mlsanityData to 7 Oct 2026

Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker

arXiv 2112.08540 ↗First posted 16 Dec 2021eess.SY cs.AI cs.ROcited by 1

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Abstract (arXiv)

We develop an integrated guidance and control system that in conjunction with a stabilized seeker and landing site detection software can achieve precise and safe planetary landing. The seeker tracks the designated landing site by adjusting seeker elevation and azimuth angles to center the designated landing site in the sensor field of view. The seeker angles, closing speed, and range to the designated landing site are used to formulate a velocity field that is used by the guidance and control system to achieve a safe landing at the designated landing site. The guidance and control system maps this velocity field, attitude, and rotational velocity directly to a commanded thrust vector for the lander's four engines. The guidance and control system is implemented as a policy optimized using reinforcement meta learning. We demonstrate that the guidance and control system is compatible with multiple diverts during the powered descent phase, and is robust to seeker lag, actuator lag and degradation, and center of mass variation induced by fuel consumption. We outline several concepts of operations, including an approach using a preplaced landing beacon.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

8 of 21 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2016Learning to reinforcement learncited by 369
  5. 2017Meta Learning Shared Hierarchiescited by 93
  6. 2015Gated Feedback Recurrent Neural Networkscited by 54
  7. 2020Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loopcited by 2
  8. 2018Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Powered Descent and Landingcited by 1

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Citing papers per year: 2023: 1.

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