PN-WSNA-based implementation for autonomous mobile robot navigation systems

2012 ◽  
Author(s):  
Ting-Shuo Chen ◽  
Yu-Chen Kuo ◽  
Chung-Hsien Kuo
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Navigation Systems ◽  
Autonomous Mobile Robot

Development of Autonomous Mobile Robot that Can Navigate in Rainy Situations

2016 ◽  
Vol 28 (4) ◽  
pp. 441-450 ◽  
Author(s):  
Naoki Akai ◽  
◽  
Yasunari Kakigi ◽  
Shogo Yoneyama ◽  
Koichi Ozaki ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Real World ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Navigation Systems ◽  
Autonomous Mobile Robot ◽  
Technical Challenge ◽  
The Real ◽  
Mobile Navigation ◽  
Unexpected Behavior

[abstFig src='/00280004/02.jpg' width='300' text='Navigation under strong rainy condition' ] The Real World Robot Challenge (RWRC), a technical challenge for mobile outdoor robots, has robots automatically navigate a predetermined path over 1 km with the objective of detecting specific persons. RWRC 2015 was conducted in the rain and every robot could not complete the mission. This was because sensors on the robots detected raindrops and the robots then generated unexpected behavior, indicating the need to study the influence of rain on mobile navigation systems – a study clearly not yet sufficient. We begin by describing our robot’s waterproofing function, followed by investigating the influence of rain on the external sensors commonly used in mobile robot navigation and discuss how the robot navigates autonomous in the rain. We conducted navigation experiments in artificial and actual rainy environments and those results showed that the robot navigates stably in the rain.

Compact Fuzzy Q Learning for Autonomous Mobile Robot Navigation

Robotics ◽  
2010 ◽  
Author(s):  
H. Wicaksono ◽  
K. Anam ◽  
P. Hastono ◽  
I.A. Sulistijono ◽  
S. Kuswadi
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Q Learning
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