Towards the Design and Implementation of an Image-Based Navigation System of an Autonomous Underwater Vehicle Combining a Color Recognition Technique and a Fuzzy Logic Controller

Abstract

This study proposes the development of an underwater object-tracking control system through an image-processing technique. It is used for the close-range recognition and dynamic tracking of autonomous underwater vehicles (AUVs) with an auxiliary light source for image processing. The image-processing technique includes color space conversion, target and background separation with binarization, noise removal with image filters, and image morphology. The image-recognition results become more complete through the aforementioned process. After the image information is obtained for the underwater object, the image area and coordinates are further adopted as the input values of the fuzzy logic controller (FLC) to calculate the rudder angle of the servomotor, and the propeller revolution speed is defined using the image information. The aforementioned experiments were all conducted in a stability water tank. Subsequently, the FLC was combined with an extended Kalman filter (EKF) for further dynamic experiments in a towing tank. Specifically, the EKF predicts new coordinates according to the original coordinates of an object to resolve data insufficiency. Consequently, several tests with moving speeds from 0.2 m/s to 0.8 m/s were analyzed to observe the changes in the rudder angles and the sensitivity of the propeller revolution speed.

Review of underwater visual navigation and docking: advances and challenges

“…However, recognizing passive beacons necessitates relatively clear water conditions. Meanwhile, National Cheng-Kung University [33] has developed an AUV navigation system based on image recognition and fuzzy logic control. The research team successfully identified and tracked an underwater target, a red sphere with a diameter of 24 centimeters.…”

Probabilistic Framework Allocation on Underwater Vehicular Systems Using Hydrophone Sensor Networks

“…However, such a created method can only measure the state of water, and the monitored data cannot be transferred to the central station in a timely manner. The data are then processed using an image processing technique [5] with the same construction design in the next stage. Additionally, colour identification algorithms for various trawls and obstacles have been integrated with a monitoring system that offers clear information for appropriate signal control.…”

CPG-MPC controller for wheel-fin-flipper integrated amphibious robot

“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0143-991X.htm Industrial Robot: the international journal of robotics research and application 50/6 (2023) 900-916 Emerald Publishing Limited [ISSN 0143-991X] [DOI 10.1108/IR-01-2023-0008] and overshoot for the underactuated system (Youssef et al, 2022). At present, the methods used for the overall control of robots mainly include model predictive control (MPC) (Camacho and Alba, 2013), neural network (Yang et al, 2018) and robust adaptive control (Lin et al, 2021). The underwater motion of amphibious robots is often accompanied by severe response lag, and MPC can react in advance to the upcoming abrupt change.…”

“…In the overall motion control of the robot, the traditional model-free feedback control method performs well in the high degree of freedom system, but there is a serious response lag and overshoot for the underactuated system (Youssef et al , 2022). At present, the methods used for the overall control of robots mainly include model predictive control (MPC) (Camacho and Alba, 2013), neural network (Yang et al , 2018) and robust adaptive control (Lin et al , 2021). The underwater motion of amphibious robots is often accompanied by severe response lag, and MPC can react in advance to the upcoming abrupt change.…”