scholarly journals Fast Artificial Landmark Detection for Indoor Mobile Robots

Author(s):  
Dmitriy Kartashov ◽  
Arthur Huletski ◽  
Kirill Krinkin

2013 ◽  
Vol 133 (2) ◽  
pp. 356-364 ◽  
Author(s):  
Yoshinobu Hagiwara ◽  
Tatsuya Shoji ◽  
Hiroki Imamura


2013 ◽  
Vol 133 (1) ◽  
pp. 142-149
Author(s):  
Atsushi Shimada ◽  
Vincent Charvillat ◽  
Hajime Nagahara ◽  
Rin-ichiro Taniguchi
Keyword(s):  


2012 ◽  
Vol 132 (3) ◽  
pp. 381-388
Author(s):  
Takaaki Imaizumi ◽  
Hiroyuki Murakami ◽  
Yutaka Uchimura


Author(s):  
Tomoko IZUMI ◽  
Taisuke IZUMI ◽  
Sayaka KAMEI ◽  
Fukuhito OOSHITA
Keyword(s):  


2006 ◽  
Vol 65 (3) ◽  
pp. 229-241
Author(s):  
S. F. Yatsun ◽  
F. K. Freire ◽  
V. S. Dyshenko ◽  
O. A. Shadrina
Keyword(s):  


2016 ◽  
Vol 2016 (7) ◽  
pp. 1-6
Author(s):  
Yaqi Wang ◽  
Liangrui Peng ◽  
Shengjin Wang ◽  
Xiaoqing Ding




2019 ◽  
Vol 7 (5) ◽  
pp. 1617-1622
Author(s):  
Takrim Ul Islam Laskar ◽  
Parismita Sarma


2019 ◽  
Vol 19 (2) ◽  
pp. 39-46
Author(s):  
Abdulmuttalib T. Rashid ◽  
Abduladhem A. Ali


2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga

Relay node placement in wireless sensor networks for constrained environment is a critical task due to various unavoidable constraints. One of the most important constraints is unpredictable obstacles. Handling obstacles during relay node placement is complicated because of complexity involved to estimate the shape and size of obstacles. This paper presents an Obstacle-resistant relay node placement strategy (ORRNP). The proposed solution not only handles the obstacles but also estimates best locations for relay node placement in the network. It also does not involve any additional hardware (mobile robots) to estimate node locations thus can significantly reduce the deployment costs. Simulation results show the effectiveness of our proposed approach.



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