Distributed coordinated tracking control of multiple unmanned surface vehicles under complex marine environments

Liang, Xiao; Qu, Xingru; Hou, Yuanhang; Li, Ye; Zhang, Rubo

doi:10.1016/j.oceaneng.2020.107328

Cited by 82 publications

(27 citation statements)

References 31 publications

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“…20 The experiment program is developed with Cþþ based on Visual Studio 2017 and the OpenCV 3.4.6 computer vision function library was used. 21 As Figure 4 shows, the program running result of a sailing USV video well reflects the space topology structure of the SOFM algorithm. As described in the third section, the testing picture is divided into 3 Â 3 slices, and they are classified according to the features p1, p2, and p3.…”

Section: Experiments Design and Results Analysismentioning

confidence: 72%

Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method

Xiang

Tao

Song

et al. 2020

International Journal of Advanced Robotic Systems

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The unmanned surface vehicle has the characteristics of high maneuverability and flexibility. Object detection and tracking skills are required to improve the ability of unmanned surface vehicle to avoid collisions and detect targets on the surface of the water. Mean-shift algorithm is a classic target tracking algorithm, but it may fail when pixel interference and occlusion occur. This article proposes a tracking algorithm for unmanned surface vehicle based on an improved mean-shift optimization algorithm. The method uses the self-organizing feature map spatial topology to reduce the interference of the background pixels on the target object and predicts the center position of the object when the target is heavily occluded according to the extended Kalman filter. First, a self-organizing feature map model is built to classify pixels in a rectangular frame and the background pixels are extracted. Then, the method optimizes the extended Kalman filter solution process to complete the prediction and correction of the target center position and introduces a similarity function to determine the target occlusion. Finally, numerical analyses based on a ship model sailing experiment are performed with the help of OpenCV library. The experimental results validated that the proposed method significantly reduces the cumulative error in the tracking process and effectively predicts the position of the target between continuous frames when temporary occlusion occurs. The research can be used for target detection and autonomous navigation of unmanned surface vehicle.

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Section: Experiments Design and Results Analysismentioning

confidence: 72%

Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method

Xiang

Tao

Song

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…1 The underactuated nature of these problems, together with the matter of uncertain parameters, external disturbances as well as input constraints, renders the control problem both challenging and interesting. 2,3 Some techniques have been proposed for the control design of path following. The line of sight (LOS) guidance was also widely used in path following.…”

Section: Introductionmentioning

confidence: 99%

A novel active disturbance rejection control with hyperbolic tangent function for path following of underactuated marine surface ships

Jia

Fan

et al. 2020

Measurement and Control

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This paper presents a TADRC method via active disturbance rejection control (ADRC) with hyperbolic tangent function for path following of underactuated surface ships with input constraint, heading rate constraint, parameters uncertainties, as well as environment disturbances. The line of sight (LOS) guidance scheme that computes the desired heading angle on basis of cross tracking error and a look ahead distance, converts path following into heading control, and also renders good helmsman behavior. Moreover, hyperbolic tangent function is introduced to modify the linear extended state observer (LESO) to design a nonlinear observer (TESO) for promoting the estimation performance of the heading, heading rate and total disturbances including parameters uncertainties and environmental disturbances. Then, the linear error feedback control is modified by a nonlinear sliding mode control scheme with hyperbolic tangent function to handle the heading rate constraint and to obtain the better control action. Furthermore, the feedback control law is embedded in a standard Quadratic Programming (QP) cost function to handle the input constraints including rudder saturation and rudder rate limit. Finally, the comparison simulation demonstrates the effectiveness of the proposed method for the underactuated ship’s path following.

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“…The underactuated nature of these problems, namely with more variables to be controlled than the number of control actuators, coupled with the matter of uncertain parameters, external disturbances as well as input constraint, renders the control problem both challenging and interesting. 2,3 The path following control problem of underactuated ships has been tackled in a number of research studies in the last few years. For example, a position prediction model was presented by Nagai and Watanabe, 4 which estimated positions by the integral in continuous time and predicted future positions by considering only constant velocities.…”

Section: Introductionmentioning

confidence: 99%

Path following of underactuated surface ships based on model predictive control with neural network

Huang

Pan

et al. 2020

International Journal of Advanced Robotic Systems

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A model predictive control approach is proposed for path following of underactuated surface ships with input saturation, parameters uncertainties, and environmental disturbances. An Euler iterative algorithm is used to reduce the calculation amount of model predictive control. The matter of input saturation is addressed naturally and flexibly by taking advantage of model predictive control. The mathematical model group (MMG) model as the internal model improves the control accuracy. A radial basis function neural network is also applied to compensate the total unknowns including parameters uncertainties and environmental disturbances. The numerical simulation results show that the designed controller can force an underactuated ship to follow the desired path accurately in the case of input saturation and time-varying environmental disturbances including wind, current, and wave.

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Distributed coordinated tracking control of multiple unmanned surface vehicles under complex marine environments

Cited by 82 publications

References 31 publications

Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method

Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method

A novel active disturbance rejection control with hyperbolic tangent function for path following of underactuated marine surface ships

Path following of underactuated surface ships based on model predictive control with neural network

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