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Artificial Intelligence Computing Platform Design for Underwater Localization

수중 위치측정을 위한 인공지능 컴퓨팅 플랫폼 설계

  • Received : 2021.12.30
  • Accepted : 2022.02.17
  • Published : 2022.02.28

Abstract

Successful underwater localization requires a large-scale, parallel computing environment that can be mounted on various underwater robots. Accordingly, we propose a design method for an artificial intelligence computing platform for underwater localization. The proposed platform consists of a total of four hardware modules. Transponder and hydrophone modules transmit and receive sound waves, and the FPGA module rapidly pre-processes the transmitted and received sound wave signals in parallel. Jetson module processes artificial intelligence based algorithms. We performed a sound wave transmission/reception experiment for underwater localization according to distance in an actual underwater environment. As a result, the designed platform was verified.

성공적인 수중 위치측정을 위해서는 다양한 수중 로봇에 탑재 가능한 대규모 병렬 컴퓨팅 환경이 필요하다. 이에, 본 논문에서는 수중 위치측정을 위한 인공지능 컴퓨팅 플랫폼 설계 방법을 제안한다. 제안한 플랫폼은 총 4개의 하드웨어 모듈로 구성된다. Transponder 및 hydrophone 모듈은 음파를 송수신하며 FPGA 모듈은 송수신한 음파 신호를 빠르게 병렬로 전처리한다. Jetson 모듈은 인공지능 기반 알고리즘 처리한다. 해당 플랫폼은 실제 수중 환경에서 거리에 따라 수중 위치측정을 위한 음파 송수신 실험을 수행하였으며 이를 통해 설계한 플랫폼을 검증할 수 있었다.

Keywords

Acknowledgement

이 논문은 2021학년도 조선대학교 학술연구비의 지원을 받아 연구되었음.

References

  1. R. Diamant and L. Lampe, "Underwater localization with time-synchronization and propagation speed uncertainties," IEEE Transactions on Mobile Computing, vol. 12, no. 7, 2012, pp. 1257-1269. https://doi.org/10.1109/TMC.2012.100
  2. T. Kim, N. Ko, S. Noh, and Y. Lee, "Localization on an Underwater Robot Using Monte Carlo Localization Algorithm," J. of the Korea Institute of Electronic Communication Sciences, vol. 6, no. 2, 2011, pp. 288-295. https://doi.org/10.13067/JKIECS.2011.6.2.288
  3. H. P. Tan, R. Diamant, W. K. Seah, and M. Waldmeyer, "A survey of techniques and challenges in underwater localization," Ocean Engineering, vol. 38 no. 14, 2011, pp. 1663-1676. https://doi.org/10.1016/j.oceaneng.2011.07.017
  4. M. T. Isik and O. B. Akan, "A three dimensional localization algorithm for underwater acoustic sensor networks," IEEE Transactions on Wireless Communications, vol. 8, no. 9, 2009, pp. 4457-4463. https://doi.org/10.1109/twc.2009.081628
  5. N. Saeed, A. Celik, T. Y. Al-Naffouri, and M. S. Alouini, "Underwater optical wireless communications, networking, and localization: A survey," Ad Hoc Networks, vol. 94, 2019, pp. 101935. https://doi.org/10.1016/j.adhoc.2019.101935
  6. M. D. S. Matheus, G. D. G. Giovanni, L. J. D. Paulo, and S. C. B. Silvia, "Matching color aerial images and underwater sonar images using deep learning for underwater localization," IEEE Robotics and Automation Letters, vol 5, no. 4, 2020, pp. 6365-6370. https://doi.org/10.1109/lra.2020.3013852
  7. X. Zhu, H. Dong, P. S. Rossi, M. Landro, "Self-supervised Underwater Source Localization based on Contrastive Predictive Coding," In 2021 IEEE Sensors, 2021, pp. 1-4. (online)
  8. A. Konstantaras, "Deep learning and parallel processing spatio-temporal clustering unveil new Ionian distinct seismic zone," In Informatics, vol. 7, no. 4, 2020, pp. 39. https://doi.org/10.3390/informatics7040039
  9. M. Cho, "A Study on the History, Classification and Development Direction of Artificial Intelligence," J. of the Korea Institute of Electronic Communication Sciences, vol. 16, no. 2, 2021, pp. 307-312. https://doi.org/10.13067/JKIECS.2021.16.2.307
  10. E. Buber and D. Banu, "Performance analysis and CPU vs GPU comparison for deep learning," In 2018 6th International Conference on Control Engineering and Information Technology, Istanbul, Turkey, 2018, pp. 1-6.
  11. J. Moon, J. Moon, and S. Bae, "Control for Manipulator of an Underwater Robot Using Meta Reinforcement Learning," J. of the Korea Institute of Electronic Communication Sciences, vol. 16, no. 1, 2021, pp. 95-100. https://doi.org/10.13067/JKIECS.2021.16.1.95
  12. S. Mittal, "A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform," J. of Systems Architecture, vol. 97, 2019, pp. 428-442. https://doi.org/10.1016/j.sysarc.2019.01.011