Abstract
Industrial robots have been widely used in various fields. The joint angle error is the main factor that affects the accuracy performance of the robot. It is important to notice that these kinematic parameters error cannot be eliminated from the robot system completely. Even after calibration, these errors still exist and will be fluctuated during the robot system running. This paper proposed a new method of finding the best position and orientation to perform a specific working path based on the current accuracy capacity of the robot system. By analyzing the robot forward/inverse kinematic and the angle error sensitivity of different joint in the serial manipulator system, a new evaluation formulation is established for mapping the trajectory accuracy within the robot’s working volume. The influence of different position and orientation on the movement accuracy of the end effector has been verified by experiments and discussed thoroughly. Finally, a visualized evaluation map can be obtained to describe the accuracy difference of a robotic laser deposition working path at different positions and orientations. This method is helpful for making the maximum usage of the robot’s current accuracy ability rather than blindly pursuing the higher accuracy of the robot system.
Recommended Citation
Z. Wang et al., "Industrial Robot Trajectory Accuracy Evaluation Maps for Hybrid Manufacturing Process based on Joint Angle Error Analysis," Advances in Robotics and Automation, vol. 7, no. 1, OMICS International, Jan 2018.
The definitive version is available at https://doi.org/10.4172/2168-9695.1000183
Department(s)
Mechanical and Aerospace Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Industrial robot; Trajectory accuracy; Joint angle error; Hybrid manufacturing
International Standard Serial Number (ISSN)
2168-9695
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2018 The Authors, All rights reserved.
Publication Date
01 Jan 2018