scholarly journals Multiple Region Coverage Path Planning for Autonomous Underwater Vehicle.

2021 ◽  
Vol 71 (6) ◽  
pp. 784-790
Author(s):  
Shristi Deva Sinha
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Specific Problem ◽  
Travelling Salesman Problem ◽  
Underwater Vehicle ◽  
Simulation Studies ◽  
Coverage Path Planning ◽  
Instance Optimality ◽  
Benchmark Instance ◽  
Planning Methodology

Coverage path planning methodology for an autonomous underwater vehicle to search multiple non-overlapping regions has been proposed in the paper. The proposed methodology is based on the genetic algorithm (GA). The GA used in the proposed methodology has been tuned for the specific problem, using design of experiment on an equivalent travelling salesman problem benchmark instance. Optimality of the generated paths was analysed through simulation studies. Results indicated that the proposed methodology generated shorter paths in comparison to conventional methods.

scholarly journals Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

2012 ◽  
Vol 8 (10) ◽  
pp. 567959 ◽  
Author(s):  
Mingzhong Yan ◽  
Daqi Zhu ◽  
Simon X. Yang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Evidence Theory ◽  
Sensor Data ◽  
Map Building ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

scholarly journals Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

2019 ◽  
Vol 52 (21) ◽  
pp. 315-322 ◽  
Author(s):  
Hui Sheng Lim ◽  
Shuangshuang Fan ◽  
Christopher K.H. Chin ◽  
Shuhong Chai ◽  
Neil Bose ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Swarm Optimization

An Algorithm of Complete Coverage Path Planning for Autonomous Underwater Vehicles

2011 ◽  
Vol 467-469 ◽  
pp. 1377-1385 ◽  
Author(s):  
Ming Zhong Yan ◽  
Da Qi Zhu
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Time Varying ◽  
Simulation Studies ◽  
Complete Coverage ◽  
Biologically Inspired ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Global Cost

Complete coverage path planning (CCPP) is an essential issue for Autonomous Underwater Vehicles’ (AUV) tasks, such as submarine search operations and complete coverage ocean explorations. A CCPP approach based on biologically inspired neural network is proposed for AUVs in the context of completely unknown environment. The AUV path is autonomously planned without any prior knowledge of the time-varying workspace, without explicitly optimizing any global cost functions, and without any learning procedures. The simulation studies show that the proposed approaches are capable of planning more reasonable collision-free complete coverage paths in unknown underwater environment.

scholarly journals Path planning for an identification mission of an Autonomous Underwater Vehicle in a lemniscate form

2018 ◽  
Vol 51 (29) ◽  
pp. 323-328 ◽  
Author(s):  
Ayushman Barua ◽  
Jörg Kalwa ◽  
Yuri Shardt ◽  
Thomas Glotzbach
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle
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