Autonomous Subsea Robotic System for inspection and maintenance of strategic Energy assets

Summary

The focus of the research here is on development of a subsea robotic system that has enhanced intelligence and autonomous capabilities to support inspection and maintenance of strategic subsea infrastructure such as subsea pipelines, subsea installations (turbines etc), subsea communication cables etc. At the core of robotic inspection and maintenance are the tasks of detection of subsea object/area of interest and performance of complex intelligent manipulation tasks in operational space. There are various challenges in autonomous operations of robotic systems specially in extreme environment of the subsea for precision navigation, sensing, perception, control and manipulation. In the proposed project novel multimodal sensors using depth cameras [1] and SONAR will be explored for navigation, sensing and perception and a custom subsea manipulator will be designed in-house to perform complex manipulation tasks for inspection and maintenance. The proposed project will explore AI/ML [3] based novel algorithms to detect and estimate region/object of interest and depth to grasp and perform complex manipulation through nonlinear operational space control [2] and reinforcement learning techniques. The proposed project will be executed through the BlueROV2 system with SONAR and subsea cameras, motors and drive systems which already exists in the Robotics Lab at Robert Gordon University.

References

1. R. Muthukrishnan, S. Kannan, R. Prabhu, Y. Zhao, P. Bhowmick and M. J. Hasan, "Tracking and Estimation of Surgical Instrument Position and Angle in Surgical Robot Using Vision System," 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Bengaluru, India, 2023, pp. 1-6, doi: 10.1109/NMITCON58196.2023.10275983.
2. Quintanar-Guzmán S, Kannan S, Aguilera-González A, Olivares-Mendez MA, Voos H. Operational space control of a lightweight robotic arm actuated by shape memory alloy wires: A comparative study. Journal of Intelligent Material Systems and Structures. 2019;30(9):1368-1384. doi:10.1177/1045389X17721050
3. Stefan Ainetter, Christoph Böhm, Rohit Dhakate, Stephan Weiss, Friedrich Fraundorfer, Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks, https://doi.org/10.48550/arXiv.2111.11114

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