# Adaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2003.02182 · HTML: https://mlsanity.com/paper/2003.02182/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Advances in Trajectory Optimization for Space Vehicle Control](https://mlsanity.com/paper/2108.02335/) (2021)
- [Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers](https://mlsanity.com/paper/2102.12432/) (2021)
- [VisualEnv: visual Gym environments with Blender](https://mlsanity.com/paper/2111.08096/) (2021)
- [Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning](https://mlsanity.com/paper/2606.13605/) (2026)
- [Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing](https://mlsanity.com/paper/2211.04284/) (2022)
- [Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks](https://mlsanity.com/paper/2507.19535/) (2025)
- [Adversarial Reinforcement Learning for Robust Planetary Landing](https://mlsanity.com/paper/2610.20252/) (2026)

## Abstract (arXiv)

Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm, although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the above-mentioned guidance architecture according to the given problem constraints.