enhancing spacecraft autonomy with transfer learning and generative ai
- Paper ID
92567
- author
- company
R V College of Engineering, Bengaluru
- country
India
- year
2025
- abstract
As space exploration advances, the integration of artificial intelligence (AI) is essential for improving mission efficiency and autonomy. This research paper investigates the synergistic application of transfer learning and generative AI to enhance spacecraft performance in complex extraterrestrial environments with minimal human intervention. Through transfer learning, AI systems leverage insights from previous missions, allowing them to recognize patterns and risks, which in turn enhances adaptive decision-making for navigation and obstacle avoidance. Generative AI contributes by simulating diverse environmental scenarios, providing extensive training that bolsters adaptability in unpredictable terrains. To evaluate the effectiveness of these technologies, this study conducts comparative analyses of AI models trained on various NASA missions and meteor exploration efforts. Key performance metrics—including efficiency, accuracy, and adaptability—will be assessed to determine the most suitable approaches for different mission types. This research aims to elucidate the benefits and limitations of AI integration, ultimately highlighting how autonomous systems can facilitate safer and more efficient space missions as humanity expands its reach into the cosmos.