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AI and Robots Impact on Space Exploration

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Abstract

Artificial intelligence (AI) has changed how humans live on Earth. What lies beyond our planet? How can knowledge advance the work and the scope of the search? AI plays a crucial role in space exploration and travel by assisting crews and ground operations. It facilitates activities like analysing cosmic occurrences, operating machinery, charting stars and black holes, and other activities that people are unable to carry out in space. AI is used by several organisations to discover and advance life for all astronauts. Scientists and governments from all over the world have long been fascinated by space travel, because it contains the key to understanding human history and many cosmological hypotheses, including the potential of extra-terrestrial life. The region of space that we can view via a telescope is known as the visible world. However, researchers and scientists think that the cosmos may be far larger. A large part (around 96%) of the space is unused.

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References

  1. Kumar S, Tomar R (2018) The role of artificial intelligence in space exploration. In: 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 2018, pp 499–503, https://doi.org/10.1109/IC3IoT.2018.8668161

  2. Meß JG, Dannemann F, Greif F (2019) Techniques of artificial intelligence for space applications—a survey. In: European Workshop on On-Board Data Processing (OBDP2019); European Space Agency: Paris, France

  3. weiqian D, Ruiliang S, Changsheng L (2021) Research progress of terahertz satellite-ground communication. In: 2021 International Conference on Computer Communication and Artificial Intelligence (CCAI), https://doi.org/10.1109/ccai50917.2021.9447526.

  4. Artificial intelligence: powering human exploration of the moon and mars (2020) https://doi.org/10.2514/6.2020-4164.vid

  5. Fourati F, Alouini M-S (2021) Artificial intelligence for satellite communication: a review. Intell Converg Netw 2(3):213–243. https://doi.org/10.23919/icn.2021.0015

    Article  Google Scholar 

  6. Shiraz A, Cyberia J, Lozano JA (2023) Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques. Eng Appl Artif Intell 117:105523. https://doi.org/10.1016/j.engappai.2022.105523

    Article  Google Scholar 

  7. Sravani R, Sujatha P (2018) A state of art techniques on machine learning algorithms: A perspective of supervised learning approaches in data classification. In: Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 14–15 June 2018; pp 945–949

  8. Cervantes J, Garcia-Lamont F, Rodríguez-Masahisa L, Lopez A (2020) A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408:189–215. https://doi.org/10.1016/j.neucom.2019.10.118

    Article  Google Scholar 

  9. Zadeh LA, Klir GJ, Yuan B (1996) Fuzzy sets, fuzzy logic, and fuzzy systems. Adv Fuzzy Syst Appl Theory. https://doi.org/10.1142/2895

    Article  Google Scholar 

  10. Liebowitz J (ed) The handbook of applied expert systems. https://doi.org/10.1201/9780138736654.

  11. Batool K, Niazi MA (2017) Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models. Complex Adaptive Syst Model. https://doi.org/10.1186/s40294-017-0043-1

    Article  Google Scholar 

  12. Octavian Dumitru C, Schwarz G, Castel F, Lorenzo J, Datcu M (2019) Artificial intelligence data science methodology for earth observation. Adv Anal Artif Intell Appl 15:10. https://doi.org/10.5772/intechopen.86886

    Article  Google Scholar 

  13. Shi L, Teng Z, Wang L, Zhang Y, Binder A (2019) Deep clue: visual interpretation of text-based deep stock prediction. IEEE Trans Knowl Data Eng 31(6):1094–1108. https://doi.org/10.1109/tkde.2018.2854193

    Article  Google Scholar 

  14. Viros Martin A, Selva D. Explanation approaches for the Daphne virtual assistant. AIAA Scitech 2020 Forum, https://doi.org/10.2514/6.2020-2254.

  15. Linares R, Furfaro R, Reddy V (2020) Space objects classification via light-curve measurements using deep convolutional neural networks. J Astronaut Sci 67(3):1063–1091. https://doi.org/10.1007/s40295-019-00208-w

    Article  Google Scholar 

  16. Dattilo A et al (2019) Identifying exoplanets with deep learning. II. Two new super-earths uncovered by a neural network in K2 data. Astron J 157(5):169. https://doi.org/10.3847/1538-3881/ab0e12

    Article  Google Scholar 

  17. Li B, Huang J, Feng Y, Wang F, Sang J (2020) A machine learning-based approach for improved orbit predictions of LEO space debris with sparse tracking data from a single station. IEEE Trans Aerosp Electron Syst 56(6):4253–4268. https://doi.org/10.1109/taes.2020.2989067

    Article  Google Scholar 

  18. Learning transferable policies for autonomous planetary landing via deep reinforcement learning (2021) https://doi.org/10.2514/6.2021-4006.vid

  19. Rayar P, Vijaya Kumar KN (2023) Triple bottom line sustainability and industry 4.0 implementation in Indian MSMEs: a conceptual model. In: Manchuri AR, Marla D, Rao VV (eds) Intelligent manufacturing and energy sustainability. Smart innovation, systems and technologies, vol 334. Springer, Singapore. https://doi.org/10.1007/978-981-19-8497-6_39

    Chapter  Google Scholar 

  20. Singi S, Gopal S, Auti S, Chaurasia R (2020) Reinforcement learning for inventory management. In: Vasudevan H, Kottur V, Raina . (eds) Proceedings of International Conference on Intelligent Manufacturing and Automation. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4485-9_33

  21. Ranka D, Vasudevan H (2022) A model for challenges and opportunities in the implementation of digitized transforming enablers in manufacturing industries. Adv Prod Ind Eng. https://doi.org/10.3233/atde220788

    Article  Google Scholar 

  22. Bhadoriya SS, Kirkire S, Vyas R, Deshmukh S, Bandi Y (2023) S.A.R.A (Smart AI Refrigerator Assistant). In: Misra R et al. Advances in Data science and artificial intelligence. ICDSAI 2022. Springer Proceedings in Mathematics & Statistics, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-031-16178-0_35

  23. Karamchandani S, Desai UB, Merchant SN, Jindal GD (2009) Principal component analysis based backpropagation algorithm for diagnosis of peripheral arterial occlusive diseases. In: 2009 Canadian Conference on Electrical and Computer Engineering, https://doi.org/10.1109/ccece.2009.5090181

  24. Kumar Shakya H et al (2022) Internet of Things-based intelligent ontology model for safety purpose using wireless networks. Wirel Commun Mob Comput. https://doi.org/10.1155/2022/1342966

    Article  Google Scholar 

  25. Venkataramanan V, Lakshmi S (2019) Performance analysis of LTE physical layer using hardware cosimulation techniques and implementation on FPGA for communication systems. Int J Commun Syst. https://doi.org/10.1002/dac.4125

    Article  Google Scholar 

  26. Lakshmi S, Kanetkar VA, Venkataramanan V (2019) Design and implementation of LTE physical layer on FPGA. Int J Comput Appl Technol 61(1/2):127. https://doi.org/10.1504/ijcat.2019.10023600

    Article  Google Scholar 

  27. Putra AS, Warnars HLHS (2018) Intelligent traffic monitoring system (ITMS) for smart city based on IoT Monitoring. In: 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), 2018, https://doi.org/10.1109/inapr.2018.8626855.

  28. Marizel B, Salenga ML (2018) Bitter melon crop yield prediction using machine learning algorithm. Int J Adv Comput Sci Appl 9. https://doi.org/10.14569/IJACSA.2018.090301

  29. Townsend AT (2019) The role of artificial intelligence in space exploration. medium.com. https://medium.com/@apltownsend/artificial-intelligence-for-space-Exploration-703d2838e37c

  30. Harvey LH (2016) NASA’s 2001 technology. blogs.mathworks.com. https://blogs.mathworks.com/headlines/2016/09/15/tbt-nasas-2001-ttechnology/

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Authors and Affiliations

  1. Dwarkadas J. Sanghvi College of Engineering, JVPD Scheme, Vile Parle West, Mumbai, 56, India

    V. Venkataramanan, Aashi Modi & Kashish Mistry

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  1. V. Venkataramanan
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  2. Aashi Modi
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All authors have contributed equally to this work. All authors read and approved the final manuscript.

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Correspondence to V. Venkataramanan or Aashi Modi.

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Venkataramanan, V., Modi, A. & Mistry, K. AI and Robots Impact on Space Exploration. Adv. Astronaut. Sci. Technol. (2024). https://doi.org/10.1007/s42423-023-00147-7

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