ROV dynamic modeling and grasping algorithm for underwater control system of marine oil and gas

Deepwater oil exhibits great difficulty and risk in extraction under complex marine environments and strong ocean currents, and the operation and maintenance of its underwater control system rely heavily on remote operation. Traditional control methods make it hard to satisfy the demands of extraction. Therefore, the study first proposes simulating the marine working environment using virtual technology and model the Remote Operated Vehicle (ROV) to optimize power allocation. Secondly, the robot grasping task is achieved by designing binocular vision stereo image matching and improving the Proximal Policy Optimization grasping algorithm to enhance its stability and operational success rate under turbulent disturbances. Finally, an underwater production simulation system is built to provide a virtualization platform for oil and gas development operations. The results show that ROV can effectively achieve propeller power distribution, and the amplitude error under different operating conditions is reduced by an average of about 25% compared to the traditional Saab Seaeye model, with a maximum of no more than 5%. The simulation effect is significant. And the robot grasping system designed by the research institute can autonomously complete tasks. After training, the positioning error is reduced by 41.6% and 74.7% compared to the Mask Region based Convolutional Neural Network (Mask R-CNN) algorithm and You Only Look Once algorithm, respectively, reaching a final height of 0.11 m. The grasping success rate is still better than the comparative algorithm by more than 10% and more than 90% in strong flow environments, and it takes less time to improve overall performance significantly. The research method can provide a guarantee for the safety of deepwater operations in oil and gas fields, and reduce operation and maintenance costs and mining difficulties.