Abstract
The main goal of this contribution is to determine the excitation of an industrial robot, such that the
energy consumption becomes a minimum during the manipulation of the tool center point (TCP) from
a start position to a given end point within a predefined time. Such tasks can be restated as optimization
problems where the functional to be minimized consists of the endpoint error and a measure for
the energy. The gradient of this functional can be calculated by solving a linear differential equation,
called the adjoint system. On the one hand the minimum of the cost functional can be achieved by
the method of steepest descent where a proper step size has to be found or on the other hand by a
Quasi-Newton algorithm where the Hessian can be appreciated. The theory is applied to a six-axis
robot and the identification leads to a reduction of 47% of the signal energy.
| Originalsprache | Englisch |
|---|---|
| Titel | 1st OAGM-ARW Joint Workshop Vision Meets Robotics |
| Seiten | 1-8 |
| Publikationsstatus | Veröffentlicht - 2016 |
| Veranstaltung | OAGM & ARW Joint Workshop on Computer Vision and Robotics - Wels, Österreich Dauer: 11 Mai 2016 → 13 Mai 2016 https://www.fh-ooe.at/en/kongresse/2016/oagm-arw/ |
Konferenz
| Konferenz | OAGM & ARW Joint Workshop on Computer Vision and Robotics |
|---|---|
| Land/Gebiet | Österreich |
| Ort | Wels |
| Zeitraum | 11.05.2016 → 13.05.2016 |
| Internetadresse |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
-
SDG 7 – Erschwingliche und saubere Energie
Schlagwörter
- optimal control
- multibody dynamics
- adjoint system
- optimization
- calculus of variation
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