Journal of Intelligent & Robotic Systems
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Published By Springer-Verlag

1573-0409, 0921-0296

2022 ◽  
Vol 104 (1) ◽  
Author(s):  
Shuai Zhang ◽  
Shiqi Li ◽  
Xiao Li ◽  
Youjun Xiong ◽  
Zheng Xie

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Loris Roveda ◽  
Marco Maroni ◽  
Lorenzo Mazzuchelli ◽  
Loris Praolini ◽  
Asad Ali Shahid ◽  
...  

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Sotirios Spanogiannopoulos ◽  
Yahya Zweiri ◽  
Lakmal Seneviratne

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Syeda Nadiah Fatima Nahri ◽  
Shengzhi Du ◽  
Barend Jacobus Van Wyk

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Shaojie Wang ◽  
Xiaoguang Hu ◽  
Jin Xiao ◽  
Tianyou Chen

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Nan Qiao ◽  
Lihui Wang ◽  
Mingjie Liu ◽  
Zhuoxuan Wang

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Jing Xin ◽  
Caixia Dong ◽  
Youmin Zhang ◽  
Yumeng Yao ◽  
Ailing Gong

AbstractAiming at satisfying the increasing demand of family service robots for housework, this paper proposes a robot visual servoing scheme based on the randomized trees to complete the visual servoing task of unknown objects in natural scenes. Here, “unknown” means that there is no prior information on object models, such as template or database of the object. Firstly, an object to be manipulated is randomly selected by user prior to the visual servoing task execution. Then, the raw image information about the object can be obtained and used to train a randomized tree classifier online. Secondly, the current image features can be computed using the well-trained classifier. Finally, the visual controller can be designed according to the error of image feature, which is defined as the difference between the desired image features and current image features. Five visual positioning of unknown objects experiments, including 2D rigid object and 3D non-rigid object, are conducted on a MOTOMAN-SV3X six degree-of-freedom (DOF) manipulator robot. Experimental results show that the proposed scheme can effectively position an unknown object in complex natural scenes, such as occlusion and illumination changes. Furthermore, the developed robot visual servoing scheme has an excellent positioning accuracy within 0.05 mm positioning error.


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