Visual Trajectory Tracking of Wheeled Mobile Robots With Uncalibrated Camera Extrinsic Parameters

2020 ◽  
pp. 1-10
Author(s):  
Kaixiang Zhang ◽  
Jian Chen ◽  
Guoqing Yu ◽  
Xinfang Zhang ◽  
Zhaojian Li
Keyword(s):  
Mobile Robots ◽  
Trajectory Tracking ◽  
Wheeled Mobile Robots ◽  
Uncalibrated Camera ◽  
Extrinsic Parameters

scholarly journals Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 18426-18441
Author(s):  
Mohamed Abdelwahab ◽  
Victor Parque ◽  
Ahmed M. R. Fath Elbab ◽  
A. A. Abouelsoud ◽  
Shigeki Sugano
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Wheeled Mobile Robots

A self-tuning trajectory tracking controller for wheeled mobile robots

2019 ◽  
Vol 46 (6) ◽  
pp. 828-838 ◽  
Author(s):  
Pouya Panahandeh ◽  
Khalil Alipour ◽  
Bahram Tarvirdizadeh ◽  
Alireza Hadi
Keyword(s):  
Neural Networks ◽  
Convergence Rate ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Suggested Method ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Tracking Controller ◽  
Trajectory Tracking Controller

Purpose Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance. Design/methodology/approach In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller. Findings Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method. Originality/value In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.

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