scholarly journals Growing Neural Gas Based Topological Environmental Map Building and Path Planning in Unknown Environment

2021 ◽  
Vol 33 (4) ◽  
pp. 872-884
Author(s):  
Yuichiro TODA ◽  
Hikari MIYASE ◽  
Mutsumi IWASA ◽  
Akimasa WADA ◽  
Soma TAKEDA ◽  
...  
Keyword(s):  
Path Planning ◽  
Map Building ◽  
Growing Neural Gas ◽  
Unknown Environment ◽  
Neural Gas ◽  
Environmental Map

Path Planning of Mobile Robot with Growing Neural Gas and Ant Colony Optimization

2018 ◽  
Author(s):  
Thiago Azevedo ◽  
Claudio César ◽  
Allan C. Gomes ◽  
Marcus Davi ◽  
Thiago A. Lima ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Growing Neural Gas ◽  
Neural Gas

From Point Cloud to Triangular Mesh by Growing Neural Gas

2014 ◽  
Vol 24 (3) ◽  
pp. 651-662
Author(s):  
Feng ZENG ◽  
Tong YANG ◽  
Shan YAO
Keyword(s):  
Point Cloud ◽  
Triangular Mesh ◽  
Growing Neural Gas ◽  
Neural Gas

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.

Path planning of rotorcrafts in unknown environment

2016 ◽  
Author(s):  
Shupeng Lai ◽  
Kangli Wang ◽  
Kun Li ◽  
Ben M. Chen
Keyword(s):  
Path Planning ◽  
Unknown Environment

Growing Neural Gas Based on Data Density

2018 ◽  
pp. 314-323
Author(s):  
Lukáš Vojáček ◽  
Pavla Dráždilová ◽  
Jiří Dvorský
Keyword(s):  
Growing Neural Gas ◽  
Neural Gas ◽  
Data Density

Path planning and traversable area marking for stereo vision-based 3D map building

2011 ◽  
Vol 2 (4) ◽  
pp. 277
Author(s):  
Jaroslav Rozman ◽  
N.A. Františ ◽  
ek V. Zboř ◽  
N.A. il
Keyword(s):  
Path Planning ◽  
Stereo Vision ◽  
Map Building

scholarly journals Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

2016 ◽  
Author(s):  
Yuichiro Toda ◽  
Zhaojie Ju ◽  
Hui Yu ◽  
Naoyuki Takesue ◽  
Kazuyoshi Wada ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Growing Neural Gas ◽  
Neural Gas ◽  
Point Cloud Segmentation
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