Research on Real Time Path Planning Method for the Underwater Robot in Unknown Environment with Random Shape Obstacle

2006 ◽  
Author(s):  
Qunjie Duan ◽  
Mingjun Zhang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Planning Method ◽  
Underwater Robot ◽  
Random Shape ◽  
Unknown Environment ◽  
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.

A two level real-time path planning method inspired by cognitive map and predictive optimization in human brain

2014 ◽  
Vol 21 ◽  
pp. 352-364 ◽  
Author(s):  
Yeganeh M. Marghi ◽  
Farzad Towhidkhah ◽  
Shahriar Gharibzadeh
Keyword(s):  
Path Planning ◽  
Human Brain ◽  
Real Time ◽  
Cognitive Map ◽  
Planning Method ◽  
Time Path

scholarly journals Improved Artificial Potential Field Method Applied for AUV Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaojing Fan ◽  
Yinjing Guo ◽  
Hui Liu ◽  
Bowen Wei ◽  
Wenhong Lyu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Field Function ◽  
Time Path

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

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