Journal of Machine and Computing

Advancements and Challenges in Underwater Soft Robotics: Materials, Control and Integration

Journal of Machine and Computing

Received On : 10 October 2023

Revised On : 10 December 2023

Accepted On : 20 March 2024

Published On : 05 April 2024

Volume 04, Issue 02

Pages : 512-520


This article focuses on the progress of underwater robots and the importance of software architectures in building robust and autonomous systems. The researchers underscore the challenges linked to implementation and stress the need for comprehensive validation of both reliability and efficacy. Their argument is on the extensive implementation of a globally applicable architectural framework that complies with established standards and guarantees interoperability within the field of robotics. The research also covers advancements in underwater soft robotics, which include the development of models, materials, sensors, control systems, power storage, and actuation techniques. This article explores the challenges and potential applications of underwater soft robotics, highlighting the need of collaboration across many fields and advancements in mechanical design and control methods. In the last section of the paper, the control approach and algorithms used to underwater exploration robots are reviewed. Particular attention is given to the application of Proportional Integral Derivative (PID) control and the incorporation of Backpropagation Neural Network (BPNN) for PID parameter determination.


Backpropagation Neural Network, Proportional Integral Derivative, Microcontroller Unit, Gradient Descent, Real-Time Operating System

  1. L. L. Whitcomb, “Underwater robotics: out of the research laboratory and into the field,” Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), Nov. 2002, doi: 10.1109/robot.2000.844135.
  2. D. Lee, G. Kim, D.-H. Kim, H. Myung, and H. Choi, “Vision-based object detection and tracking for autonomous navigation of underwater robots,” Ocean Engineering, vol. 48, pp. 59–68, Jul. 2012, doi: 10.1016/j.oceaneng.2012.04.006.
  3. S. B. Williams, O. Pizarro, D. Steinberg, A. Friedman, and M. Bryson, “Reflections on a decade of autonomous underwater vehicles operations for marine survey at the Australian Centre for Field Robotics,” Annual Reviews in Control, vol. 42, pp. 158–165, Jan. 2016, doi: 10.1016/j.arcontrol.2016.09.010.
  4. M. Calisti, M. Cianchetti, M. Manti, F. Corucci, and C. Laschi, “Contest-Driven Soft-Robotics Boost: The RoboSoft Grand Challenge,” Frontiers in Robotics and AI, vol. 3, Sep. 2016, doi: 10.3389/frobt.2016.00055.
  5. Q.-Q. Hu and Y. Yu, “The hydrodynamic effects of undulating patterns on propulsion and braking performances of long-based fin,” AIP Advances, vol. 12, no. 3, Mar. 2022, doi: 10.1063/5.0083912.
  6. S. J. Nawaz, M. Hussain, S. Watson, N. Trigoni, and P. N. Green, “An underwater robotic network for monitoring nuclear waste storage pools,” in Springer eBooks, 2010, pp. 236–255. doi: 10.1007/978-3-642-11528-8_17.
  7. R. A. S. I. Subad, L. B. Cross, and K. Park, “Soft robotic hands and tactile sensors for underwater robotics,” Applied Mechanics, vol. 2, no. 2, pp. 356–383, Jun. 2021, doi: 10.3390/applmech2020021.
  8. MajidiCarmel, “Soft Robotics: A Perspective—Current Trends and Prospects for the Future,” Soft Robotics, vol. 1, no. 1, pp. 5–11, Mar. 2014, doi: 10.1089/soro.2013.0001.
  9. Y. Sun et al., “Soft Mobile Robots: a Review of Soft Robotic Locomotion Modes,” Current Robotics Reports, vol. 2, no. 4, pp. 371–397, Dec. 2021, doi: 10.1007/s43154-021-00070-5.
  10. D. Scaradozzi, G. Palmieri, D. Costa, and A. Pinelli, “BCF swimming locomotion for autonomous underwater robots: a review and a novel solution to improve control and efficiency,” Ocean Engineering, vol. 130, pp. 437–453, Jan. 2017, doi: 10.1016/j.oceaneng.2016.11.055.
  11. G. L. Amy et al., “Membrane-based seawater desalination: Present and future prospects,” Desalination, vol. 401, pp. 16–21, Jan. 2017, doi: 10.1016/j.desal.2016.10.002.
  12. G. S, D. T, and A. Haldorai, “A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing,” Defence Science Journal, vol. 72, no. 5, pp. 712–720, Nov. 2022, doi: 10.14429/dsj.72.17533.
  13. R. Subha, A. Haldorai, and A. Ramu, “An Optimal Approach to Enhance Context Aware Description Administration Service for Cloud Robots in a Deep Learning Environment,” Wireless Personal Communications, vol. 117, no. 4, pp. 3343–3358, Feb. 2021, doi: 10.1007/s11277-021-08073-3.
  14. W. Li, “Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller,” IEEE Transactions on Fuzzy Systems, vol. 6, no. 4, pp. 449–463, Jan. 1998, doi: 10.1109/91.728430.
  15. Y. Nishikawa, N. Sannomiya, T. Ohta, and H. Tanaka, “A method for auto-tuning of PID control parameters,” Automatica, vol. 20, no. 3, pp. 321–332, May 1984, doi: 10.1016/0005-1098(84)90047-5.


The author(s) received no financial support for the research, authorship, and/or publication of this article.


No funding was received to assist with the preparation of this manuscript.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Availability of data and materials

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Author information


All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.

Corresponding author

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit

Cite this article

Lekhaa T R, Saravanakumar K, Akshaya V S and Aravindhan K, “Advancements and Challenges in Underwater Soft Robotics: Materials, Control and Integration", pp. 512-520, April 2024. doi: 10.53759/7669/jmc202404049.


© 2024 Lekhaa T R, Saravanakumar K, Akshaya V S and Aravindhan K. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.