Decentralized Formation Control and Obstacles Avoidance Based on Potential Field Method

2006 ◽  
Author(s):  
Jia Wang ◽  
Xiao-bei Wu ◽  
Zhi-liang Xu
Keyword(s):  
Potential Field ◽  
Formation Control ◽  
Field Method ◽  
Potential Field Method

Multi-Robot Formation Control Based on the Artificial Potential Field Method

2014 ◽  
Vol 519-520 ◽  
pp. 1360-1363 ◽  
Author(s):  
Xi Na Gao ◽  
Li Juan Wu
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Field Method ◽  
Artificial Potential Field ◽  
Control Methods ◽  
Potential Field Method ◽  
Artificial Potential Field Method ◽  
Multi Robot

The artificial potential field method is one of multi-robot formation control methods. In this paper we make a study on multi-robot formation control based on the artificial potential field method and the leader-follower method. The robots are set leader robot and follower robots respectively. According to the known ideal distance between the leader and follower, we adjust the repulsiveness or attractiveness to maintain multi-robot formation. Multi-robots obstacle avoidance is adopted the artificial potential field method. In this paper the triangle formation is taken as an example. At last the simulation result proves the validity of this algorithm.

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.

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

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