Jacobian performance measures can ignore first and last joint angles
Joint-Angle Invariance of Jacobian-Based Performance Indices for Serial Robotic Manipulators
Robotics
Summary
Evaluating how well robot arms perform can be complicated because it depends on all the joint angles. This paper shows that under certain conditions, some important performance scores don't change if you adjust the first or last joint angle. This means fewer angles need to be considered, making evaluations simpler and faster. The authors tested this idea in making performance maps and designing tasks, and it saved time and computing power without losing accuracy.
What this means in practice
- •For robotics engineers: Create faster workspace performance maps by ignoring irrelevant joint angles in Jacobian computations.
- •For automation system designers: Optimize robot task layouts more efficiently by reducing the dimensionality of configuration space during performance evaluation.
Authors
Yaohua Zhou, Mingdao Lin, Jiayu Song, Xixin Zhao, Haidong Huang, Jiahu Qin, Xiaocong Li
Abstract
This article investigates the joint-angle invariance (JAI) of Jacobian-based performance indices for serial robotic manipulators. We identify kinematic conditions under which representative performance indices, including manipulability, stiffness, and other Jacobian-derived indices, remain independent of the first and last joint angles. This property allows a systematic reduction in the effective configuration-space dimensionality, thereby simplifying the performance evaluation. The utility of JAI is demonstrated through two application scenarios: workspace performance map construction and task layout optimization. Comparative results show that the proposed JAI-based strategy reduces computational effort and storage requirements without compromising the effectiveness.