Stable mobile robots teleoperation via Potential Field method

2008 ◽  
Author(s):  
Adha I. Cahyadi ◽  
Chang Ya-Chun ◽  
Yoshio Yamamoto
Keyword(s):  
Mobile Robots ◽  
Potential Field ◽  
Field Method ◽  
Potential Field Method

Potential field method to navigate several mobile robots

2006 ◽  
Vol 25 (3) ◽  
pp. 321-333 ◽  
Author(s):  
Saroj Kumar Pradhan ◽  
Dayal Ramakrushna Parhi ◽  
Anup Kumar Panda ◽  
Rabindra Kumar Behera
Keyword(s):  
Mobile Robots ◽  
Potential Field ◽  
Field Method ◽  
Potential Field Method

A novel potential field method for path planning of mobile robots by adapting animal motion attributes

2016 ◽  
Vol 82 ◽  
pp. 24-34 ◽  
Author(s):  
Bence Kovács ◽  
Géza Szayer ◽  
Ferenc Tajti ◽  
Mauricio Burdelis ◽  
Péter Korondi
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Potential Field ◽  
Field Method ◽  
Potential Field Method

scholarly journals Potential Field Method Parameters Tuning Using Fuzzy Inference System for Adaptive Formation Control of Multi-Mobile Robots

Robotics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 10 ◽  
Author(s):  
Basma Gh. Elkilany ◽  
A. A. Abouelsoud ◽  
Ahmed M. R. Fathelbab ◽  
Hiroyuki Ishii
Keyword(s):  
Mobile Robots ◽  
Potential Field ◽  
Formation Control ◽  
Fuzzy Inference ◽  
Field Method ◽  
Burger Model ◽  
Inference System ◽  
Modified Method ◽  
Potential Field Method ◽  
Narrow Passage

Nowadays, employing more than one single robot in complex tasks or dangerous environments is highly required. Thus, the formation of multi-mobile robots is an active field. One famous method for formation control is the Potential Field Method due to its simplicity and efficiency in dynamic environments. Therefore, we propose a Fuzzy Inference tuning of the potential field parameters to overcome its limitations. We implement the modified method with tuned parameters on MATLAB and apply it to three TurtleBot3 burger model robots. Then, several real-time experiments are carried out to confirm the applicability and validity of the modified potential filed method to achieve the robots’ tasks. The results assert that the TurtleBot3 robots can escape from a local minimum, pass through a narrow passage, and pass between two closely placed obstacles.

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