SkiROS—A Skill-Based Robot Control Platform on Top of ROS

2017 ◽  
pp. 121-160 ◽  
Author(s):  
Francesco Rovida ◽  
Matthew Crosby ◽  
Dirk Holz ◽  
Athanasios S. Polydoros ◽  
Bjarne Großmann ◽  
...  
Keyword(s):  
Robot Control

Robot Control—Theory and Applications

IEE Review ◽  
1988 ◽  
Vol 34 (7) ◽  
pp. 280
Author(s):  
A.G. Blay
Keyword(s):  
Control Theory ◽  
Robot Control

Flexible Two-wheeled Self-balancing Robot Control Based on Hopfield Neural Network

ROBOT ◽  
2010 ◽  
Vol 32 (3) ◽  
pp. 405-413
Author(s):  
Xiaogang RUAN ◽  
Jianwei ZHAO
Keyword(s):  
Neural Network ◽  
Robot Control ◽  
Hopfield Neural Network ◽  
Balancing Robot

Remote Two-wheel Robot control using OPC-UA

2020 ◽  
Author(s):  
Seung-Jun Lee ◽  
Woojin Kim ◽  
Yang Koo Lee ◽  
DaeSub Yoon ◽  
Jun wook Lee
Keyword(s):  
Robot Control

Safety in Robot Control Systems

1988 ◽  
Vol 21 (9) ◽  
pp. 266-271 ◽  
Author(s):  
G R Ward
Keyword(s):  
Control Systems ◽  
Robot Control

scholarly journals Control system of 4-DOF palletizing robot based on improved R control multi-objective trajectory planning

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110027
Author(s):  
Jianqiang Wang ◽  
Yanmin Zhang ◽  
Xintong Liu
Keyword(s):  
Control System ◽  
Trajectory Planning ◽  
Robot Control ◽  
Machining Process ◽  
Planning System ◽  
Positioning Accuracy ◽  
Multi Objective ◽  
Planning Algorithm ◽  
Function Testing ◽  
Robot Control System

To realize efficient palletizing robot trajectory planning and ensure ultimate robot control system universality and extensibility, the B-spline trajectory planning algorithm is used to establish a palletizing robot control system and the system is tested and analyzed. Simultaneously, to improve trajectory planning speeds, R control trajectory planning is used. Through improved algorithm design, a trajectory interpolation algorithm is established. The robot control system is based on R-dominated multi-objective trajectory planning. System stack function testing and system accuracy testing are conducted in a production environment. During palletizing function testing, the system’s single-step code packet time is stable at approximately 5.8 s and the average evolutionary algebra for each layer ranges between 32.49 and 45.66, which can save trajectory planning time. During system accuracy testing, the palletizing robot system’s repeated positioning accuracy is tested. The repeated positioning accuracy error is currently 10−1 mm and is mainly caused by friction and the machining process. By studying the control system of a four-degrees-of-freedom (4-DOF) palletizing robot based on the trajectory planning algorithm, the design predictions and effects are realized, thus providing a reference for more efficient future palletizing robot design. Although the working process still has some shortcomings, the research has major practical significance.

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