Refine
Year of publication
Document Type
- Conference Proceeding (31)
- Article (reviewed) (6)
- Patent (4)
- Contribution to a Periodical (3)
- Article (unreviewed) (2)
- Part of a Book (1)
- Report (1)
Conference Type
- Konferenzartikel (29)
- Konferenz-Abstract (1)
- Sonstiges (1)
Language
- English (36)
- German (11)
- Other language (1)
Is part of the Bibliography
- yes (48)
Keywords
- energy harvesting (4)
- Funktechnik (3)
- Kommunikation (3)
- Robotics (3)
- Sicherheit (3)
- 3D printing (2)
- Applikation (2)
- Datensicherung (2)
- Human-Robot Collaboration (2)
- robot (2)
Institute
Open Access
- Closed Access (24)
- Open Access (11)
- Closed (9)
- Bronze (3)
- Gold (2)
Novel approaches for the design of assistive technology controls propose the usage of eye tracking devices such as for smart wheelchairs and robotic arms. The advantages of artificial feedback, especially vibrotactile feedback, as opposed to their use in prostheses, have not been sufficiently explored. Vibrotactile feedback reduces the cognitive load on the visual and auditory channel. It provides tactile sensation, resulting in better use of assistive technologies. In this study the impact of vibration on the precision and accuracy of a head-worn eye tracking device is investigated. The presented system is suitable for further research in the field of artificial feedback. Vibration was perceivable for all participants, yet it does not produce any significant deviations in precision and accuracy.
Established robot manufacturers have developed methods to determine and optimize the accuracy of their robots. These methods vary from robot manufacturers to their competitors. Due to the lack of published data, a comparison of robot performance is difficult. The aim of this article is to find methods to evaluate important characteristics of a robot with an accurate and cost-effective setup. A laser triangulation sensor and geometric referenced spheres were used as a base to compare the robot performance.
A benchmark analysis of Long Range (LoRaTM) Communication at 2.45 Ghz for safety applications
(2014)
The demand of wireless solutions in industrial applications increases since the early nineties. This trend is not only ongoing, it is further pushed by developments in the area of software stacks like the latest Bluetooth Low Energy Stack. It is also pushed by new chip-designs and powerful and highly integrated electronic hardware. The acceptance of wireless technologies as a possible solution for industrial applications, has overcome the entry barrier [1]. The first step to see wireless as standard for many industrial applications is almost accomplished. Nevertheless there is nearly none acceptance of wireless technology for Safety applications. One highly challenging and demanding requirement is still unsolved: The aspect safety and robustness. Those topics have been addressed in many cases but always in a similar manner. WirelessHART as an example addresses this topic with redundant so called multiple propagation paths and frequency hopping to handle with interferences and loss of network participants. So far the pure peer to peer link is rarely investigated and there are less safety solutions available. One product called LoRa™ can be seen as one possible solution to address this lack of safety within wireless links. This paper focuses on the safety performance evaluation of a modem-chip-design. The use of diverse and redundant wireless technologies like LoRa can lead to an increase acceptance of wireless in safety applications. Many measurements in real industrial application have been carried out to be able to benchmark the new chip in terms of the safety aspects. The content of this research results can help to raise the level of confidence in wireless. In this paper, the term “safety” is used for data transmission reliability.
Sicher funken mit 2,45 GHz
(2015)
In this contribution, we present a novel 3D printed multi-material, electromagnetic vibration harvester. The harvester is based on a cantilever design and utilizes an embedded constantan wire within a matrix of polyethylene terephthalate glycol (PETG). A prototype has been manufactured with a combination of a fused filament fabrication (FFF) printer and a robot with a custom-made tool.
Differentiation between human and non-human objects can increase efficiency of human-robot collaborative applications. This paper proposes to use convolutional neural networks for classifying objects in robotic applications. The body temperature of human beings is used to classify humans and to estimate the distance to the sensor. Using image classification with convolutional neural networks it is possible to detect humans in the surroundings of a robot up to five meters distance with low-cost and low-weight thermal cameras. Using transfer learning technique we trained the GoogLeNet and MobilenetV2. Results show accuracies of 99.48 % and 99.06 % respectively.