Study on a New Nonlinear H∞ Guidance Law for Autonomous Underwater Vehicle

2011 ◽  
Vol 219-220 ◽  
pp. 362-365
Author(s):  
Hao Ding ◽  
Kui Ping Liu ◽  
Wen Li You
Keyword(s):  
Reference Frame ◽  
Relative Motion ◽  
High Performance ◽  
Autonomous Underwater Vehicle ◽  
Normal Load ◽  
Underwater Vehicle ◽  
Target Line ◽  
Line Of Sight ◽  
Guidance Law ◽  
Target Pursuit

The key for Autonomous Underwater Vehicle (AUV) to implement target pursuit is to design high performance guidance law. The globose reference frame is adopted, and equations for 3-D relative motion between AUV and the target are built. Then the H∞ theory is used, and a new AUV nonlinear H∞ guidance law is obtained by solving Hamilton-Jacobi inequation. Simulation is taken on to verify the performance of H∞ guidance law. And the results show that the H∞ guidance law can help AUV overtake the target in less time, and the azimuth and pitching angle of the target line of sight are always staying at the initial numerical values. Furthermore, the normal load decreases to zero gradually. So the guidance law is effective for AUV to pursuit target.

Intelligent Steering Control of an Autonomous Underwater Vehicle

2000 ◽  
Vol 53 (3) ◽  
pp. 511-525 ◽  
Author(s):  
R. Sutton ◽  
R. S. Burns ◽  
P. J. Craven
Keyword(s):  
Control Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Fuzzy Inference ◽  
Underwater Vehicle ◽  
Guidance System ◽  
Line Of Sight ◽  
Steering Control ◽  
Inference System ◽  
Sea Current ◽  
Proportional Derivative Controller

This paper considers the development of three autopilots for controlling the yaw responses of an autonomous underwater vehicle model. The autopilot designs are based on the adaptive network-based fuzzy inference system (ANFIS), a simulated, annealing-tuned control algorithm and a traditional proportional-derivative controller. In addition, each autopilot is integrated with a line-of-sight (LOS) guidance system to test its effectiveness in steering round a series of waypoints with and without the presence of sea current disturbance. Simulation results are presented that show the overall superiority of the ANFIS approach.

Drift angle compensation-based adaptive line-of-sight path following for autonomous underwater vehicle

2019 ◽  
Vol 93 ◽  
pp. 101943 ◽  
Author(s):  
Fengxu Liu ◽  
Yue Shen ◽  
Bo He ◽  
Dianrui Wang ◽  
Junhe Wan ◽  
...  
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Path Following ◽  
Underwater Vehicle ◽  
Line Of Sight ◽  
Drift Angle

Transient Analysis of Hydrodynamic Interaction Effects on an Autonomous Underwater Vehicle in Proximity of a Moving Submarine

2015 ◽  
Vol 157 (A4) ◽  
pp. 205-218
Keyword(s):  
Relative Motion ◽  
Transient Analysis ◽  
Hydrodynamic Interaction ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Cfd Simulations ◽  
Close Proximity ◽  
Computational Fluid Dynamics Cfd ◽  
Submarine Hull ◽  
Hull Forms

"When an Autonomous Underwater Vehicle (AUV) is operating close to a moving submarine, the hydrodynamic interaction between the two vehicles can prevent the AUV from maintaining its desired trajectory. This can lead to mission failure and, in extreme cases, collision with the submarine. This paper outlines the transient interaction influence on the hydrodynamic coefficients of an AUV operating in close proximity and in relative motion to a larger moving submarine. The effects of relative motion on the interaction behaviour were investigated via two manoeuvres, i.e. the AUV overtaking and being overtaken by the submarine at different relative forward velocities and lateral distances. The results presented are from a series of Computational Fluid Dynamics (CFD) simulations on axisymmetric AUV and submarine hull forms, with validation of the CFD model carried out through scaled captive model experiments. The results showed that an AUV becomes less susceptible to the interaction influence when overtaking at speeds higher than the submarine. The implications of the interaction influence on the AUV’s ability to safely manoeuvre around the submarine are also discussed."

Landing Motion Simulation of Autonomous Underwater Lurk Vehicle

2011 ◽  
Vol 366 ◽  
pp. 444-446
Author(s):  
Peng Zhang ◽  
Bao Wei Song ◽  
Xiao Xu Du
Keyword(s):  
Rigid Body ◽  
High Performance ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Motion Simulation ◽  
Great Contribution ◽  
Military Application ◽  
New Type ◽  
Simulation Results

Autonomous underwater lurk vehicle is a new type of autonomous underwater vehicle, and the advantages of autonomous underwater lurk vehicle are intelligent, long-term working, covert, high-performance and recyclables, etc. It leads great contribution to the civilian and military application. In this paper, the frames were built first. Then, based on rigid body momentum and moment of momentum theorem, the dynamics equation of autonomous underwater lurk vehicle was built. At last, the landing motion of the autonomous underwater lurk vehicle was simulated. The simulation results show preliminarily that the autonomous underwater lurk vehicle can steady complete the required landing motion.

scholarly journals MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV

2020 ◽  
Vol 10 (9) ◽  
pp. 2991 ◽  
Author(s):  
André Bianchi Figueiredo ◽  
Aníbal Coimbra Matos
Keyword(s):  
High Performance ◽  
Degrees Of Freedom ◽  
Autonomous Underwater Vehicle ◽  
Ground Truth ◽  
Complete Characterization ◽  
Point Of View ◽  
Monocular Vision ◽  
Experimental Point ◽  
Guidance Law ◽  
Visual Markers

This paper presents a high performance (low computationally demanding) monocular vision-based system for a hovering Autonomous Underwater Vehicle (AUV) in the context of autonomous docking process-MViDO system: Monocular Vision-based Docking Operation aid. The MViDO consists of three sub-modules: a pose estimator, a tracker and a guidance sub-module. The system is based on a single camera and a three spherical color markers target that signal the docking station. The MViDO system allows the pose estimation of the three color markers even in situations of temporary occlusions, being also a system that rejects outliers and false detections. This paper also describes the design and implementation of the MViDO guidance module for the docking manoeuvres. We address the problem of driving the AUV to a docking station with the help of the visual markers detected by the on-board camera, and show that by adequately choosing the references for the linear degrees of freedom of the AUV, the AUV is conducted to the dock while keeping those markers in the field of view of the on-board camera. The main concepts behind the MViDO are provided and a complete characterization of the developed system is presented from the formal and experimental point of view. To test and evaluate the MViDO detector and pose an estimator module, we created a ground truth setup. To test and evaluate the tracker module we used the MARES AUV and the designed target in a four-meter tank. The performance of the proposed guidance law was tested on simulink/Matlab.

scholarly journals Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 795 ◽  
Author(s):  
Xuliang Yao ◽  
Xiaowei Wang ◽  
Feng Wang ◽  
Le Zhang
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicle ◽  
Control Method ◽  
Path Following ◽  
Underwater Vehicle ◽  
System Stability ◽  
Guidance Law ◽  
Angular Velocities ◽  
Avoidance Control

This paper studies three-dimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Line-of-sight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the real-time obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUV’s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes real-time obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.

A convolutional neural network accelerator for real-time underwater image recognition of autonomous underwater vehicle

2020 ◽  
pp. 095965182095820
Author(s):  
Wanting Zhao ◽  
Hong Qi ◽  
Yu Jiang ◽  
Chong Wang ◽  
Fenglin Wei
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
High Performance ◽  
Autonomous Underwater Vehicle ◽  
Random Access ◽  
Underwater Vehicle ◽  
Underwater Image

In the field of underwater image recognition, a chip with smaller footprint and lower energy consumption is required to be implanted into autonomous intelligent underwater vehicle to make real-time response to the surrounding objects. Therefore, a promising accelerator with high performance and low energy consumption is designed, which optimizes the features possessed by convolutional neural network. The sharing of weights between neurons reduces the memory requirement. With all convolutional neural network data stored within on-chip static random-access memory, the need for memory access is drastically decreased. Besides, several small processing elements are used to form neural functional unit, which considerably reduces the bandwidth requirement through inter-processing element data transmission. By sending control signals to autonomous underwater vehicle, this accelerator enables it to avoid dangerous areas such as rocks and algae in time. The result suggests the proposed accelerator successfully achieves a higher processing speed than that of CPU and GPU with a footprint of 6.09 mm2 only and the energy consumption of 327.3 mW at 1 GHz.

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