Master data

Automated Log Ordering through robotic Grasper
Description:

AutoLOG will focus on research towards automating challenging and DDD (dull, dangerous, dirty) tasks concerning the handling of raw material in production lines currently executed manually. We will focus on artificial intelligence inspired vision based approaches to categorize and segment the raw material and its geometry to subsequently define (again through AI) optimal handling/grasping poses for the automation machinery. We seek to automate existing infrastructure in a versatile and cost effective way. Thus, we will investigate in retrofittable sensors and robust control strategies for seamless and cost efficient upgrading/retrofitting/automating existing infrastructure. As a specific application scenario and immediate benefit to Austrian’s industry, we will tackle the problem of autonomously grasping logs to be placed from the truck to the processing machinery.

Keywords: Deep learning, vision based navigation, autarkic sensors, retrofittable sensors
Short title: Auto-LOG
Period: 01.04.2018 - 31.03.2021
Contact e-mail: -
Homepage: -

Categorisation

Project type Research funding (on request / by call for proposals)
Funding type §27
Research type
  • Applied research
Subject areas
  • 202036 - Sensor systems
  • 202034 - Control engineering
  • 102003 - Image processing
  • 202035 - Robotics
Research Cluster
  • Self-organizing systems
Gender aspects Genderrelevance not selected
Project focus
  • Science to Science (Quality indicator: I)
Classification raster of the assigned organisational units:
working groups
  • Control of Networked Systems
  • Sensor- und Aktortechnik

Funding

Funding program
Produktion der Zukunft
Organisation: Österreichische Forschungsförderungsgesellschaft mbH (FFG)

Cooperations

No partner organisations selected