Visual Servoing for Underwater Vehicle Using Dual-Eyes Evolutionary Real-Time Pose Tracking

Abstract

[abstFig src='/00280004/12.jpg' width='300' text='ROV with dual-eyes cameras and 3D marker' ] Recently, a number of researches related to underwater vehicle has been conducted worldwide with the huge demand in different applications. In this paper, we propose visual servoing for underwater vehicle using dual-eyes cameras. A new method of pose estimation scheme that is based on 3D model-based recognition is proposed for real-time pose tracking to be applied in Autonomous Underwater Vehicle (AUV). In this method, we use 3D marker as a passive target that is simple but enough rich of information. 1-step Genetic Algorithm (GA) is utilized in searching process of pose in term of optimization, because of its effectiveness, simplicity and promising performance of recursive evaluation, for real-time pose tracking performance. The proposed system is implemented as software implementation and Remotely Operated Vehicle (ROV) is used as a test-bed. In simulated experiment, the ROV recognizes the target, estimates the relative pose of vehicle with respect to the target and controls the vehicle to be regulated in desired pose. PID control concept is adapted for proper regulation function. Finally, the robustness of the proposed system is verified in the case when there is physical disturbance and in the case when the target object is partially occluded. Experiments are conducted in indoor pool. Experimental results show recognition accuracy and regulating performance with errors kept in centimeter level.

Real-time pose tracking of 3D targets by photo-model-based stereo-vision

“…Using Lyapunov analysis in (Song et al, 2010), the time convergence performance of the RM-GA in successively input dynamic images has been confirmed experimentally. Real-time 3D pose estimation using 3D-model-based recognition and the RM-GA has been presented in detail in a previous paper (Myint et al, 2016). Real-time pose estimation using RM-GA and docking performance of ROV is discussed in an earlier study (Myint et al, 2017).…”

Adaptive neural network control for visual docking of an autonomous underwater vehicle using command filtered backstepping

“…Ghosh et al 15 employed ellipse parameters to estimate the relative pose of a circular docking station, then control the underwater vehicle to move into the station. Mamoru et al 16 used 3D markers as a passive target, and a genetic algorithm‐based optimization method was utilized in the pose estimation process, then PID control is used. Yan et al 17 proposed a visual positioning algorithm based on the L‐shaped light array installed at the bottom of the docking device.…”

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

“…Machine vision processing technology mainly employs cameras and computers to analyze images and provide control information of the AUV drive system. There have been numerous research studies on the application of an AUV equipped with machine vision processing technology, such as the detection of underwater man-made structures and pipeline detection [3][4][5][6][7][8][9][10], auxiliary sonar image navigation [11,12], simultaneous localization and mapping (SLAM) [13][14][15][16][17], obstacle avoidance [18,19], identifying and tracking the habitats of sea animals [20], underwater docking systems [21][22][23][24][25][26][27], and object tracking [28][29][30][31][32][33][34][35][36].…”