Switching Reference Image-Based Visual Servoing Forunderwater Docking Of Autonomous Underwater Vehicle - Yahya, Mohd Faid (2019)
Autonomous underwater vehicle (AUV) has great potential to dive deep under the ocean and perform various tasks. However, the AUV operates on a limited amount of battery capacity. To overcome this limitation, underwater docking is required so that when the AUV docks, it is able to recharge the battery to full capacity. One of the ways to achieve underwater docking is by means of vision-based robot control or visual servoing. There are many methods to perform visual servoing such as positionbased visual servoing (PBVS), image-based visual servoing (IBVS), and 2-½-D visual servoing. Nevertheless, these methods failed when there is no resemblance of target features between acquired and desired images. Such problem arises when the target features from acquired image could be out of image plane or disoriented due to AUV’s skewed position or appeared to be disfigured due to harsh underwater conditions. To resolve this problem, a switching reference IBVS method is proposed in this study. To realize the proposed method, a control system based on Proportional-Integral and Sliding-Mode controllers are developed to control the AUV movement. Then, vision system is developed based on deep learning to detect and classify targets installed on the docking station. Subsequently, the switching reference IBVS method is developed for guiding the AUV into the docking station. The underlying concept of the proposed method is to switch the desired target features to match the currently acquired target features. The method also enables the AUV to switch between two modes of operation which are homing and docking. In addition, an AUV and a docking station prototypes have been developed to verify simulation results. Simulation wise, the developed control system has responsiveness to track desired trajectory by 93.89% and robust under the effect of lateral water current up to 0.1 meter per second. As for the developed vision system, the detection and classification accuracies of targets based on confusion matrix are 96.68% and 99.72% respectively. Then, for switching reference IBVS, when benchmarked, the proposed method excelled in reliability to avoid collision between AUV and docking station by 83.33% and more robust under the effect of missing target features when compared to normal IBVS method and IBVS with attitude keeping control method by 100%. Finally, from experimental result, the number of successful trials for underwater docking using the proposed method is 20 out of 24 or 83.33%.