Research/ SPACE AI & AUTONOMY

A Comparative Evaluation of Reinforcement Learning Algorithms for Efficient Autonomous Spacecraft Landing

Utkarsh R. Moholkar

Advances in Nonlinear Variational Inequalities 2025/Mar 4, 2025

Space × AI

Technique

Learning-based MethodsOpenAlex topic: Reinforcement Learning in Robotics · Guidance, Dynamics & ControlOpenAlex topic: Guidance and Control Systems

Indexed as

OpenAlexReinforcement Learning in Robotics · Spacecraft Dynamics and Control · Guidance and Control Systems

PDF DOI Semantic Scholar OpenAlex

In the field of aerospace engineering, precision landing is crucial for ensuring the safety and success of spacecraft missions. This study compares the performance of three distinct algorithms designed for spacecraft landing scenarios, tested within a simulation environment developed in Unreal Engine. The algorithms are evaluated based on their accuracy, computational efficiency, and resource consumption. The results indicate that all three algorithms achieve successful landings with minimal resource utilization, though their performance varies under different conditions. This research provides insights into selecting the most suitable algorithm for spacecraft landing in resource-constrained environments.

A Comparative Evaluation of Reinforcement Learning Algorithms for Efficient Autonomous Spacecraft Landing

Related papers

Image-based Deep Reinforcement Learning for Autonomous Lunar Landing

Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning

Curriculum-Driven Deep Reinforcement Learning for Autonomous Far-Range Rocket Landing

Adversarial Reinforcement Learning for Robust Planetary Landing

Autonomous Six-Degree-of-Freedom Spacecraft Docking with Rotating Targets via Reinforcement Learning

A Reinforcement Learning Framework for Space Missions in Unknown Environments

Adaptive pinpoint and fuel efficient mars landing using reinforcement learning

Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning

Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls

Multiphase Autonomous Docking via Model-Based and Hierarchical Reinforcement Learning

A Comparative Evaluation of Reinforcement Learning Algorithms for Efficient Autonomous Spacecraft Landing

Related papers

Image-based Deep Reinforcement Learning for Autonomous Lunar Landing

Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning

Curriculum-Driven Deep Reinforcement Learning for Autonomous Far-Range Rocket Landing

Adversarial Reinforcement Learning for Robust Planetary Landing

Autonomous Six-Degree-of-Freedom Spacecraft Docking with Rotating Targets via Reinforcement Learning

A Reinforcement Learning Framework for Space Missions in Unknown Environments

Adaptive pinpoint and fuel efficient mars landing using reinforcement learning

Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning

Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls

Multiphase Autonomous Docking via Model-Based and Hierarchical Reinforcement Learning