Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Advances in Nonlinear Variational Inequalities

DOI: 10.52783/anvi.v28.4126

Author: Utkarsh R. Moholkar

Institutions: Smt. Kashibai Navale Medical College and General hospital, Savitribai Phule Pune University

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4409083279

FWCI:

Cites:

Cited by:

Related to: 10