The Local Area Map Building for Mobile Robot Navigation Using Ultrasound and Infrared Sensors

2007 ◽  
Author(s):  
Vasyl Koval ◽  
Oleh Adamiv ◽  
Viktor Kapura
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Local Area ◽  
Mobile Robot Navigation ◽  
Map Building ◽  
Infrared Sensors

MOBILE ROBOT PATH OPTIMIZATION ALGORITHM USING VECTOR CALCULUS AND MAPPING OF 2 DIMENSIONAL SPACE

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Ammar Zahari ◽  
Amelia Ritahani Ismail ◽  
Recky Desia
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Dimensional Space ◽  
Optimization Technique ◽  
Path Optimization ◽  
Mobile Robot Navigation ◽  
Infrared Sensors ◽  
Unknown Environment ◽  
Simulated Environment ◽  
Vector Calculus

This research explores path integration in mobile robot navigation and path optimization technique using vector calculus. A simulated robot in a simulated environment is used to test the algorithm that is to be developed. The simulated robot is equipped with a sonar sensor and several infrared sensors on its chassis. Mobile robot navigation in an unknown environment is very crucial as It not only has to be concerned about reaching its destination but also to avoid obstacles that may be in the way. This algorithm can effectively allow a mobile robot to navigate an unknown environment without collision into obstacles.

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.

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