A software-based procedure for robotic end effector error correction

Presents a procedure for modeling and eliminating an end effector configuration error as a result of a faulty joint or a damaged link. This procedure provides an inexpensive software alternative to hardware replacement. A neural network model was developed and tested an a 6 DOF PUMA robot. The network approximates the error based on data obtained through observing the robot while executing a set of MOVE commands. The results show that, regardless of the error source, the robot's accuracy could be highly improved even when a small number of data points are used.

Authors

• Medhat Moussa
• Martin Hill
• James Fernandes
• Fakhri Karray

Keywords

• End effectors
• Error correction
• Neural networks
• Robot kinematics
• Computer errors
• Testing
• Medical robotics
• Hardware
• Service robots
• Orbital robotics
• End-effector
• Neural Network
• Artificial Neural Network
• Sources Of Error
• Visual System
• Average Error
• Unit Vector
• 3D Space
• Experimental Environment
• Position Error
• Test Points
• Coordinate Frame
• Joint Function
• Neural Net
• Inverse Kinematics
• Inverse Solution
• End-effector Position
• Joint Displacement