Three-dimensional map building for mobile robot navigation environments using a self-organizing neural network

2004 ◽  
Vol 21 (6) ◽  
pp. 323-343 ◽  
Author(s):  
Min Young Kim ◽  
Hyungsuck Cho
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Three Dimensional ◽  
Mobile Robot Navigation ◽  
Map Building ◽  
Self Organizing

Surface Estimation ICP Algorithm for Building a 3D Map by a Scanning LRF

2017 ◽  
Vol 14 (03) ◽  
pp. 1750011
Author(s):  
Yoseop Hwang ◽  
Jangmyung Lee
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Three Dimensional ◽  
Mobile Robot Navigation ◽  
Laser Range Finder ◽  
Map Building ◽  
Feature Points ◽  
Icp Algorithm ◽  
Surface Conditions ◽  
Surface Estimation

A new three-dimensional (3D) map building method based on Laser Range Finder (LRF) has been proposed in this research, performing a surface estimation with the Iterative Closest Point (ICP) algorithm. While a mobile robot is navigating in an unknown environment, the entire environment cannot be scanned by LRF since kinematic features of the mobile robot and surface conditions are dynamically changing. To resolve this difficulty in building a 3D map while the mobile robot is navigating, a surface estimation ICP algorithm is proposed, which is based on the continuity of the environment around mobile robot. That is, this new algorithm recovers the un-scanned area by estimating feature points in the neighboring two regions based on the continuous environment information. The effectiveness of proposed algorithm has been demonstrated through real experiments of the mobile robot navigation.

Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment

2020 ◽  
Vol 1 (1) ◽  
pp. 59-67
Author(s):  
P. Pei ◽  
Yu. N. Petrenko
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Industrial Internet Of Things ◽  
Navigation Problem ◽  
Automatic Navigation ◽  
Industrial Internet ◽  
Fuzzy Neural ◽  
Navigation Control

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.

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