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This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The UAV camera data are used to generate 3D point clouds that are fused with the 3D point clouds generated by a rolling 2D laser scanner at the UGV. The registration method is based on the matching of corresponding planar segments that are extracted from the point clouds. Based on the registration, an approach for a globally optimized localization is presented. Apart from the structural information of the point clouds, it is important to mention that no further information is required for the localization. Two examples show the performance of the overall registration.
Global registration of heterogeneous ground and aerial mapping data is a challenging task. This is especially difficult in disaster response scenarios when we have no prior information on the environment and cannot assume the regular order of man-made environments or meaningful semantic cues. In this work we extensively evaluate different approaches to globally register UGV generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud maps from vision sensors. The approaches are realizations of different selections for: a) local features: key-points or segments; b) descriptors: FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR. Additionally, we compare the results against standard approaches like applying ICP after a good prior transformation has been given. The evaluation criteria include the distance which a UGV needs to travel to successfully localize, the registration error, and the computational cost. In this context, we report our findings on effectively performing the task on two new Search and Rescue datasets. Our results have the potential to help the community take informed decisions when registering point-cloud maps from ground robots to those from aerial robots.
A compact and efficient PEM electrolyser stack design based on hydraulic single cell compression
(2019)
For this study gas diffusion electrodes (GDE) with low platinum loading are prepared for the application as anode in polymer electrolyte membrane fuel cell (PEMFC) systems based on hydraulic compression. As catalyst support material, carbon nanofibers (CNF) are investigated because of their high specific surface area and high graphitization degree. The electrode preparation is optimized by an economic and environmental friendly pre-treatment process in oxygen plasma. For GDE manufacture an ink containing oxygen plasma activated CNFs as well as hydrophilic polymer is used. After spray coating of this CNF ink on a graphitic substrate, platinum is deposited using the pulse plating technique. Preliminary results showed a considerable improvement of CNF dispersibility as well as an increased amount and an optimized morphology of the deposited platinum. Morphology and microstructure are observed by scanning electron microscopy as well as transmission electron microscopy. Platinum loading is determined by thermogravimetric analysis to be in the range of 0.01 mg cm-2 to 0.017 mg cm-2. Furthermore, MEAs are prepared from these GDEs and testing is performed in a novel modular fuel cell test stack based on hydraulic compression. Technical information about stack design and functions is given in this work.
The concept of “Internationalisation at Home“ has gained momentum with the increasing digitalization of education and limitations on mobility. Collaborative Online International Learning (COIL) is an innovative, cost-effective instructional method that promotes intercul-tural learning through online collaboration between faculty and students from different countries or locations. The benefits of using COIL courses have been widely recognized, with learners developing intercultural competencies, digital skills, international education experi-ence, and global awareness.
However, multicultural communication in project environments can be complex and demand awareness of cultural variations . The creation and development of effective cross-cultural collectivism, trust, communication, and empathy in leadership is an important ingredient for remote project collaborations success. This is an area that has been least explored in re-search on communication in virtual teams.
The GIPE projects are mainly carried out as so-called Collaborative Online International Learning (COIL) events. However, to gain a “real world“ experience abroad in an intercultural team, students from all partner universities can participate in the Spring School being held for two weeks in Germany and the Germany students present and hand-over the results in the country of the partner university. The main objective of this research was to examine the experiences of students participating in the GIPE project and to evaluate the effectiveness of the project in enhancing intercultural competencies and fostering collaboration among stu-dents from different continents. This paper will also explore the implications of the GIPE project for Education 2.0 considering the COVID-19 pandemic and the future of education delivery and administration transformation.
Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner’s pervasiveness as they interrupt “natural” browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners.
In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for “cookie banner interaction.” We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user’s preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set—even for extensions that aim to implement the same cookie choice. Extensions for “cookie banner interaction” can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies.
Membrane electrode assemblies (MEA) developed at the Westphalian Energy Institute for polymer electrolyte membrane fuel cells (PEMFC) are high tech systems containing various materials structured in nanoscale, at which electrochemical reactions occur on catalyst nano particle surfaces. For low reactance homogeneous compression of the MEA’s layers is necessary. A novel stack architecture for electrochemical cells, especially PEMFC as well as PEM electrolysers, has been developed according to achieve ideal cell operation conditions. Single cells of such a stack are inserted into flexible slots that are surrounded by hydraulic media. While operation the hydraulic media is pressurised which leads to an even compression and cooling of the stack’s cells. With this stack design it has been possible to construct a test facility for simultaneous characterisation of several MEA samples. As compression and temperature conditions of every single sample are equal, with the novel test system the effect of e.g. different electrode configurations can be investigated. Furthermore, the modular stack design leads to the development of hybrid energy applications combining fuel cells, electrolysers, batteries as well as metal hydride tanks in one system.
In this experimental work we present a novel electrolyzer system for the production of hydrogen and oxygen at high pressure levels without an additional mechanical compressor. Due to its control strategies, the operation conditions for this electrolyzer can be kept optimal for each load situation of the system. Furthermore, the novel system design allows for dynamic long-term operation as well as for easy maintainability. Therefore, the device meets the requirements for prospective power-to-gas applications, especially, in order to store excess energy from renewable sources. A laboratory scale device has been developed and high-pressure operation was validated. We also studied the long-term stability of the system by applying dynamic load cycles with load changes every 30 sec. After 80 h of operation the used membrane electrode assembly (MEA) was investigated by means of SEM, EDX and XRD analysis.
A Robust Interface for Head Motion based Control of a Robot Arm using MARG and Visual Sensors
(2018)
Head-controlled human machine interfaces have gained popularity over the past years, especially in the restoration of the autonomy of severely disabled people, like tetraplegics. These interfaces need to be reliable and robust regarding the environmental conditions to guarantee safety of the user and enable a direct interaction between a human and a machine. This paper presents a hybrid MARG and visual sensor system for head orientation estimation which is in this case used to teleoperate a robotic arm. The system contains a Magnetic Angular Rate Gravity (MARG)-sensor and a Tobii eye tracker 4C. A MARG sensor consists of tri-axis accelerometer, gyroscope as well as a magnetometer which enable a complete measurement of orientation relative to the direction of gravity and magnetic field of the earth. The tri-axis magnetometer is sensitive to external magnetic fields which result in incorrect orientation estimation from the sensor fusion process. In this work the Tobii eye tracker 4C is used to increase head orientation estimation because it also features head tracking even though it is commonly used for eye tracking. This type of visual sensor does not suffer magnetic drift. However, it computes orientation data only, if a user is detectable. Within this work a state machine is presented which enables data fusion of the MARG and visual sensor to improve orientation estimation. The fusion of the orientation data of MARG and visual sensors enables a robust interface, which is immune against external magnetic fields. Therefore, it increases the safety of the human machine interaction.
In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.