TR2024-174

Divert-feasible lunar landing under navigational uncertainty


    •  Lishkova, Y., Vinod, A.P., Di Cairano, S., Weiss, A., "Divert-feasible lunar landing under navigational uncertainty", IEEE Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-174 PDF
      • @inproceedings{Lishkova2024dec,
      • author = {Lishkova, Yana and Vinod, Abraham P. and Di Cairano, Stefano and Weiss, Avishai}},
      • title = {Divert-feasible lunar landing under navigational uncertainty},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-174}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Dynamical Systems

Abstract:

We develop a guidance policy for lunar landing under navigational uncertainty with feasible divert in the event a hazard is detected. Offline, we compute stochastic controllable sets under convexified dynamics and constraints that characterize the set of noisy state estimates from which the lander can be driven to a landing site with a pre-specified, sufficiently high probability. We establish that the sets computed for the convexified problem are inner-approximations of the true stochastic controllable sets. The controllable sets are parameterized by available fuel mass and length of trajectory, and provide a tractable method to quickly assess online whether a landing site is reachable. Numerical simulations demonstrate the efficacy of the approach.

 

  • Related News & Events

    •  NEWS    MERL researchers present 7 papers at CDC 2024
      Date: December 16, 2024 - December 19, 2024
      Where: Milan, Italy
      MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; James Queeney; Abraham P. Vinod; Avishai Weiss; Gordon Wichern
      Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
      Brief
      • MERL researchers presented 7 papers at the recently concluded Conference on Decision and Control (CDC) 2024 in Milan, Italy. The papers covered a wide range of topics including safety shielding for stochastic model predictive control, reinforcement learning using expert observations, physics-constrained meta learning for positioning, variational-Bayes Kalman filtering, Bayesian measurement masks for GNSS positioning, divert-feasible lunar landing, and centering and stochastic control using constrained zonotopes.

        As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.

        In addition, Ankush Chakrabarty (Principal Research Scientist, Multiphysical Systems Team) was an invited speaker in the pre-conference Workshop on "Learning Dynamics From Data" where he gave a talk on few-shot meta-learning for black-box identification using data from similar systems.
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