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LOW-THRUST TRAJECTORY OPTIMIZATION

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NASA Chief Technologist Visit

NASA Chief Technologist John Dankanich visited our laboratory (2022). 

Adrian Arustei poster presentation. Doctoral student Adrian Arustei presented his research at our college symposium (2024). 

Amrutha Dasyam presenting at Engineering Open House

Doctoral student Amrutha Dasyam presented her research at college open house (2023). 

Matthew Chace presenting durinng Science Olympiad in 2023.

MS student Matthew Chace presenting a few spacecraft trajectories to students touring our laboratory during Science Olympiad National Tournament held at WSU (2023). 

Spacecraft employing solar-electric propulsion generates low magnitudes of thrust, limited by the amount of power generated by the solar panels onboard the spacecraft. Computation of optimal low-thrust geocentric orbit-raising trajectories requires the solution of a nonlinear, non-convex, multi-phase optimal control problem that is challenging to solve. In addition, orbit-raising in cislunar space has added complexity owing to the effect of strong lunar gravitational forces. 

KEY CONTRIBUTIONS 

  • Developed h-e orbital elements for describing the dynamics of a spacecraft. 
  • Developed a sequential algorithm for computing sub-optimal low-thrust trajectories in a fast, robust and automated manner. 
  • Demonstrated that a traditional direct optimization framework using adjoint sensitivities can compute good quality orbit-raising trajectories using the sequential solution as an initial guess. 
  • Developed a cascaded deep reinforcement learning framework to compute geocentric low-thrust orbit-raising trajectories. 
  • Developed a deep reinforcement learning framework, incorporating single-agent actor-critic attetion network, to compute geocentric and cislunar low-thrust orbit-raising trajectories. 

NASA EPSCOR CAN PROJECT 

(2020-2025)  

Title: Artificial Intelligence Assisted Spacecraft Trajectory Optimization and Planning 

NASA Award Number: 80NSSC20M0217 

Project Team

Science Investigator (Science-PI):

    Atri Dutta, Aerospace Engineering, Wichita State University (WSU) 

Co-Investigators:

   James Steck, Aerospace Engineering, WSU 

   Craig McLaughlin, Aerospace Engineering, University of Kansas (KU)  

   Arslan Munir, Computer Science, Kansas State University (KSU)

Kansas NASA EPSCOR Director/PI: 

   L. Scott Miller, Aerospace Engineering, WSU (2020-2023) 

   Linda Kliment, Aerospace Engineering, WSU (2023-2025) 

NASA Technical Monitor: 

   John Dankanich, Chief Technologist, NASA Marshall Space Flight Center 

Industry Collaborator: 

   Pradipto Ghosh, Senior Mission Design and Navigation Engineer, JHU Applied Physics Laboratory  

STEM Partner: 

   William Polite, Director of Equity, Diversity and Accountability, Wichita Public School 

Students (directly funded by project): 

   Amrutha Dasyam, PhD Student, Aerospace Engineering, WSU (2022-Current)

   Adrian Arustei, PhD Student, Aerospace Engineering, WSU (2022-2023) 

   Kyle Messick, PhD Student, Aerospace Engineering, WSU (2022)  

   Yrithu, PhD Student, Aerospace Engineering, WSU (2020-2023) 

   Matthew Chace, MS Student, Aerospace Engineering, WSU (2022-2024) 

   Ella Kreger, UG Student, Aerospace Engineering (2024)  

   Melvin Rafi, Postdoctoral Researcher, Aerospace Engineering (2020) 

   Syed Talha Zaidi, PhD Student, Computer Science, KSU (2022-2024)  

   Ali H. Mughal, MS Student, Computer Science, KSU (2021-2022)   

   Mahmood Azhar Qureshi, Computer Science, KSU (2021)  

   Hayat Ullah, PhD Student, Computer Science, KSU (2022)  

   Charles A. Fry, MS Student, Aerospace Engineering, KU (2021-2023) 

   Orion Roach, MS Student, Aerospace Engineering, KU (2024-2025) 

Other students engaged for research experience and on related projects: 

   Pardhasai Chadalavada, PhD Student, WSU (2020-2022) 

   Tanzimul Farabi, MS Student, WSU (2020-2021) 

   Ramses Young, UG Student, WSU (2020-2021) 

   Laura Elliott, MS Student, WSU (2023-2024) 

   Noah Johson, MS Student, WSU (2024-2025)  

   Jonathann Maye, MS Student, WSU (2023) 

Project Publications

Zaidi, T., Arustei, A., Munir, A., & Dutta, A. (2025). Single-Agent Attention Actor-Critic: A Deep Reinforcement Learning-Based Solution for Low-Thrust Spacecraft Trajectory Optimization. IEEE Transactions on Aerospace and Electronic Systems. accepted for publication. 

Zaidi, T., Arustei, A., Munir, A., & Dutta, A. (2025). Automated Trajectory Planning: A Cascaded Deep Reinforcement Learning Approach for Low-Thrust Spacecraft Orbit-Raising. IEEE Aerospace and Electronic Systems Magazine. doi: 10.1109/MAES.2025.3556795. https://ieeexplore.ieee.org/document/10947520 

Steck, J., Arustei, A., & Dutta, A. (2025). Low Thrust Minimum Time Transfer: Single Network Adaptive Critic Approximate Dynamic Programming. AAS/AIAA Space Flight Mechanics Meeting. Kauai, HI. AAS 25-311. 

Arustei, A., Dutta, A (2024). Direct Optimization of Low-Thrust Orbit-Raising Maneuvers using Adjoint Sensitivities,. Acta Astronautica. Vol. 219, pp. 965-981.  https://doi.org/10.1016/j.actaastro.2024.03.059  

Pillay, Y., Chace, M., Steck, J., Watkins, J., and Dutta, A (2024). Neuro-adaptive Model Reference Tracking Controller for Cislunar Missions. AIAA Guidance Navigation and Control Conference,
AIAA SciTech Forum, Orlando FL. AIAA 2024-0509. https://doi.org/10.2514/6.2024-0509 

Dutta, A., Arustei, A., Chace, M., Chadalavada, P., Steck,  J., Zaidi, T. & Munir, A (2024). Machine
Learning Assisted Low-Thrust Orbit-Raising: A Comparative Assessment of a Sequential Al-
gorithm and a Deep Reinforcement Learning Approach (2024).  AAS/AIAA Space Flight Mechanics Meeting, AIAA SciTech Forum. Orlando FL. AIAA 2024-1669. https://doi.org/10.2514/6.2024-1669 

Zaidi, A., Chadalavada, P., Ullah, H., Munir, A., and Dutta, A (2023). Cascaded Deep Reinforcement
Learning-Based Multi-Revolution Low-Thrust Spacecraft Orbit-Transfer. IEEE Access,
vol. 11, pp. 82894-82911, https://ieeexplore.ieee.org/document/10207710.  

Fry, C. A (2023). An Exploration of Solar Radio Flux Forecasting Using Long Short-Term Memory Artificial Neural Networks. MS Thesis, University of Kansas.

Dasyam, A., Dutta, A (2023). Artificial Neural Network based Atmospheric Density Model for Aerobraking Trajectory Design. AAS/AIAA Space Flight Mechanics Meeting. Austin TX.

Mughal, A., Chadalavada, P., Munir, A., Dutta, A., & Qureshi, M (2022). Design of deep neural networks for transfer time prediction of spacecraft electric orbit-raising. Elsevier Intelligent Systems with Application. Vol. 15, Article No 200092. https://doi.org/10.1016/j.iswa.2022.200092

Pillay, Y., Chace, M., Steck, J., & Dutta, A (2022). Neural network for predicting unmodelled dynamics in multi-revolution transfers in cis-lunar missions. AAS/AIAA Astrodynamics Specialist Meeting. Charlotte, NC.

Arustei, A., Dutta (2022), A. An adjoint sensitivity method for the sequential low-thrust orbit-raising problem. AAS/AIAA Astrodynamics Specialist Conference. Charlotte NC.

Fry, C., McLaughlin, C (2022). Optimizing Long Short-Term Memory Neural Network to Forecast Solar Radio Flux. AAS/AIAA Astrodynamics Specialist Conference. Charlotte NC.  

Dasyam, A., Chadalavada, P., Fry, C., Dutta, A., & McLaughlin, C (2021). Neural Network Based Estimation of Atmospheric Density during Aerobraking. AAS/AIAA Astrodynamics Specialist Meeting. Held virtually.

Pillay, Y., Chace, M., Messick, K., Steck, J., & Dutta, A (2021). Modified State Observer for Characterization of Unmodeled Dynamics in Cis-lunar Missions. AAS/AIAA Astrodynamics Specialist Meeting. Held virtually.

Chadalavada, P., Dutta, A., & Ghosh, P (2021). An Efficient Algorithm for the Longitude-Targeted Ascent of All-Electric Satellites. AAS/AIAA Space Flight Mechanics Meeting (AIAA Scitech Forum). San Diego CA.

Farabi, T., & Dutta, A. (2021). Artificial Neural Network Based Prediction of Solar Array Degradation during Electric Orbit-Raising. AAS/AIAA Space Flight Mechanics Meeting. Virtual Conference. AAS 21-424.

Outreach

Astrodynamics Experience for High-School Students, a workshop held during June 24-28, 2024, and attended by six students from four Wichita-area high schools. Webpage for the event. 

Astronautics Workshop for High-School Teachers, a workshop held during June 3-7, 2024, and attended by four teachers from Wichita-area high schools. Webpage for the event.  

Lessons Learned with NASA Innovation and Technology Development. Public tak by NASA Chief Technologist John Dankanich, at Wichita State University. January 13, 2022.  

Astronautics Summer Camp, held during Summer 2022, attended by nine students from around Kansas. 

SELECTED PUBLICATIONS 

Zaidi, T., Arustei, A., Munir, A., & Dutta, A. (2025). Single-Agent Attention Actor-Critic: A Deep Reinforcement Learning-Based Solution for Low-Thrust Spacecraft Trajectory Optimization. IEEE Transactions on Aerospace and Electronic Systems. accepted for publication. 

Arustei, A., Dutta, A (2024). Direct Optimization of Low-Thrust Orbit-Raising Maneuvers using Adjoint Sensitivities,. Acta Astronautica. Vol. 219, pp. 965-981.  https://doi.org/10.1016/j.actaastro.2024.03.059  

Zaidi, A., Chadalavada, P., Ullah, H., Munir, A., and Dutta, A (2023). Cascaded Deep Reinforcement
Learning-Based Multi-Revolution Low-Thrust Spacecraft Orbit-Transfer. IEEE Access,
vol. 11, pp. 82894-82911, https://ieeexplore.ieee.org/document/10207710. (Computational code available on GitHub: https://github.com/talhazaidi13/Cascaded-Deep-Reinforcement-Learning-Based-Multi-Revolution-Low-Thrust-Spacecraft-Orbit-Transfer)  

S. Sreesawet, A. Dutta (2018). “Fast and Robust Computation of Low-Thrust Orbit-Raising Trajectories,” AIAA Journal of Guidance, Control and Dynamics, Vol 41, No 9, pp. 1888-1905. https://doi.org/10.2514/1.G003319

 

FUNDING ACKNOWLEDGMENT 

This research has been sponsored by:

  • Kansas NASA EPSCOR Program Seed Research Initiation grant (2014-2016).  
  • NASA EPSCOR CAN program grant number 80NSSC20M0217 (2020-2025).
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