Three Dimensional AUV Complete Coverage Path Planning with Glasius Bio-inspired Neural Network

2018 ◽  
pp. 125-136 ◽  
Author(s):  
Bing Sun ◽  
Xixi Zhu ◽  
Wei Zhang ◽  
Daqi Zhu ◽  
Zhenzhong Chu
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Three Dimensional ◽  
Complete Coverage ◽  
Coverage Path Planning

scholarly journals Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm

2021 ◽  
Vol 9 (11) ◽  
pp. 1163
Author(s):  
Peng-Fei Xu ◽  
Yan-Xu Ding ◽  
Jia-Cheng Luo
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Target Area ◽  
Complete Coverage ◽  
A Algorithm ◽  
Unmanned Surface Vehicle ◽  
Calculation Time ◽  
Coverage Path Planning ◽  
Practical Applications ◽  
Area Of Interest

In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In this paper, a complete coverage neural network (CCNN) algorithm for the path planning of a USV is proposed for the first time. By simplifying the calculation process of the neural activity, the CCNN algorithm can significantly reduce calculation time. To improve coverage efficiency and make the path more regular, the optimal next position decision formula combined with the covering direction term is established. The CCNN algorithm has increased moving directions of the path in grid maps, which in turn has further reduced turning-angles and makes the path smoother. Besides, an improved A* algorithm that can effectively decrease path turns is presented to escape the deadlock. Simulations are carried out in different environments in this work. The results show that the coverage path generated by the CCNN algorithm has less turning-angle accumulation, deadlocks, and calculation time. In addition, the CCNN algorithm is capable to maintain the covering direction and adapt to complex environments, while effectively escapes deadlocks. It is applicable for USVs to perform multiple engineering missions.

DISCRETE BIOINSPIRED NEURAL NETWORK FOR COMPLETE COVERAGE PATH PLANNING

2017 ◽  
Vol 32 (2) ◽  
Author(s):  
Jianfeng Zhang ◽  
Houyong Lv ◽  
Dongjian He ◽  
Lvwen Huang ◽  
Yuan Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Complete Coverage ◽  
Coverage Path Planning

Application of Improved Back Propagation Neural Network in Mowing Robot’s Path Planning

2014 ◽  
Vol 602-605 ◽  
pp. 916-919
Author(s):  
Yan Deng ◽  
Guo Wei Yang ◽  
Xue Mei Cui ◽  
Shao Long Wu
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Dead Zone ◽  
Back Propagation ◽  
Grid Method ◽  
Back Propagation Neural Network ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Local Path ◽  
Coverage Rates

To address the problem of planning complete coverage paths for mowing robots that have the greatest coverage rates and the lowest repetitive rates, we proposed an improved back propagation neural network algorithm based on priority traversal thoughts for local path planning. The algorithm based on plowing global path planning. We adopted grid method to model the environment and used Matlab2010a to simulate for the algorithm. Simulation results show that the proposed algorithm can make the mowing robot walk out of dead zone, the dead zone was composed of obstacle grid or the grid that had been cut around this area, and achieve the complete coverage path planning.

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.

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