Advancing Space Exploration with AI and Robotics: The Future of Autonomous Missions

Authors

  • Daniel Parker

Abstract

Artificial intelligence (AI) and robotics are playing an increasingly vital role in space exploration, enabling autonomous navigation, data analysis, and decision-making for interplanetary missions. This paper examines how AI-powered rovers, robotic arms, and autonomous spacecraft enhance the efficiency of space missions. Case studies from China’s Chang’e lunar program and NASA’s Mars rovers highlight the success of AI in extraterrestrial exploration. The research discusses challenges such as deep-space communication delays, energy efficiency, and AI reliability in extreme environments. The findings offer insights into the future of self-sustaining AI-driven space missions and human-robot collaboration beyond Earth.

References

Guttha, P. R. (2023). Architecting Scalable Business Applications: Design, Development, and Delivery Strategies. Australian Journal of Cross-Disciplinary Innovation, 5(5).

Mahida, A. (2023). An Automated Disaster Recovery Strategies for Fintech Infrastructure. Journal of Engineering and Applied Sciences Technology. SRC/JEAST-342. DOI: doi. org/10.47363/JEAST/2023 (5), 236, 2-4.

Mahida, A. (2022). Comprehensive Review on Optimizing Resource Allocation in Cloud Computing for Cost Efficiency. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-249. DOI: doi. org/10.47363/JAICC/2022 (1), 232, 2-4.

Mahida, A. (2023). Machine Learning for Predictive Observability-A Study Paper. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-252. DOI: doi. org/10.47363/JAICC/2023 (2), 235, 2-3.

Mahida, A. (2021). A Review on Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning. International journal of science and research, 10(3), 1967-1970.

Mahida, A. (2023). Enhancing Observability in Distributed Systems-A Comprehensive Review. Journal Of Mathematical & Computer Applications. Src/Jmca-166. Doi: Doi. Org/10.47363/Jmca/2023 (2), 135, 2-4.

Mahida, A. (2023). Explainable Generative Models in FinCrime. J Artif Intell Mach Learn & Data Sci, 1(2), 205-208.

Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).

Mohammed, C. (2021). Revolutionizing Financial Operations: A Comprehensive Study on the Impact of SAP and Kyriba Integration. International Journal of Sustainable Development in Computing Science, 3(2), 1-19.

Sudhakar, V. M. (2022). Advancements in Automl: Designing Scalable Solutions for Enterprise Data Science Platforms.

Sudhakar, V. M. (2020). Optimizing Supply Chain Management in Oil and Gas with Machine Learning: A Data-Driven Approach for Cost Reduction and Efficiency.

Published

2023-11-14

Issue

Section

Articles