TR2024-174

Divert-feasible lunar landing under navigational uncertainty


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.

 

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    •  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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