Underwater Target Tracking Control of an Untethered Robotic Fish with a Camera Stabilizer

Author(s):  
Junzhi Yu ◽  
Xingyu Chen ◽  
Shihan Kong
Author(s):  
Songlin Chen ◽  
Jianxun Wang ◽  
Xiaobo Tan

In this paper we apply backstepping technique to develop a novel hybrid target-tracking control scheme for a carangiform robotic fish, based on a dynamic model that combines rigid-body dynamics with Lighthill’s large-amplitude elongated-body theory. This hybrid controller consists of an open-loop turning controller and a closed-loop approaching controller. A hysteretic switching strategy based on the orientation error is designed. Using Lyapunov analysis, we show that the trajectory of the robotic fish will converge to the target point. The effectiveness of the proposed control strategy is demonstrated through both simulations and experiments.


2016 ◽  
Vol 63 (1) ◽  
pp. 355-363 ◽  
Author(s):  
Junzhi Yu ◽  
Feihu Sun ◽  
De Xu ◽  
Min Tan

2011 ◽  
Vol 317-319 ◽  
pp. 890-896
Author(s):  
Ming Jun Zhang ◽  
Yuan Yuan Wan ◽  
Zhen Zhong Chu

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.


2018 ◽  
Vol 32 (2) ◽  
pp. 206-215
Author(s):  
Dong-dong Li ◽  
Yang Lin ◽  
Yao Zhang

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