Refine
Year of publication
Document Type
- Article (71)
- Conference Proceeding (60)
- Part of a Book (7)
- Book (2)
Keywords
- Autonomous mobile robots (2)
- Industry 4.0 (2)
- Multi-robot systems (2)
- Smart factory (2)
- Anomaly detection (1)
- Automation (1)
- Benchmark (1)
- Computational modeling (1)
- Control (1)
- Cyber-physical systems (1)
- Datasets (1)
- GPU (1)
- Heuristic algorithms (1)
- Lidar (1)
- Mpc (1)
- Navigation (1)
- Neural networks (1)
- Path-following (1)
- Process optimization (1)
- Quality control (1)
- RoboCup (1)
- Self-driving (1)
- autonomous driving (1)
- do-it-yourself (1)
- education (1)
- embedded hardware (1)
- information systems (1)
- model-predictive control (1)
- sensor networks (1)
Institute
- Fachbereich Elektrotechnik und Informationstechnik (140) (remove)
The Scarab Project
(2015)
Urban Search and Rescue (USAR) is an active research
field in the robotics community. Despite recent advances
for many open research questions, these kind of systems are
not widely used in real rescue missions. One reason is that such
systems are complex and not (yet) very reliable; another is that
one has to be an robotic expert to run such a system. Moreover,
available rescue robots are very expensive and the benefits of
using them are still limited.
In this paper, we present the Scarab robot, an alternative
design for a USAR robot. The robot is light weight, humanpackable
and its primary purpose is that of extending the
rescuer’s capability to sense the disaster site. The idea is that a
responder throws the robot to a certain spot. The robot survives
the impact with the ground and relays sensor data such as
camera images or thermal images to the responder’s hand-held
control unit from which the robot can be remotely controlled.
This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.
RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.