AI-Guided Rocket Landing: Navigating Precision Descent Strategies

DOI

10.2991/978-94-6463-496-9_28How to use a DOI?

Keywords

Autonomous rocket landing; Reinforcement learning; Precision landing; Real-time decision-making; Proximal Policy Optimization (PPO) algorithm; Unity ML-Agents

Abstract

Autonomous rocket landing stands as a crucial milestone in aerospace engineering, pivotal for the realization of safe and cost-effective space missions. This paper introduces a pioneering approach that harnesses reinforcement learning methodologies to enhance the precision and efficiency of rocket landing procedures. Grounded on a realistic Falcon 9 model, the study integrates sophisticated control mechanisms including Thrust Vector Control (TVC) and Cold Gas Thrusters (CGT), ensuring agile propulsion and balance adjustments. Observational data, encompassing critical parameters like rocket position, orientation, and velocity, guide the reinforcement learning algorithm in making real-time decisions to optimize landing trajectories. Through the strategic implementation of curriculum learning strategies and the Proximal Policy Optimization (PPO) algorithm, the rocket agent undergoes iterative training, steadily improving its capabilities to execute soft landings on designated pads. Experimental results underscore the efficacy of the proposed methodology, exhibiting remarkable proficiency in achieving precise and controlled descents. This research represents a significant stride in the advancement of autonomous landing systems, poised to revolutionize space exploration missions and unlock new frontiers in commercial rocketry endeavors.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hicham Bouchana
AU  - Meftah Zouai
AU  - Ahmed Aloui
AU  - Guadalupe Ortiz
AU  - Dounya Kassimi
PY  - 2024
DA  - 2024/08/31
TI  - AI-Guided Rocket Landing: Navigating Precision Descent Strategies
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 371
EP  - 386
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_28
DO  - 10.2991/978-94-6463-496-9_28
ID  - Bouchana2024
ER  -