Robot Manipulator Control via Solving Four-Layered Time-Variant Equations Including Linear, Nonlinear Equalities and Inequalities

Author(s):  
Xinhui Zhu ◽  
Li Zhang ◽  
Yang Shi ◽  
Jing Wang ◽  
Jian Li
2020 ◽  
Vol 25 (3) ◽  
pp. 7-12
Author(s):  
Rud V.V. ◽  

This paper considers the problems of the integration of independent manipulator control systems. Areas of control of the manipulator are: recognition of objects and obstacles, identification of objects to be grasped, determination of reliable positions by the grasping device, planning of movement of the manipulator to certain positions with avoidance of obstacles, and recognition of slipping or determination of reliable grasping. This issue is a current problem primarily in industry, general-purpose robots, and experimental robots. This paper considers current publications that address these issues. Existing algorithms and approaches have been found in the management of both parts of the robot manipulator and solutions that combine several areas, or the integration of several existing approaches. There is a brief review of current literature and publications on the above algorithms and approaches. The advantages and disadvantages of the considered methods and approaches are determined. There are solutions that cover either some areas or only one of them, which does not meet the requirements of the problem. Using existing approaches, integration points of existing implementations are identified to get the best results. In the process, a system was developed that analyzes the environment, finds obstacles, objects for interaction, poses for grasping, plans the movement of the manipulator to a specific position, and ensures reliable grasping of the object. The next step was to test the system, test the performance, and adjust the parameters for the best results. The resulting system was developed by the research team of RT-Lions, Technik University, Reutlingen. The hardware research robot includes an Intel Realsense camera, a Sawyer Arm manipulator from Rethink Robotics, and an internally grabbing device.


1995 ◽  
Vol 7 (1) ◽  
pp. 21-28
Author(s):  
Mohammad Teshnehlab ◽  
◽  
Keigo Watanabe ◽  

This paper describes the complete flexible design of a fuzzy gaussian potential neural network (FGPNN) having the ability to learn expert control rules of fuzzy controller. The proposed structure consists of gaussian potential function (GPF) which is utilized in the antecedent as the membership function, and the flexible bipolar sigmoid function (FBSF) is utilized in the conclusion part. The GPF enables a reduction in the number of labelings in the antecedent, and the FBSF leads to a reduction in the learning load in the conclusion and captures the linearity and/or nonlinearity of the system in the conclusion. The proposed construction reduces the complexity to a simple design in the antecedent, especially for large-scale inputs, thus shortening the time for learning with learning sigmoid function parameters (SFPs) in the conclusion part only. Finally, the simulations of two-link manipulator will be provided for both the conventional and proposed FGPNN controller in order to evaluate the newly designed controller.


2000 ◽  
Vol 33 (24) ◽  
pp. 197-202
Author(s):  
L. Peñalver ◽  
J.C. Fernández ◽  
S. Terrasa ◽  
J. Tornero ◽  
V. Hernández

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