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}
- }
- , "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 Abraham Vinod Delivers Invited Talks at The University of Texas at Austin and The University of Texas at Dallas Date: November 11, 2025 - November 13, 2025
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- MERL researcher Abraham Vinod was invited to present MERL's latest research at the University of Texas at Austin and The University of Texas at Dallas this November. His talk discussed a tractable set-based method for a broad class of robust control problems with nonlinear dynamics and bounded uncertainty, with applications to powered descent guidance and drone motion planning problems. Additionally, he also presented MERL's recent research on environmental monitoring using hetereogenous robots, with applications in disaster management and search-and-rescue.
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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: Vedang M. Deshpande; Stefano Di Cairano; 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.