Analysis of Intelligent Path Planning and Control of UW-ROVs Using a Hybrid SSO-PID Technique
Surya Prakash Mishra, Dayal Ramakrushna Parhi, Aswini Kumar Sahoo, Abhijit Mahapatro · Marine Geodesy · 2026
Modernized underwater vehicles require the implementation of efficient path-planning algorithms to move through challenging environments, especially regions with strong and persistent currents, where conventional AI-based methods are not feasible. The present work proposes a hybrid control system combining Shark Smell Optimization (SSO) with a standard control system to achieve greater navigational efficiency and overall performance of an underwater remotely operated vehicle (UW-ROV). Inspired by a shark’s natural ability to follow scent traces and track them, the SSO algorithm computationally mimics such behavior, thus enabling precise obstacle avoidance and real-time path correction. The SSO approach allowed UW-ROV to optimize its agility, accuracy, and adaptability based on real-time sensor inputs, and demonstrated significant robustness against thruster-induced water current disturbances in the trials examined. Under experimental pool trials, the UW-ROV followed predefined paths with precise accuracy and minimal deviation. Average differences in navigation paths and times between experiments and simulations were a mere 5.25% and 5.77%, respectively. Moreover, the proposed method achieved a 4.56% improvement in accuracy over previous methods. These results validate the robustness and real-time response of the system, which is a noteworthy research advancement in underwater robotics, especially autonomous operation in challenging and dynamic aquatic environments.