous Strip Imaging Task Scheduling In Super-Agile Satellites,” AAS Astrodynamics Specialist Conference, Boston, Massachusetts, August 10–14, 2025.

  25. Y. Nagano and H. Schaub, “Enhancing Fault Resilience In {RL}-Based Satellite Autonomous Task Scheduling,” AAS Astrodynamics Specialist Conference, Boston, Massachusetts, August 10–14, 2025.

  26. D. Huterer Prats and H. Schaub, “Reinforcement Learning for Space-to-Space Surveillance: Autonomous Scheduling for Resident Space Object Imaging,” Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, Hawaii, September 16–19, 2025.

  27. M. Stephenson and H. Schaub, “Safe, Autonomous Multiagent Inspection of Space Objects Leveraging Relative Orbit Dynamics,” Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, Hawaii, September 16–19, 2025.