The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to handle. Therefore, the use of low-cost radar sensors is becoming more relevant. In this article, to make the usage of radar simpler and more efficient, we developed with automotive radar technology. We introduce basic radar techniques and present two radar sensors with their specifications. To record the radar data, we developed a system with an integrated camera and sensors. The weight of the whole system is about 315 g for the small radar and 450 g for the large one. The whole system was integrated into a UAV and test flights were performed. After that, several flights were carried out, to verify the system with both radar sensors. Thereby, the records provide an insight into the radar data. We demonstrated that the recording system works and the radar sensors are suitable for the usage in a UAV and future earth science research because of its autonomy, precision, and lightweight.
The major tasks of Battery Management Systems (BMS) are to guaranty safe operation conditions and to maintain every single cell of the hole battery pack. These tasks require measurements and balancing processes for each cell. Modern BMS take advantage of the wealth of information to estimate hardly measurable conditions like State of charge (SoC) or State of Health (SoH). This paper describes a method to estimate the State of Charge for every single cell in a battery pack using an Unscented Kalman Filter (UKF) running on an electronic control unit of the BMS. The verification of the developed algorithms on the control unit takes a long time on a real battery system. For that purpose a real-time Hardware-in-the-Loop (HiL) test bench is developed. In this test bench a LiFePO4 cell model was implemented by Matlab/Simulink®. So the developed and embedded algorithm can be verified by means of various test cases. In this paper the results are presented on signal level. Future work will include a HiL test bench on power level together with an cell emulator. Beside this the test bench offers the opportunity to develop models and sophisticated algorithms for further beneficial state variables like the SoH or the inner temperature of each single cell of the battery pack
The use of an appropriate sensor on an unmanned aerial vehicle (UAV) is vital to assess specific environmental conditions successfully. In addition, technicians and scientists also appreciate a platform to carry the sensors with some advantages such as the low costs or easy pilot management. However, extra requirements like a low-altitude flight are necessary for special applications such as plant density or rice yield. A rotary UAV matches this requirement, but the flight endurance is too short for large areas. Therefore, in this article, a fixed-wing UAV is used, which is more appropriate because of its longer flight endurance. It is necessary to develop an own controller system to use special sensors such as Lidar or Radar on the platform as a multifunctionality system. Thereby, these sensors are used to generate a digital elevation model and also as a collision avoidance sensor at the same time. To achieve this goal, a small UAV was equipped with a hardware platform including a microcontroller and sensors. After testing the system and simulation, the controller was converted into program code to implement it on the microcontroller. After that, several real flights were performed to validate the controller and sensors. We demonstrated that the system is able to work and match the high requirements for future research.
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