Real-time path planning and obstacle avoidance for an autonomous underwater vehicle

2003 ◽  
Author(s):  
G. Antonelli ◽  
S. Chiaverini ◽  
R. Finotello ◽  
E. Morgavi
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Time Path

Real-time path planning and obstacle avoidance for RAIS: an autonomous underwater vehicle

2001 ◽  
Vol 26 (2) ◽  
pp. 216-227 ◽  
Author(s):  
G. Antonelli ◽  
S. Chiaverini ◽  
R. Finotello ◽  
R. Schiavon
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Time Path

scholarly journals A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jianjun Ni ◽  
Liuying Wu ◽  
Pengfei Shi ◽  
Simon X. Yang
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Planning Method ◽  
Planning Problem ◽  
Detection Range ◽  
The Real ◽  
Time Path

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Three Dimensional Path Planning of Autonomous Underwater Vehicles

2013 ◽  
Vol 328 ◽  
pp. 128-132
Author(s):  
Yan Peng ◽  
Wei Qing Wu ◽  
Mei Liu ◽  
Shao Rong Xie ◽  
Jun Luo
Keyword(s):  
Lyapunov Function ◽  
Path Planning ◽  
Real Time ◽  
Tracking Control ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Control Lyapunov Function

The path planning relates to the safe movement and navigation of the Autonomous Underwater Vehicles (AUV). This paper discusses the way of real-time path planning for autonomous underwater vehicle based on tracking control lyapunov function. The simulation conducted on H300 illustrates the effectiveness of proposed method.

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.

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