Development of Social Spider Optimization Algorithm with PID Controller for Enhanced Performance in Underwater ROVs

Abstract

Advances in intelligent control technologies and remote sensing have opened up a whole new world of underwater autonomy. A number of research issues must first be investigated in simulation and experimental platforms to build competent remotely operated underwater robots for subsea environments. Underwater (Remotely Operated Vehicles) ROVs can improve their exploration and navigation efficiency by applying the Social Spider Optimization Algorithm (SSOA). Through the utilization of social spiders' adaptive and cooperative behaviour, SSOA algorithm has the ability to enhance path planning, hence improving the ROV's ability to explore intricate underwater terrain. The potential for advancing autonomous undersea exploration through resource-efficient operations and intelligent decision-making is present when Social Spider Optimization is integrated into ROV systems. Compared to previous optimization algorithms, Social Spider Optimization has a unique combination of advantages that make it a potential option for improving the autonomy and performance of underwater ROVs. As the Social Spider Optimization Algorithm is decentralized and resembles the cooperative behaviour of social spiders, it is being used in this work to provide a more effective method for real-time path planning and obstacle avoidance. The PID controller will be built to find the optimal path for the underwater vehicles using the SSOA technique. Several results will be presented on the simulation and experimental platforms using the integrated SSOA-PID controller