Robot Manipulator Control under Unix RCCL: A Robot Control "C" Library

1986 ◽  
Vol 5 (4) ◽  
pp. 94-111 ◽  
Author(s):  
Vincent Hayward ◽  
Richard P. Paul
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.


Author(s):  
Stephen Mascaro

This paper describes a modular 2-DOF serial robot manipulator and accompanying experiments that have been developed to introduce students to the fundamentals of robot control. The robot is designed to be safe and simple to use, and to have just enough complexity (in terms of nonlinear dynamics) that it can be used to showcase and compare the performance of a variety of textbook robot control techniques including computed torque feedforward control, inverse dynamics control, robust sliding-mode control, and adaptive control. These various motion control schemes can be easily implemented in joint space or operational space using a MATLAB/Simulink real-time interface. By adding a simple 2-DOF force sensor to the end-effector, the robot can also be used to showcase a variety of force control techniques including impedance control, admittance control, and hybrid force/position control. The 2-DOF robots can also be used in pairs to demonstrate control architectures for multi-arm coordination and master/slave teleoperation. This paper will describe the 2-DOF robot and control hardware/software, illustrate the spectrum of robot control methods that can be implemented, and show sample results from these experiments.


1997 ◽  
Vol 9 (6) ◽  
pp. 482-489
Author(s):  
Takahiro Tsuchiya ◽  
◽  
Ryosuke Masuda ◽  

In this paper, we discuss the sensor allocation problem in detecting obstacles in robot manipulators. The detection of obstacles in a work area is important for safety purposes and for the efficiency of robot control. Therefore, it is necessary to allocate the sensors properly on the links in a robot manipulator. Here, we propose two types of effective sensor allocation methods. One is based on the joint coordinates of the robot, and the other is based on the orthogonal work space. In addition, we show the allocation of additional sensors based on the quantitative conditions of the robot and its obstacles. The optical proximity sensor, which was developed by the authors, is used, and the proposed allocation methods are applied using a SCARA-type robot. It is proved, by experiments on obstacle avoidance control, that effective sensor allocations can be found.


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.


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