TR2024-174
Divert-feasible lunar landing under navigational uncertainty
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- "Divert-feasible lunar landing under navigational uncertainty", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC56724.2024.10886637, December 2024, pp. 7497-7503.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,
- pages = {7497--7503},
- month = dec,
- doi = {10.1109/CDC56724.2024.10886637},
- url = {https://www.merl.com/publications/TR2024-174}
- }
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- "Divert-feasible lunar landing under navigational uncertainty", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC56724.2024.10886637, December 2024, pp. 7497-7503.
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MERL Contacts:
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Research Areas:
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
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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, RoboticsBrief- 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.
- 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.