Computational Intelligence Based Approach for the Joint Trajectory Generation of Cooperative Robotic Systems

2002 ◽  
Vol 42 (10) ◽  
pp. 1499-1520 ◽  
Author(s):  
W. Gueaieb ◽  
F. Karray ◽  
S. Al-Sharhan
Keyword(s):  
Computational Intelligence ◽  
Trajectory Generation ◽  
Robotic Systems

scholarly journals Computational Intelligence for Mobile Robotic Systems - Decision Making, Learning, and Planning -

2000 ◽  
Vol 12 (3) ◽  
pp. 194-201 ◽  
Author(s):  
Toshio Fukuda ◽  
◽  
Naoyuki Kubota ◽  
Keyword(s):  
Decision Making ◽  
Computational Intelligence ◽  
System Architecture ◽  
Evolutionary Computing ◽  
Robotic Systems ◽  
Neural Computing ◽  
Changing Environments ◽  
Fuzzy Computing

This paper describes recent topics of computational intelligence. The intelligent capabilities will be required for the various systems to adapt to dynamically changing environments. First, we introduce the computational intelligence including evolutionary computing, neural computing, and fuzzy computing. Next, some of the important problems including the system architecture, structured intelligence, emerging system, and implementation methods are discussed for mobile robotic systems from the viewpoint of coevolution.

NURBs for Robot Manipulator Trajectory Generation

2011 ◽  
Author(s):  
John Steuben ◽  
John Steele ◽  
Cameron J. Turner
Keyword(s):  
Control Systems ◽  
Robot Manipulator ◽  
Trajectory Generation ◽  
Robotic Systems ◽  
Future Research ◽  
B Splines ◽  
Motion Trajectories ◽  
Arbitrary Geometries ◽  
The Future ◽  
Do So

Modern applications of automation and robotic technologies require increasingly sophisticated control systems. One of the areas in which the control of these robotic systems can be improved is the generation of motion trajectories. We demonstrate that Non-Uniform Rational B-splines (NURBs) can be successfully adapted for use in producing trajectories for 6-DOF manipulators. We also discuss a methodology for fitting such splines to arbitrary geometries allowing for the generation of paths in cases where it is difficult or impossible to do so using less sophisticated methods. We demonstrate these techniques in simulation, where they are applied to a pair of complex industrial tasks. Several important properties of NURBs which offer advantages not present in other methods are also discussed. We conclude by discussing the future research needed to bring this technique to maturity, and potential opportunities that may be realized in its adoption.

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